kv-cache : simplify the interface (#13660)

* kv-cache : simplify the interface

ggml-ci

* context : revert llama_batch_allocr position change

ggml-ci

.clang-tidy

@@ -13,6 +13,7 @@ Checks: >
     -readability-magic-numbers,
     -readability-uppercase-literal-suffix,
     -readability-simplify-boolean-expr,
+    -readability-math-missing-parentheses,
     clang-analyzer-*,
     -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling,
     performance-*,

.devops/cpu.Dockerfile

@@ -14,9 +14,9 @@ WORKDIR /app
 COPY . .
 
 RUN if [ "$TARGETARCH" = "amd64" ]; then \
-        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \
+        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON; \
     elif [ "$TARGETARCH" = "arm64" ]; then \
-        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \
+        cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=OFF -DLLAMA_BUILD_TESTS=OFF -DGGML_CPU_ARM_ARCH=${GGML_CPU_ARM_ARCH}; \
     else \
         echo "Unsupported architecture"; \
         exit 1; \

.devops/cuda.Dockerfile

@@ -21,7 +21,7 @@ COPY . .
 RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
     export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
     fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \

.devops/intel.Dockerfile

@@ -1,4 +1,4 @@
-ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
+ARG ONEAPI_VERSION=2025.1.1-0-devel-ubuntu24.04
 
 ## Build Image
 
@@ -17,7 +17,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
         && export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
     fi && \
     echo "Building with dynamic libs" && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON ${OPT_SYCL_F16} && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${OPT_SYCL_F16} && \
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \

.devops/llama-cli-cann.Dockerfile

@@ -22,7 +22,7 @@ ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH
 
 RUN echo "Building with static libs" && \
     source /usr/local/Ascend/ascend-toolkit/set_env.sh --force && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF  && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF -DLLAMA_BUILD_TESTS=OFF  && \
     cmake --build build --config Release --target llama-cli
 
 # TODO: use image with NNRT

.devops/musa.Dockerfile

@@ -1,10 +1,10 @@
 ARG UBUNTU_VERSION=22.04
 # This needs to generally match the container host's environment.
-ARG MUSA_VERSION=rc3.1.1
+ARG MUSA_VERSION=rc4.0.1
 # Target the MUSA build image
-ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
+ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-mudnn-devel-ubuntu${UBUNTU_VERSION}
 
-ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
+ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-mudnn-runtime-ubuntu${UBUNTU_VERSION}
 
 FROM ${BASE_MUSA_DEV_CONTAINER} AS build
 
@@ -21,21 +21,14 @@ RUN apt-get update && \
     libcurl4-openssl-dev \
     libgomp1
 
-COPY requirements.txt   requirements.txt
-COPY requirements       requirements
-
-RUN pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt
-
 WORKDIR /app
 
 COPY . .
 
-# Use the default MUSA archs if not specified
 RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
         export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
     fi && \
-    cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \

.devops/rocm.Dockerfile

@@ -40,7 +40,7 @@ WORKDIR /app
 COPY . .
 
 RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
-    cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \
+    cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DCMAKE_BUILD_TYPE=Release -DLLAMA_BUILD_TESTS=OFF \
     && cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib \

.devops/vulkan.Dockerfile

@@ -16,7 +16,7 @@ WORKDIR /app
 
 COPY . .
 
-RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON && \
+RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1  -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON && \
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \

.editorconfig

@@ -21,15 +21,15 @@ indent_style = tab
 [prompts/*.txt]
 insert_final_newline = unset
 
-[examples/server/public/*]
+[tools/server/public/*]
 indent_size = 2
 
-[examples/server/public/deps_*]
+[tools/server/public/deps_*]
 trim_trailing_whitespace = unset
 indent_style = unset
 indent_size = unset
 
-[examples/server/deps_*]
+[tools/server/deps_*]
 trim_trailing_whitespace = unset
 indent_style = unset
 indent_size = unset
@@ -37,7 +37,7 @@ indent_size = unset
 [examples/llama.swiftui/llama.swiftui.xcodeproj/*]
 indent_style = tab
 
-[examples/cvector-generator/*.txt]
+[tools/cvector-generator/*.txt]
 trim_trailing_whitespace = unset
 insert_final_newline = unset
 

.flake8

@@ -2,8 +2,9 @@
 max-line-length = 125
 ignore = E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503
 exclude =
-    # Do not traverse examples
+    # Do not traverse examples and tools
     examples,
+    tools,
     # Do not include package initializers
     __init__.py,
     # No need to traverse our git directory

.github/actions/get-tag-name/action.yml

@@ -0,0 +1,22 @@
+name: "Determine tag name"
+description: "Determine the tag name to use for a release"
+outputs:
+  name:
+    description: "The name of the tag"
+    value: ${{ steps.tag.outputs.name }}
+
+runs:
+  using: "composite"
+  steps:
+    - name: Determine tag name
+      id: tag
+      shell: bash
+      run: |
+        BUILD_NUMBER="$(git rev-list --count HEAD)"
+        SHORT_HASH="$(git rev-parse --short=7 HEAD)"
+        if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+          echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
+        else
+          SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
+          echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
+        fi

.github/actions/windows-setup-cuda/action.yml

@@ -0,0 +1,67 @@
+name: "Windows - Setup CUDA Toolkit"
+description: "Setup CUDA Toolkit for Windows"
+inputs:
+  cuda_version:
+    description: "CUDA toolkit version"
+    required: true
+
+runs:
+  using: "composite"
+  steps:
+    - name: Install Cuda Toolkit 11.7
+      if: ${{ inputs.cuda_version == '11.7' }}
+      shell: pwsh
+      run: |
+          mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7"
+          choco install unzip -y
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-11.7.99-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-11.7.99-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-11.7.99-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-11.7.4.6-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-11.7.91-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-11.7.91-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvprof/windows-x86_64/cuda_nvprof-windows-x86_64-11.7.101-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cccl/windows-x86_64/cuda_cccl-windows-x86_64-11.7.91-archive.zip"
+          unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7"
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_cudart-windows-x86_64-11.7.99-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvcc-windows-x86_64-11.7.99-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvrtc-windows-x86_64-11.7.99-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\libcublas-windows-x86_64-11.7.4.6-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvtx-windows-x86_64-11.7.91-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\visual_studio_integration-windows-x86_64-11.7.91-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_nvprof-windows-x86_64-11.7.101-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\cuda_cccl-windows-x86_64-11.7.91-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" /E /I /H /Y
+          echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\libnvvp" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
+          echo "CUDA_PATH_V11_7=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
+
+    - name: Install Cuda Toolkit 12.4
+      if: ${{ inputs.cuda_version == '12.4' }}
+      shell: pwsh
+      run: |
+          mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4"
+          choco install unzip -y
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-12.4.127-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-12.4.131-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-12.4.127-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-12.4.5.8-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-12.4.127-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_profiler_api/windows-x86_64/cuda_profiler_api-windows-x86_64-12.4.127-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-12.4.127-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvprof/windows-x86_64/cuda_nvprof-windows-x86_64-12.4.127-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cccl/windows-x86_64/cuda_cccl-windows-x86_64-12.4.127-archive.zip"
+          unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4"
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_cudart-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvcc-windows-x86_64-12.4.131-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvrtc-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\libcublas-windows-x86_64-12.4.5.8-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvtx-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_profiler_api-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\visual_studio_integration-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_nvprof-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\cuda_cccl-windows-x86_64-12.4.127-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" /E /I /H /Y
+          echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\libnvvp" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
+          echo "CUDA_PATH_V12_4=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8

.github/actions/windows-setup-curl/action.yml

@@ -5,6 +5,10 @@ inputs:
     description: 'CURL version'
     required: false
     default: '8.6.0_6'
+  architecture:
+    description: 'Architecture of the libcurl to download'
+    required: false
+    default: 'win64'
 outputs:
   curl_path:
     description: "Path to the downloaded libcurl"
@@ -18,8 +22,9 @@ runs:
       shell: powershell
       env:
         CURL_VERSION: ${{ inputs.curl_version }}
+        ARCHITECTURE: ${{ inputs.architecture }}
       run: |
-        curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-mingw.zip"
+        curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-${env:ARCHITECTURE}-mingw.zip"
         mkdir $env:RUNNER_TEMP/libcurl
         tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl
         echo "curl_path=$env:RUNNER_TEMP/libcurl" >> $env:GITHUB_OUTPUT

.github/labeler.yml

@@ -45,7 +45,9 @@ build:
             - CMakePresets.json
 examples:
     - changed-files:
-        - any-glob-to-any-file: examples/**
+        - any-glob-to-any-file:
+            - examples/**
+            - tools/**
 devops:
     - changed-files:
         - any-glob-to-any-file:
@@ -70,7 +72,7 @@ android:
 server:
     - changed-files:
         - any-glob-to-any-file:
-            - examples/server/**
+            - tools/server/**
 ggml:
     - changed-files:
         - any-glob-to-any-file:

.github/workflows/bench.yml.disabled

@@ -27,10 +27,10 @@ on:
   push:
     branches:
       - master
-    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
+    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'tools/server/*.h*', 'tools/server/*.cpp']
   pull_request_target:
     types: [opened, synchronize, reopened]
-    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
+    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'tools/server/*.h*', 'tools/server/*.cpp']
   schedule:
     -  cron: '04 2 * * *'
 
@@ -69,7 +69,7 @@ jobs:
       - name: Install python env
         id: pipenv
         run: |
-          cd examples/server/bench
+          cd tools/server/bench
           python3 -m venv venv
           source venv/bin/activate
           pip install -r requirements.txt
@@ -79,7 +79,7 @@ jobs:
         run: |
           wget --quiet https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz
           tar xzf prometheus*.tar.gz --strip-components=1
-          ./prometheus --config.file=examples/server/bench/prometheus.yml &
+          ./prometheus --config.file=tools/server/bench/prometheus.yml &
           while ! nc -z localhost 9090; do
             sleep 0.1
           done
@@ -92,7 +92,7 @@ jobs:
       - name: Install k6 and xk6-sse
         id: k6_installation
         run: |
-          cd examples/server/bench
+          cd tools/server/bench
           go install go.k6.io/xk6/cmd/xk6@latest
           xk6 build master \
               --with github.com/phymbert/xk6-sse
@@ -116,7 +116,7 @@ jobs:
       - name: Download the dataset
         id: download_dataset
         run: |
-          cd examples/server/bench
+          cd tools/server/bench
           wget --quiet https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
 
       - name: Server bench
@@ -126,7 +126,7 @@ jobs:
         run: |
           set -eux
 
-          cd examples/server/bench
+          cd tools/server/bench
           source venv/bin/activate
           python bench.py \
               --runner-label ${{ env.RUNNER_LABEL }} \
@@ -157,9 +157,9 @@ jobs:
           name: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
           compression-level: 9
           path: |
-            examples/server/bench/*.jpg
-            examples/server/bench/*.json
-            examples/server/bench/*.log
+            tools/server/bench/*.jpg
+            tools/server/bench/*.json
+            tools/server/bench/*.log
 
       - name: Commit status
         uses: Sibz/github-status-action@v1
@@ -178,17 +178,17 @@ jobs:
         with:
           client_id: ${{secrets.IMGUR_CLIENT_ID}}
           path: |
-            examples/server/bench/prompt_tokens_seconds.jpg
-            examples/server/bench/predicted_tokens_seconds.jpg
-            examples/server/bench/kv_cache_usage_ratio.jpg
-            examples/server/bench/requests_processing.jpg
+            tools/server/bench/prompt_tokens_seconds.jpg
+            tools/server/bench/predicted_tokens_seconds.jpg
+            tools/server/bench/kv_cache_usage_ratio.jpg
+            tools/server/bench/requests_processing.jpg
 
       - name: Extract mermaid
         id: set_mermaid
         run: |
           set -eux
 
-          cd examples/server/bench
+          cd tools/server/bench
           PROMPT_TOKENS_SECONDS=$(cat prompt_tokens_seconds.mermaid)
           echo "PROMPT_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
           echo "$PROMPT_TOKENS_SECONDS" >> $GITHUB_ENV

.github/workflows/build-linux-cross.yml

@@ -4,18 +4,25 @@ on:
   workflow_call:
 
 jobs:
-  ubuntu-latest-riscv64-cpu-cross:
-    runs-on: ubuntu-latest
+  ubuntu-24-riscv64-cpu-cross:
+    runs-on: ubuntu-24.04
 
     steps:
       - uses: actions/checkout@v4
       - name: Setup Riscv
         run: |
           sudo dpkg --add-architecture riscv64
-          sudo sed -i 's|http://azure.archive.ubuntu.com/ubuntu|http://ports.ubuntu.com/ubuntu-ports|g' \
-                 /etc/apt/sources.list /etc/apt/apt-mirrors.txt
-          sudo apt-get clean
-          sudo apt-get update
+
+          # Add arch-specific repositories for non-amd64 architectures
+          cat << EOF | sudo tee /etc/apt/sources.list.d/riscv64-ports.list
+          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
+          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
+          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
+          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
+          EOF
+
+          sudo apt-get update || true    ;# Prevent failure due to missing URLs.
+
           sudo apt-get install -y --no-install-recommends \
                   build-essential \
                   gcc-14-riscv64-linux-gnu \
@@ -27,6 +34,7 @@ jobs:
           cmake -B build -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \
+                         -DLLAMA_BUILD_TOOLS=ON \
                          -DLLAMA_BUILD_TESTS=OFF \
                          -DCMAKE_SYSTEM_NAME=Linux \
                          -DCMAKE_SYSTEM_PROCESSOR=riscv64 \
@@ -40,21 +48,25 @@ jobs:
 
           cmake --build build --config Release -j $(nproc)
 
-  ubuntu-latest-riscv64-vulkan-cross:
-    runs-on: ubuntu-latest
+  ubuntu-24-riscv64-vulkan-cross:
+    runs-on: ubuntu-24.04
 
     steps:
       - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
       - name: Setup Riscv
         run: |
           sudo dpkg --add-architecture riscv64
-          sudo sed -i 's|http://azure.archive.ubuntu.com/ubuntu|http://ports.ubuntu.com/ubuntu-ports|g' \
-                 /etc/apt/sources.list /etc/apt/apt-mirrors.txt
-          sudo apt-get clean
-          sudo apt-get update
+
+          # Add arch-specific repositories for non-amd64 architectures
+          cat << EOF | sudo tee /etc/apt/sources.list.d/riscv64-ports.list
+          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
+          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
+          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
+          deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
+          EOF
+
+          sudo apt-get update || true    ;# Prevent failure due to missing URLs.
+
           sudo apt-get install -y --no-install-recommends \
                   build-essential \
                   glslc \
@@ -69,6 +81,7 @@ jobs:
                          -DGGML_VULKAN=ON \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \
+                         -DLLAMA_BUILD_TOOLS=ON \
                          -DLLAMA_BUILD_TESTS=OFF \
                          -DCMAKE_SYSTEM_NAME=Linux \
                          -DCMAKE_SYSTEM_PROCESSOR=riscv64 \
@@ -82,21 +95,25 @@ jobs:
 
           cmake --build build --config Release -j $(nproc)
 
-  ubuntu-latest-arm64-vulkan-cross:
-    runs-on: ubuntu-latest
+  ubuntu-24-arm64-vulkan-cross:
+    runs-on: ubuntu-24.04
 
     steps:
       - uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
       - name: Setup Arm64
         run: |
           sudo dpkg --add-architecture arm64
-          sudo sed -i 's|http://azure.archive.ubuntu.com/ubuntu|http://ports.ubuntu.com/ubuntu-ports|g' \
-                 /etc/apt/sources.list /etc/apt/apt-mirrors.txt
-          sudo apt-get clean
-          sudo apt-get update
+
+          # Add arch-specific repositories for non-amd64 architectures
+          cat << EOF | sudo tee /etc/apt/sources.list.d/arm64-ports.list
+          deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
+          deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
+          deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
+          deb [arch=arm64] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
+          EOF
+
+          sudo apt-get update || true    ;# Prevent failure due to missing URLs.
+
           sudo apt-get install -y --no-install-recommends \
                   build-essential \
                   glslc \
@@ -110,6 +127,7 @@ jobs:
                          -DGGML_VULKAN=ON \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \
+                         -DLLAMA_BUILD_TOOLS=ON \
                          -DLLAMA_BUILD_TESTS=OFF \
                          -DCMAKE_SYSTEM_NAME=Linux \
                          -DCMAKE_SYSTEM_PROCESSOR=aarch64 \
@@ -122,3 +140,94 @@ jobs:
                          -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
 
           cmake --build build --config Release -j $(nproc)
+
+  ubuntu-24-ppc64el-cpu-cross:
+    runs-on: ubuntu-24.04
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup PowerPC64le
+        run: |
+          sudo dpkg --add-architecture ppc64el
+
+          # Add arch-specific repositories for non-amd64 architectures
+          cat << EOF | sudo tee /etc/apt/sources.list.d/ppc64el-ports.list
+          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
+          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
+          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
+          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
+          EOF
+
+          sudo apt-get update || true    ;# Prevent failure due to missing URLs.
+
+          sudo apt-get install -y --no-install-recommends \
+                  build-essential \
+                  gcc-14-powerpc64le-linux-gnu \
+                  g++-14-powerpc64le-linux-gnu \
+                  libcurl4-openssl-dev:ppc64el
+
+      - name: Build
+        run: |
+          cmake -B build -DCMAKE_BUILD_TYPE=Release \
+                         -DGGML_OPENMP=OFF \
+                         -DLLAMA_BUILD_EXAMPLES=ON \
+                         -DLLAMA_BUILD_TOOLS=ON \
+                         -DLLAMA_BUILD_TESTS=OFF \
+                         -DCMAKE_SYSTEM_NAME=Linux \
+                         -DCMAKE_SYSTEM_PROCESSOR=ppc64 \
+                         -DCMAKE_C_COMPILER=powerpc64le-linux-gnu-gcc-14 \
+                         -DCMAKE_CXX_COMPILER=powerpc64le-linux-gnu-g++-14 \
+                         -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+                         -DCMAKE_FIND_ROOT_PATH=/usr/lib/powerpc64le-linux-gnu \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
+
+          cmake --build build --config Release -j $(nproc)
+
+  ubuntu-24-ppc64el-vulkan-cross:
+    runs-on: ubuntu-24.04
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Setup PowerPC64le
+        run: |
+          sudo dpkg --add-architecture ppc64el
+
+          # Add arch-specific repositories for non-amd64 architectures
+          cat << EOF | sudo tee /etc/apt/sources.list.d/ppc64el-ports.list
+          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble main universe
+          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-updates main universe
+          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-security main universe
+          deb [arch=ppc64el] http://ports.ubuntu.com/ubuntu-ports/ noble-backports main universe
+          EOF
+
+          sudo apt-get update || true    ;# Prevent failure due to missing URLs.
+
+          sudo apt-get install -y --no-install-recommends \
+                  build-essential \
+                  glslc \
+                  gcc-14-powerpc64le-linux-gnu \
+                  g++-14-powerpc64le-linux-gnu \
+                  libvulkan-dev:ppc64el \
+                  libcurl4-openssl-dev:ppc64el
+
+      - name: Build
+        run: |
+          cmake -B build -DCMAKE_BUILD_TYPE=Release \
+                         -DGGML_VULKAN=ON \
+                         -DGGML_OPENMP=OFF \
+                         -DLLAMA_BUILD_EXAMPLES=ON \
+                         -DLLAMA_BUILD_TOOLS=ON \
+                         -DLLAMA_BUILD_TESTS=OFF \
+                         -DCMAKE_SYSTEM_NAME=Linux \
+                         -DCMAKE_SYSTEM_PROCESSOR=ppc64 \
+                         -DCMAKE_C_COMPILER=powerpc64le-linux-gnu-gcc-14 \
+                         -DCMAKE_CXX_COMPILER=powerpc64le-linux-gnu-g++-14 \
+                         -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
+                         -DCMAKE_FIND_ROOT_PATH=/usr/lib/powerpc64le-linux-gnu \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_PROGRAM=NEVER \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_LIBRARY=ONLY \
+                         -DCMAKE_FIND_ROOT_PATH_MODE_INCLUDE=BOTH
+
+          cmake --build build --config Release -j $(nproc)

.github/workflows/docker.yml

@@ -36,10 +36,13 @@ jobs:
       matrix:
         config:
           # Multi-stage build
-          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: false }
+          # Note: the arm64 images are failing, which prevents the amd64 images from being built
+          # https://github.com/ggml-org/llama.cpp/issues/11888
+          #- { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: false }
+          - { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false }
           - { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false }
           - { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true }
-          - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false }
+          - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true }
           - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false }
           # Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete
           #- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: true }

.github/workflows/release.yml

@@ -0,0 +1,716 @@
+name: Create Release
+
+on:
+  workflow_dispatch: # allows manual triggering
+    inputs:
+      create_release:
+        description: 'Create new release'
+        required: true
+        type: boolean
+  push:
+    branches:
+      - master
+    paths: ['.github/workflows/release.yml', '**/CMakeLists.txt', '**/.cmake', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal', '**/*.comp']
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  cancel-in-progress: true
+
+env:
+  BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
+  CMAKE_ARGS: "-DLLAMA_BUILD_EXAMPLES=OFF -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_TOOLS=ON -DLLAMA_BUILD_SERVER=ON -DGGML_RPC=ON"
+
+jobs:
+  macOS-arm64:
+    runs-on: macos-14
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: macOS-latest-cmake-arm64
+          evict-old-files: 1d
+
+      - name: Dependencies
+        id: depends
+        continue-on-error: true
+        run: |
+          brew update
+          brew install curl
+
+      - name: Build
+        id: cmake_build
+        run: |
+          sysctl -a
+          cmake -B build \
+            -DCMAKE_BUILD_RPATH="@loader_path" \
+            -DLLAMA_FATAL_WARNINGS=ON \
+            -DGGML_METAL_USE_BF16=ON \
+            -DGGML_METAL_EMBED_LIBRARY=ON \
+            -DGGML_RPC=ON \
+            ${{ env.CMAKE_ARGS }}
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        run: |
+          cp LICENSE ./build/bin/
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/*
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip
+          name: llama-bin-macos-arm64.zip
+
+  macOS-x64:
+    runs-on: macos-13
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: macOS-latest-cmake-x64
+          evict-old-files: 1d
+
+      - name: Dependencies
+        id: depends
+        continue-on-error: true
+        run: |
+          brew update
+          brew install curl
+
+      - name: Build
+        id: cmake_build
+        run: |
+          sysctl -a
+          # Metal is disabled due to intermittent failures with Github runners not having a GPU:
+          # https://github.com/ggml-org/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313
+          cmake -B build \
+            -DCMAKE_BUILD_RPATH="@loader_path" \
+            -DLLAMA_FATAL_WARNINGS=ON \
+            -DGGML_METAL=OFF \
+            -DGGML_RPC=ON
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        run: |
+          cp LICENSE ./build/bin/
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/*
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip
+          name: llama-bin-macos-x64.zip
+
+  ubuntu-22-cpu:
+    strategy:
+      matrix:
+        include:
+          - build: 'x64'
+            os: ubuntu-22.04
+          - build: 'arm64'
+            os: ubuntu-22.04-arm
+
+    runs-on: ${{ matrix.os }}
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: ubuntu-cpu-cmake
+          evict-old-files: 1d
+
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential libcurl4-openssl-dev
+
+      - name: Build
+        id: cmake_build
+        run: |
+          cmake -B build \
+            -DLLAMA_FATAL_WARNINGS=ON \
+            ${{ env.CMAKE_ARGS }}
+          cmake --build build --config Release -j $(nproc)
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        run: |
+          cp LICENSE ./build/bin/
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip ./build/bin/*
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip
+          name: llama-bin-ubuntu-${{ matrix.build }}.zip
+
+  ubuntu-22-vulkan:
+    runs-on: ubuntu-22.04
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: ubuntu-22-cmake-vulkan
+          evict-old-files: 1d
+
+      - name: Dependencies
+        id: depends
+        run: |
+          wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | sudo apt-key add -
+          sudo wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
+          sudo apt-get update -y
+          sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk libcurl4-openssl-dev
+
+      - name: Build
+        id: cmake_build
+        run: |
+          cmake -B build \
+            -DGGML_VULKAN=ON \
+            ${{ env.CMAKE_ARGS }}
+          cmake --build build --config Release -j $(nproc)
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        run: |
+          cp LICENSE ./build/bin/
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip ./build/bin/*
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip
+          name: llama-bin-ubuntu-vulkan-x64.zip
+
+  windows:
+    runs-on: windows-latest
+
+    env:
+      OPENBLAS_VERSION: 0.3.23
+      VULKAN_VERSION: 1.4.309.0
+
+    strategy:
+      matrix:
+        include:
+          - build: 'cpu-x64'
+            arch: 'x64'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF'
+          #- build: 'openblas-x64'
+          #  arch: 'x64'
+          #  defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/x64-windows-llvm.cmake -DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_OPENMP=OFF -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
+          - build: 'vulkan-x64'
+            arch: 'x64'
+            defines: '-DGGML_NATIVE=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DGGML_VULKAN=ON'
+          - build: 'cpu-arm64'
+            arch: 'arm64'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF'
+          - build: 'opencl-adreno-arm64'
+            arch: 'arm64'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON'
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: windows-latest-cmake-${{ matrix.build }}
+          variant: ccache
+          evict-old-files: 1d
+
+      - name: Download OpenBLAS
+        id: get_openblas
+        if: ${{ matrix.build == 'openblas-x64' }}
+        run: |
+          curl.exe -o $env:RUNNER_TEMP/openblas.zip -L "https://github.com/xianyi/OpenBLAS/releases/download/v${env:OPENBLAS_VERSION}/OpenBLAS-${env:OPENBLAS_VERSION}-x64.zip"
+          curl.exe -o $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt -L "https://github.com/xianyi/OpenBLAS/raw/v${env:OPENBLAS_VERSION}/LICENSE"
+          mkdir $env:RUNNER_TEMP/openblas
+          tar.exe -xvf $env:RUNNER_TEMP/openblas.zip -C $env:RUNNER_TEMP/openblas
+          $vcdir = $(vswhere -latest -products * -requires Microsoft.VisualStudio.Component.VC.Tools.x86.x64 -property installationPath)
+          $msvc = $(join-path $vcdir $('VC\Tools\MSVC\'+$(gc -raw $(join-path $vcdir 'VC\Auxiliary\Build\Microsoft.VCToolsVersion.default.txt')).Trim()))
+          $lib =  $(join-path $msvc 'bin\Hostx64\x64\lib.exe')
+          & $lib /machine:x64 "/def:${env:RUNNER_TEMP}/openblas/lib/libopenblas.def" "/out:${env:RUNNER_TEMP}/openblas/lib/openblas.lib" /name:openblas.dll
+
+      - name: Install Vulkan SDK
+        id: get_vulkan
+        if: ${{ matrix.build == 'vulkan-x64' }}
+        run: |
+          curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/VulkanSDK-${env:VULKAN_VERSION}-Installer.exe"
+          & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install
+          Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}"
+          Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin"
+
+      - name: Install Ninja
+        id: install_ninja
+        run: |
+          choco install ninja
+
+      - name: Install OpenCL Headers and Libs
+        id: install_opencl
+        if: ${{ matrix.build == 'opencl-adreno-arm64' }}
+        run: |
+          git clone https://github.com/KhronosGroup/OpenCL-Headers
+          cd OpenCL-Headers
+          cmake -B build `
+            -DBUILD_TESTING=OFF `
+            -DOPENCL_HEADERS_BUILD_TESTING=OFF `
+            -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF `
+            -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
+          cmake --build build --target install
+          git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader
+          cd OpenCL-ICD-Loader
+          cmake -B build-arm64-release `
+            -A arm64 `
+            -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" `
+            -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
+          cmake --build build-arm64-release --target install --config release
+
+      - name: libCURL
+        id: get_libcurl
+        uses: ./.github/actions/windows-setup-curl
+        with:
+          architecture: ${{ matrix.arch == 'x64' && 'win64' || 'win64a' }}
+
+      - name: Build
+        id: cmake_build
+        env:
+          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
+        run: |
+          cmake -S . -B build ${{ matrix.defines }} `
+            -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" `
+            ${{ env.CMAKE_ARGS }}
+          cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS}
+
+      - name: Add libopenblas.dll
+        id: add_libopenblas_dll
+        if: ${{ matrix.build == 'openblas-x64' }}
+        run: |
+          cp $env:RUNNER_TEMP/openblas/bin/libopenblas.dll ./build/bin/Release/openblas.dll
+          cp $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt ./build/bin/Release/OpenBLAS-${env:OPENBLAS_VERSION}.txt
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        env:
+          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
+        run: |
+          Copy-Item $env:CURL_PATH\bin\libcurl-${{ matrix.arch }}.dll .\build\bin\Release\
+          7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip .\build\bin\Release\*
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip
+          name: llama-bin-win-${{ matrix.build }}.zip
+
+  windows-cuda:
+    runs-on: windows-2019
+
+    strategy:
+      matrix:
+        cuda: ['12.4', '11.7']
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+            fetch-depth: 0
+
+      - name: Install ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: windows-cuda-${{ matrix.cuda }}
+          variant: ccache
+          evict-old-files: 1d
+
+      - name: Install Cuda Toolkit
+        uses: ./.github/actions/windows-setup-cuda
+        with:
+          cuda_version: ${{ matrix.cuda }}
+
+      - name: Install Ninja
+        id: install_ninja
+        run: |
+          choco install ninja
+
+      - name: libCURL
+        id: get_libcurl
+        uses: ./.github/actions/windows-setup-curl
+
+      - name: Build
+        id: cmake_build
+        shell: cmd
+        env:
+          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
+        run: |
+          call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat"
+          cmake -S . -B build -G "Ninja Multi-Config" ^
+            -DGGML_NATIVE=OFF ^
+            -DGGML_BACKEND_DL=ON ^
+            -DGGML_CPU_ALL_VARIANTS=ON ^
+            -DGGML_CUDA=ON ^
+            -DCURL_LIBRARY="%CURL_PATH%/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="%CURL_PATH%/include" ^
+            ${{ env.CMAKE_ARGS }}
+          set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
+          cmake --build build --config Release -j %NINJA_JOBS% -t ggml
+          cmake --build build --config Release
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        env:
+          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
+        run: |
+          cp $env:CURL_PATH\bin\libcurl-x64.dll .\build\bin\Release\libcurl-x64.dll
+          7z a llama-${{ steps.tag.outputs.name }}-bin-win-cuda${{ matrix.cuda }}-x64.zip .\build\bin\Release\*
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-win-cuda${{ matrix.cuda }}-x64.zip
+          name: llama-bin-win-cuda${{ matrix.cuda }}-x64.zip
+
+      - name: Copy and pack Cuda runtime
+        run: |
+          echo "Cuda install location: ${{ env.CUDA_PATH }}"
+          $dst='.\build\bin\cudart\'
+          robocopy "${{env.CUDA_PATH}}\bin" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
+          robocopy "${{env.CUDA_PATH}}\lib" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
+          7z a cudart-llama-bin-win-cuda${{ matrix.cuda }}-x64.zip $dst\*
+
+      - name: Upload Cuda runtime
+        uses: actions/upload-artifact@v4
+        with:
+          path: cudart-llama-bin-win-cuda${{ matrix.cuda }}-x64.zip
+          name: cudart-llama-bin-win-cuda${{ matrix.cuda }}-x64.zip
+
+  windows-sycl:
+    runs-on: windows-latest
+
+    defaults:
+      run:
+        shell: bash
+
+    env:
+      WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/7cd9bba0-7aab-4e30-b3ae-2221006a4a05/intel-oneapi-base-toolkit-2025.1.1.34_offline.exe
+      WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel:intel.oneapi.win.dnnl:intel.oneapi.win.tbb.devel
+      ONEAPI_ROOT: "C:/Program Files (x86)/Intel/oneAPI"
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: windows-latest-cmake-sycl
+          variant: ccache
+          evict-old-files: 1d
+
+      - name: Install
+        run:  |
+          scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL
+
+      # TODO: add libcurl support ; we will also need to modify win-build-sycl.bat to accept user-specified args
+
+      - name: Build
+        id: cmake_build
+        run:  examples/sycl/win-build-sycl.bat
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Build the release package
+        id: pack_artifacts
+        run: |
+          echo "cp oneAPI running time dll files in ${{ env.ONEAPI_ROOT }} to ./build/bin"
+
+          cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_sycl_blas.5.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_core.2.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/mkl/latest/bin/mkl_tbb_thread.2.dll" ./build/bin
+
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_level_zero.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_adapter_opencl.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_loader.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/ur_win_proxy_loader.dll" ./build/bin
+
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl8.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/svml_dispmd.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libmmd.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libiomp5md.dll" ./build/bin
+
+          cp "${{ env.ONEAPI_ROOT }}/dnnl/latest/bin/dnnl.dll" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/tbb/latest/bin/tbb12.dll" ./build/bin
+
+          echo "cp oneAPI running time dll files to ./build/bin done"
+          7z a llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip ./build/bin/*
+
+      - name: Upload the release package
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip
+          name: llama-bin-win-sycl-x64.zip
+
+  windows-hip:
+    runs-on: windows-latest
+
+    strategy:
+      matrix:
+        gpu_target: [gfx1100, gfx1101, gfx1030]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+            fetch-depth: 0
+
+      - name: Clone rocWMMA repository
+        id: clone_rocwmma
+        run: |
+          git clone https://github.com/rocm/rocwmma --branch rocm-6.2.4 --depth 1
+
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@v1.2.16
+        with:
+          key: windows-latest-cmake-hip-release
+          evict-old-files: 1d
+
+      - name: Install
+        id: depends
+        run: |
+          $ErrorActionPreference = "Stop"
+          write-host "Downloading AMD HIP SDK Installer"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          write-host "Installing AMD HIP SDK"
+          Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait
+          write-host "Completed AMD HIP SDK installation"
+
+      - name: Verify ROCm
+        id: verify
+        run: |
+          & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version
+
+      - name: libCURL
+        id: get_libcurl
+        uses: ./.github/actions/windows-setup-curl
+
+      - name: Build
+        id: cmake_build
+        env:
+          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
+        run: |
+          $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path)
+          $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}"
+          cmake -G "Unix Makefiles" -B build -S . `
+            -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" `
+            -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" `
+            -DCMAKE_CXX_FLAGS="-I$($PWD.Path.Replace('\', '/'))/rocwmma/library/include/" `
+            -DCMAKE_BUILD_TYPE=Release `
+            -DAMDGPU_TARGETS=${{ matrix.gpu_target }} `
+            -DGGML_HIP_ROCWMMA_FATTN=ON `
+            -DGGML_HIP=ON `
+            -DCURL_LIBRARY="$env:CURL_PATH/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:CURL_PATH/include" `
+            ${{ env.CMAKE_ARGS }}
+          cmake --build build -j ${env:NUMBER_OF_PROCESSORS}
+          md "build\bin\rocblas\library\"
+          cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\"
+          cp "${env:HIP_PATH}\bin\rocblas.dll" "build\bin\"
+          cp "${env:HIP_PATH}\bin\rocblas\library\*" "build\bin\rocblas\library\"
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        env:
+          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
+        run: |
+          cp $env:CURL_PATH\bin\libcurl-x64.dll .\build\bin\libcurl-x64.dll
+          7z a llama-${{ steps.tag.outputs.name }}-bin-win-hip-x64-${{ matrix.gpu_target }}.zip .\build\bin\*
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-win-hip-x64-${{ matrix.gpu_target }}.zip
+          name: llama-bin-win-hip-x64-${{ matrix.gpu_target }}.zip
+
+  ios-xcode-build:
+    runs-on: macos-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Build
+        id: cmake_build
+        run: |
+          sysctl -a
+          cmake -B build -G Xcode \
+            -DGGML_METAL_USE_BF16=ON \
+            -DGGML_METAL_EMBED_LIBRARY=ON \
+            -DLLAMA_CURL=OFF \
+            -DLLAMA_BUILD_EXAMPLES=OFF \
+            -DLLAMA_BUILD_TOOLS=OFF \
+            -DLLAMA_BUILD_TESTS=OFF \
+            -DLLAMA_BUILD_SERVER=OFF \
+            -DCMAKE_SYSTEM_NAME=iOS \
+            -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
+            -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
+          cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
+
+      - name: xcodebuild for swift package
+        id: xcodebuild
+        run: |
+          ./build-xcframework.sh
+
+      - name: Build Xcode project
+        run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' FRAMEWORK_FOLDER_PATH=./build-ios build
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        run: |
+          zip --symlinks -r llama-${{ steps.tag.outputs.name }}-xcframework.zip build-apple/llama.xcframework
+
+      - name: Upload artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-xcframework.zip
+          name: llama-${{ steps.tag.outputs.name }}-xcframework
+
+  release:
+    if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+
+    # Fine-grant permission
+    # https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token
+    permissions:
+        contents: write # for creating release
+
+    runs-on: ubuntu-latest
+
+    needs:
+      - ubuntu-22-cpu
+      - ubuntu-22-vulkan
+      - windows
+      - windows-cuda
+      - windows-sycl
+      - windows-hip
+      - macOS-arm64
+      - macOS-x64
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Download artifacts
+        id: download-artifact
+        uses: actions/download-artifact@v4
+        with:
+          path: ./artifact
+
+      - name: Move artifacts
+        id: move_artifacts
+        run: mkdir -p ./artifact/release && mv ./artifact/*/*.zip ./artifact/release
+
+      - name: Create release
+        id: create_release
+        uses: ggml-org/action-create-release@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          tag_name: ${{ steps.tag.outputs.name }}
+
+      - name: Upload release
+        id: upload_release
+        uses: actions/github-script@v3
+        with:
+          github-token: ${{secrets.GITHUB_TOKEN}}
+          script: |
+            const path = require('path');
+            const fs = require('fs');
+            const release_id = '${{ steps.create_release.outputs.id }}';
+            for (let file of await fs.readdirSync('./artifact/release')) {
+              if (path.extname(file) === '.zip') {
+                console.log('uploadReleaseAsset', file);
+                await github.repos.uploadReleaseAsset({
+                  owner: context.repo.owner,
+                  repo: context.repo.repo,
+                  release_id: release_id,
+                  name: file,
+                  data: await fs.readFileSync(`./artifact/release/${file}`)
+                });
+              }
+            }

.github/workflows/server.yml

@@ -15,10 +15,10 @@ on:
   push:
     branches:
       - master
-    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
+    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
+    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*']
 
 env:
   LLAMA_LOG_COLORS: 1
@@ -74,7 +74,7 @@ jobs:
       - name: Tests dependencies
         id: test_dependencies
         run: |
-          pip install -r examples/server/tests/requirements.txt
+          pip install -r tools/server/tests/requirements.txt
 
       # Setup nodejs (to be used for verifying bundled index.html)
       - uses: actions/setup-node@v4
@@ -84,14 +84,14 @@ jobs:
       - name: WebUI - Install dependencies
         id: webui_lint
         run: |
-          cd examples/server/webui
+          cd tools/server/webui
           npm ci
 
       - name: WebUI - Check code format
         id: webui_format
         run: |
           git config --global --add safe.directory $(realpath .)
-          cd examples/server/webui
+          cd tools/server/webui
           git status
 
           npm run format
@@ -108,7 +108,7 @@ jobs:
         id: verify_server_index_html
         run: |
           git config --global --add safe.directory $(realpath .)
-          cd examples/server/webui
+          cd tools/server/webui
           git status
 
           npm run build
@@ -161,21 +161,21 @@ jobs:
         env:
           GITHUB_ACTIONS: "true"
         run: |
-          cd examples/server/tests
+          cd tools/server/tests
           ./tests.sh
 
       - name: Tests (sanitizers)
         id: server_integration_tests_sanitizers
         if: ${{ matrix.sanitizer != '' }}
         run: |
-          cd examples/server/tests
+          cd tools/server/tests
           LLAMA_SANITIZE=1 ./tests.sh
 
       - name: Slow tests
         id: server_integration_tests_slow
         if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
         run: |
-          cd examples/server/tests
+          cd tools/server/tests
           SLOW_TESTS=1 ./tests.sh
 
 
@@ -211,7 +211,7 @@ jobs:
       - name: Tests dependencies
         id: test_dependencies
         run: |
-          pip install -r examples/server/tests/requirements.txt
+          pip install -r tools/server/tests/requirements.txt
 
       - name: Copy Libcurl
         id: prepare_libcurl
@@ -224,7 +224,7 @@ jobs:
         id: server_integration_tests
         if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
         run: |
-          cd examples/server/tests
+          cd tools/server/tests
           $env:PYTHONIOENCODING = ":replace"
           pytest -v -x -m "not slow"
 
@@ -232,6 +232,6 @@ jobs:
         id: server_integration_tests_slow
         if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
         run: |
-          cd examples/server/tests
+          cd tools/server/tests
           $env:SLOW_TESTS = "1"
           pytest -v -x

.gitignore

@@ -96,11 +96,11 @@ perf-*.txt
 # Examples
 
 examples/jeopardy/results.txt
-examples/server/*.css.hpp
-examples/server/*.html.hpp
-examples/server/*.js.hpp
-examples/server/*.mjs.hpp
-examples/server/*.gz.hpp
+tools/server/*.css.hpp
+tools/server/*.html.hpp
+tools/server/*.js.hpp
+tools/server/*.mjs.hpp
+tools/server/*.gz.hpp
 !build_64.sh
 !examples/*.bat
 !examples/*/*.kts
@@ -110,7 +110,7 @@ examples/server/*.gz.hpp
 
 # Server Web UI temporary files
 node_modules
-examples/server/webui/dist
+tools/server/webui/dist
 
 # Python
 

CMakeLists.txt

@@ -77,6 +77,7 @@ option(LLAMA_BUILD_COMMON "llama: build common utils library" ${LLAMA_STANDALONE
 
 # extra artifacts
 option(LLAMA_BUILD_TESTS    "llama: build tests"          ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_TOOLS    "llama: build tools"          ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES "llama: build examples"       ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
 
@@ -187,6 +188,10 @@ if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES)
     add_subdirectory(pocs)
 endif()
 
+if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TOOLS)
+    add_subdirectory(tools)
+endif()
+
 #
 # install
 #
@@ -247,20 +252,3 @@ configure_file(cmake/llama.pc.in
 
 install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
         DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig)
-
-#
-# copy the license files
-#
-
-# Check if running in GitHub Actions
-if(DEFINED ENV{GITHUB_ACTIONS} AND "$ENV{GITHUB_ACTIONS}" STREQUAL "true")
-    message(STATUS "Running inside GitHub Actions - copying license files")
-
-    # Copy all files from licenses/ to build/bin/
-    file(GLOB LICENSE_FILES "${CMAKE_SOURCE_DIR}/licenses/*")
-    foreach(LICENSE_FILE ${LICENSE_FILES})
-        get_filename_component(FILENAME ${LICENSE_FILE} NAME)
-        configure_file(${LICENSE_FILE} "${CMAKE_BINARY_DIR}/bin/${FILENAME}" COPYONLY)
-    endforeach()
-endif()
-

CMakePresets.json

@@ -38,15 +38,6 @@
         }
     },
 
-    {
-        "name": "arm64-windows-msvc", "hidden": true,
-        "architecture": { "value": "arm64",    "strategy": "external" },
-        "toolset":      { "value": "host=x64", "strategy": "external" },
-        "cacheVariables": {
-            "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-msvc.cmake"
-        }
-    },
-
     {
         "name": "arm64-windows-llvm", "hidden": true,
         "architecture": { "value": "arm64",    "strategy": "external" },
@@ -73,10 +64,6 @@
     { "name": "arm64-apple-clang-release", "inherits": [ "base", "arm64-apple-clang", "reldbg" ] },
     { "name": "arm64-apple-clang+static-release", "inherits": [ "base", "arm64-apple-clang",  "reldbg", "static" ] },
 
-    { "name": "arm64-windows-msvc-debug", "inherits": [ "base", "arm64-windows-msvc",  "debug"   ] },
-    { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg" ] },
-    { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg", "static" ] },
-
     { "name": "x64-windows-llvm-debug", "inherits": [ "base", "x64-windows-llvm", "debug" ] },
     { "name": "x64-windows-llvm-release", "inherits": [ "base", "x64-windows-llvm", "release" ] },
     { "name": "x64-windows-llvm-reldbg", "inherits": [ "base", "x64-windows-llvm", "reldbg" ] },

CODEOWNERS

@@ -2,7 +2,7 @@
 
 /ci/ @ggerganov
 /.devops/*.Dockerfile @ngxson
-/examples/server/ @ngxson
+/tools/server/ @ngxson
 /ggml/src/ggml-cuda/fattn* @JohannesGaessler
 /ggml/src/ggml-cuda/mmq.* @JohannesGaessler
 /ggml/src/ggml-cuda/mmv.* @JohannesGaessler

Makefile

@@ -780,10 +780,6 @@ ifdef GGML_HIP
 
 	MK_CPPFLAGS += -DGGML_USE_HIP -DGGML_USE_CUDA
 
-ifdef GGML_HIP_UMA
-	MK_CPPFLAGS += -DGGML_HIP_UMA
-endif # GGML_HIP_UMA
-
 	MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib
 	MK_LDFLAGS += -L$(ROCM_PATH)/lib64 -Wl,-rpath=$(ROCM_PATH)/lib64
 	MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas
@@ -1160,10 +1156,10 @@ $(LIB_COMMON_S): $(OBJ_COMMON)
 
 # Clean generated server assets
 clean-server-assets:
-	find examples/server -type f -name "*.js.hpp"   -delete
-	find examples/server -type f -name "*.mjs.hpp"  -delete
-	find examples/server -type f -name "*.css.hpp"  -delete
-	find examples/server -type f -name "*.html.hpp" -delete
+	find tools/server -type f -name "*.js.hpp"   -delete
+	find tools/server -type f -name "*.mjs.hpp"  -delete
+	find tools/server -type f -name "*.css.hpp"  -delete
+	find tools/server -type f -name "*.html.hpp" -delete
 
 # Clean rule
 clean: clean-server-assets
@@ -1183,7 +1179,7 @@ clean: clean-server-assets
 # Helper function that replaces .c, .cpp, and .cu file endings with .o:
 GET_OBJ_FILE = $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(patsubst %.cu,%.o,$(1))))
 
-llama-cli: examples/main/main.cpp \
+llama-cli: tools/main/main.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -1191,12 +1187,7 @@ llama-cli: examples/main/main.cpp \
 	@echo '====  Run ./llama-cli -h for help.  ===='
 	@echo
 
-llama-infill: examples/infill/infill.cpp \
-	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-llama-run: examples/run/run.cpp \
+llama-run: tools/run/run.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -1211,7 +1202,7 @@ llama-simple-chat: examples/simple-chat/simple-chat.cpp \
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-llama-tokenize: examples/tokenize/tokenize.cpp \
+llama-tokenize: tools/tokenize/tokenize.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -1221,27 +1212,27 @@ llama-batched: examples/batched/batched.cpp \
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-llama-batched-bench: examples/batched-bench/batched-bench.cpp \
+llama-batched-bench: tools/batched-bench/batched-bench.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-llama-quantize: examples/quantize/quantize.cpp \
+llama-quantize: tools/quantize/quantize.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-llama-quantize-stats: examples/quantize-stats/quantize-stats.cpp \
+llama-quantize-stats: tools/quantize-stats/quantize-stats.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-llama-perplexity: examples/perplexity/perplexity.cpp \
+llama-perplexity: tools/perplexity/perplexity.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-llama-imatrix: examples/imatrix/imatrix.cpp \
+llama-imatrix: tools/imatrix/imatrix.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -1283,7 +1274,7 @@ llama-gguf-hash: examples/gguf-hash/gguf-hash.cpp examples/gguf-hash/deps/sha1/s
 	$(CXX) $(CXXFLAGS) -Iexamples/gguf-hash/deps -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-llama-gguf-split: examples/gguf-split/gguf-split.cpp \
+llama-gguf-split: tools/gguf-split/gguf-split.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -1293,7 +1284,7 @@ llama-eval-callback: examples/eval-callback/eval-callback.cpp \
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-llama-cvector-generator: examples/cvector-generator/cvector-generator.cpp \
+llama-cvector-generator: tools/cvector-generator/cvector-generator.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -1303,12 +1294,12 @@ llama-convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-llama-bench: examples/llama-bench/llama-bench.cpp \
+llama-bench: tools/llama-bench/llama-bench.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-llama-export-lora: examples/export-lora/export-lora.cpp \
+llama-export-lora: tools/export-lora/export-lora.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -1364,17 +1355,17 @@ llama-gbnf-validator: examples/gbnf-validator/gbnf-validator.cpp \
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 ifdef GGML_RPC
-rpc-server: examples/rpc/rpc-server.cpp \
+rpc-server: tools/rpc/rpc-server.cpp \
 	$(OBJ_GGML)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
 endif # GGML_RPC
 
 llama-server: \
-	examples/server/server.cpp \
-	examples/server/utils.hpp \
-	examples/server/httplib.h \
-	examples/server/index.html.hpp \
-	examples/server/loading.html.hpp \
+	tools/server/server.cpp \
+	tools/server/utils.hpp \
+	tools/server/httplib.h \
+	tools/server/index.html.hpp \
+	tools/server/loading.html.hpp \
 	common/chat.cpp \
 	common/chat.h \
 	common/chat-template.hpp \
@@ -1382,10 +1373,10 @@ llama-server: \
 	common/minja.hpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Itools/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
 
-# Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`:
-examples/server/%.hpp: examples/server/public/% FORCE Makefile
+# Portable equivalent of `cd tools/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`:
+tools/server/%.hpp: tools/server/public/% FORCE Makefile
 	@( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \
 		echo "unsigned char $${NAME}[] = {" && \
 		cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \
@@ -1398,36 +1389,36 @@ llama-gen-docs: examples/gen-docs/gen-docs.cpp \
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-libllava.a: examples/llava/llava.cpp \
-	examples/llava/llava.h \
-	examples/llava/clip.cpp \
-	examples/llava/clip.h \
+libllava.a: tools/mtmd/llava.cpp \
+	tools/mtmd/llava.h \
+	tools/mtmd/clip.cpp \
+	tools/mtmd/clip.h \
 	common/stb_image.h \
 	common/base64.hpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual
 
-llama-llava-cli: examples/llava/llava-cli.cpp \
-	examples/llava/llava.cpp \
-	examples/llava/llava.h \
-	examples/llava/clip.cpp \
-	examples/llava/clip.h \
+llama-llava-cli: tools/mtmd/llava-cli.cpp \
+	tools/mtmd/llava.cpp \
+	tools/mtmd/llava.h \
+	tools/mtmd/clip.cpp \
+	tools/mtmd/clip.h \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
 
-llama-minicpmv-cli: examples/llava/minicpmv-cli.cpp \
-	examples/llava/llava.cpp \
-	examples/llava/llava.h \
-	examples/llava/clip.cpp \
-	examples/llava/clip.h \
+llama-minicpmv-cli: tools/mtmd/minicpmv-cli.cpp \
+	tools/mtmd/llava.cpp \
+	tools/mtmd/llava.h \
+	tools/mtmd/clip.cpp \
+	tools/mtmd/clip.h \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
 
-llama-qwen2vl-cli: examples/llava/qwen2vl-cli.cpp \
-	examples/llava/llava.cpp \
-	examples/llava/llava.h \
-	examples/llava/clip.cpp \
-	examples/llava/clip.h \
+llama-qwen2vl-cli: tools/mtmd/qwen2vl-cli.cpp \
+	tools/mtmd/llava.cpp \
+	tools/mtmd/llava.h \
+	tools/mtmd/clip.cpp \
+	tools/mtmd/clip.h \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
 
@@ -1484,12 +1475,12 @@ tests/test-double-float: tests/test-double-float.cpp
 
 tests/test-json-schema-to-grammar: tests/test-json-schema-to-grammar.cpp \
 	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) -Itools/server -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-chat: tests/test-chat.cpp \
 	$(OBJ_ALL)
-	$(CXX) $(CXXFLAGS) -Iexamples/server -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) -Itools/server -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
 tests/test-opt: tests/test-opt.cpp \

README.md

@@ -16,8 +16,10 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 
 ## Hot topics
 
-- **How to use [MTLResidencySet](https://developer.apple.com/documentation/metal/mtlresidencyset?language=objc) to keep the GPU memory active?** https://github.com/ggml-org/llama.cpp/pull/11427
-- **VS Code extension for FIM completions:** https://github.com/ggml-org/llama.vscode
+- 🔥 Multimodal support arrived in `llama-server`: [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) | [documentation](./docs/multimodal.md)
+- **GGML developer experience survey (organized and reviewed by NVIDIA):** [link](https://forms.gle/Gasw3cRgyhNEnrwK9)
+- A new binary `llama-mtmd-cli` is introduced to replace `llava-cli`, `minicpmv-cli`, `gemma3-cli` ([#13012](https://github.com/ggml-org/llama.cpp/pull/13012)) and `qwen2vl-cli` ([#13141](https://github.com/ggml-org/llama.cpp/pull/13141)), `libllava` will be deprecated
+- VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode
 - Universal [tool call support](./docs/function-calling.md) in `llama-server` https://github.com/ggml-org/llama.cpp/pull/9639
 - Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
 - Introducing GGUF-my-LoRA https://github.com/ggml-org/llama.cpp/discussions/10123
@@ -35,7 +37,7 @@ range of hardware - locally and in the cloud.
 - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
 - AVX, AVX2, AVX512 and AMX support for x86 architectures
 - 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
-- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads MTT GPUs via MUSA)
+- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
 - Vulkan and SYCL backend support
 - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
 
@@ -235,13 +237,13 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [BLAS](docs/build.md#blas-build) | All |
 | [BLIS](docs/backend/BLIS.md) | All |
 | [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU |
-| [MUSA](docs/build.md#musa) | Moore Threads MTT GPU |
+| [MUSA](docs/build.md#musa) | Moore Threads GPU |
 | [CUDA](docs/build.md#cuda) | Nvidia GPU |
 | [HIP](docs/build.md#hip) | AMD GPU |
 | [Vulkan](docs/build.md#vulkan) | GPU |
 | [CANN](docs/build.md#cann) | Ascend NPU |
 | [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
-| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/examples/rpc) | All |
+| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
 
 ## Building the project
 
@@ -275,9 +277,9 @@ The Hugging Face platform provides a variety of online tools for converting, qua
 - Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268)
 - Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669)
 
-To learn more about model quantization, [read this documentation](examples/quantize/README.md)
+To learn more about model quantization, [read this documentation](tools/quantize/README.md)
 
-## [`llama-cli`](examples/main)
+## [`llama-cli`](tools/main)
 
 #### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality.
 
@@ -340,7 +342,7 @@ To learn more about model quantization, [read this documentation](examples/quant
     </details>
 
 
-## [`llama-server`](examples/server)
+## [`llama-server`](tools/server)
 
 #### A lightweight, [OpenAI API](https://github.com/openai/openai-openapi) compatible, HTTP server for serving LLMs.
 
@@ -410,7 +412,7 @@ To learn more about model quantization, [read this documentation](examples/quant
     </details>
 
 
-## [`llama-perplexity`](examples/perplexity)
+## [`llama-perplexity`](tools/perplexity)
 
 #### A tool for measuring the perplexity [^1][^2] (and other quality metrics) of a model over a given text.
 
@@ -435,10 +437,10 @@ To learn more about model quantization, [read this documentation](examples/quant
 
     </details>
 
-[^1]: [examples/perplexity/README.md](./examples/perplexity/README.md)
+[^1]: [tools/perplexity/README.md](./tools/perplexity/README.md)
 [^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
 
-## [`llama-bench`](examples/llama-bench)
+## [`llama-bench`](tools/llama-bench)
 
 #### Benchmark the performance of the inference for various parameters.
 
@@ -459,7 +461,7 @@ To learn more about model quantization, [read this documentation](examples/quant
 
     </details>
 
-## [`llama-run`](examples/run)
+## [`llama-run`](tools/run)
 
 #### A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama [^3].
 
@@ -503,8 +505,8 @@ To learn more about model quantization, [read this documentation](examples/quant
 
 ## Other documentation
 
-- [main (cli)](examples/main/README.md)
-- [server](examples/server/README.md)
+- [main (cli)](tools/main/README.md)
+- [server](tools/server/README.md)
 - [GBNF grammars](grammars/README.md)
 
 #### Development documentation
@@ -570,4 +572,11 @@ automatically. For example:
 $ echo "source ~/.llama-completion.bash" >> ~/.bashrc
 ```
 
-## References
+## Dependencies
+
+- [yhirose/cpp-httplib](https://github.com/yhirose/cpp-httplib) - Single-header HTTP server, used by `llama-server` - MIT license
+- [stb-image](https://github.com/nothings/stb) - Single-header image format decoder, used by multimodal subsystem - Public domain
+- [nlohmann/json](https://github.com/nlohmann/json) - Single-header JSON library, used by various tools/examples - MIT License
+- [minja](https://github.com/google/minja) - Minimal Jinja parser in C++, used by various tools/examples - MIT License
+- [linenoise.cpp](./tools/run/linenoise.cpp/linenoise.cpp) - C++ library that provides readline-like line editing capabilities, used by `llama-run` - BSD 2-Clause License
+- [curl](https://curl.se/) - Client-side URL transfer library, used by various tools/examples - [CURL License](https://curl.se/docs/copyright.html)

SECURITY.md

@@ -40,7 +40,8 @@ To protect sensitive data from potential leaks or unauthorized access, it is cru
 ### Untrusted environments or networks
 
 If you can't run your models in a secure and isolated environment or if it must be exposed to an untrusted network, make sure to take the following security precautions:
-* Confirm the hash of any downloaded artifact (e.g. pre-trained model weights) matches a known-good value
+* Do not use the RPC backend, [rpc-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) and [llama-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/server) functionality (see https://github.com/ggml-org/llama.cpp/pull/13061).
+* Confirm the hash of any downloaded artifact (e.g. pre-trained model weights) matches a known-good value.
 * Encrypt your data if sending it over the network.
 
 ### Multi-Tenant environments

build-xcframework.sh

@@ -8,6 +8,7 @@ TVOS_MIN_OS_VERSION=16.4
 
 BUILD_SHARED_LIBS=OFF
 LLAMA_BUILD_EXAMPLES=OFF
+LLAMA_BUILD_TOOLS=OFF
 LLAMA_BUILD_TESTS=OFF
 LLAMA_BUILD_SERVER=OFF
 GGML_METAL=ON
@@ -31,6 +32,7 @@ COMMON_CMAKE_ARGS=(
     -DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
     -DBUILD_SHARED_LIBS=${BUILD_SHARED_LIBS}
     -DLLAMA_BUILD_EXAMPLES=${LLAMA_BUILD_EXAMPLES}
+    -DLLAMA_BUILD_TOOLS=${LLAMA_BUILD_TOOLS}
     -DLLAMA_BUILD_TESTS=${LLAMA_BUILD_TESTS}
     -DLLAMA_BUILD_SERVER=${LLAMA_BUILD_SERVER}
     -DGGML_METAL_EMBED_LIBRARY=${GGML_METAL_EMBED_LIBRARY}
@@ -115,6 +117,7 @@ setup_framework_structure() {
     # Copy all required headers (common for all platforms)
     cp include/llama.h             ${header_path}
     cp ggml/include/ggml.h         ${header_path}
+    cp ggml/include/ggml-opt.h     ${header_path}
     cp ggml/include/ggml-alloc.h   ${header_path}
     cp ggml/include/ggml-backend.h ${header_path}
     cp ggml/include/ggml-metal.h   ${header_path}

ci/README.md

@@ -54,7 +54,7 @@ docker run --privileged -it \
     -v $HOME/llama.cpp/ci-cache:/ci-cache \
     -v $HOME/llama.cpp/ci-results:/ci-results \
     -v $PWD:/ws -w /ws \
-    mthreads/musa:rc3.1.1-devel-ubuntu22.04
+    mthreads/musa:rc4.0.1-mudnn-devel-ubuntu22.04
 ```
 
 Inside the container, execute the following commands:

ci/run.sh

@@ -187,8 +187,8 @@ function gg_run_test_scripts_debug {
 
     set -e
 
-    (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
-    (cd ./examples/quantize   && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+    (cd ./tools/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+    (cd ./tools/quantize   && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
 
     set +e
 }
@@ -211,8 +211,8 @@ function gg_run_test_scripts_release {
 
     set -e
 
-    (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
-    (cd ./examples/quantize   && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+    (cd ./tools/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+    (cd ./tools/quantize   && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
 
     set +e
 }

cmake/arm64-windows-msvc.cmake

@@ -1,6 +0,0 @@
-set( CMAKE_SYSTEM_NAME Windows )
-set( CMAKE_SYSTEM_PROCESSOR arm64 )
-
-set( target arm64-pc-windows-msvc )
-set( CMAKE_C_COMPILER_TARGET   ${target} )
-set( CMAKE_CXX_COMPILER_TARGET ${target} )

cmake/build-info.cmake

@@ -41,14 +41,20 @@ endif()
 
 if(MSVC)
     set(BUILD_COMPILER "${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
-    set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME})
+    if (CMAKE_VS_PLATFORM_NAME)
+        set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME})
+    else()
+        set(BUILD_TARGET "${CMAKE_SYSTEM_NAME} ${CMAKE_SYSTEM_PROCESSOR}")
+    endif()
 else()
     execute_process(
-        COMMAND sh -c "\"$@\" --version | head -1" _ ${CMAKE_C_COMPILER}
+        COMMAND ${CMAKE_C_COMPILER} --version
         OUTPUT_VARIABLE OUT
         OUTPUT_STRIP_TRAILING_WHITESPACE
     )
+    string(REGEX REPLACE " *\n.*" "" OUT "${OUT}")
     set(BUILD_COMPILER ${OUT})
+
     execute_process(
         COMMAND ${CMAKE_C_COMPILER} -dumpmachine
         OUTPUT_VARIABLE OUT

cmake/x64-windows-llvm.cmake

@@ -3,9 +3,3 @@ set( CMAKE_SYSTEM_PROCESSOR x86_64 )
 
 set( CMAKE_C_COMPILER    clang )
 set( CMAKE_CXX_COMPILER  clang++ )
-
-set( arch_c_flags "-march=native" )
-
-set( CMAKE_C_FLAGS_INIT   "${arch_c_flags}" )
-set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags}" )
-

common/CMakeLists.txt

@@ -39,7 +39,9 @@ add_custom_command(
     COMMENT "Generating build details from Git"
     COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}
             -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}
-            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info-gen-cpp.cmake"
+            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
+            -DCMAKE_SYSTEM_NAME=${CMAKE_SYSTEM_NAME} -DCMAKE_SYSTEM_PROCESSOR=${CMAKE_SYSTEM_PROCESSOR}
+            -P "${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info-gen-cpp.cmake"
     WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.."
     DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
     VERBATIM
@@ -71,6 +73,8 @@ add_library(${TARGET} STATIC
     minja/minja.hpp
     ngram-cache.cpp
     ngram-cache.h
+    regex-partial.cpp
+    regex-partial.h
     sampling.cpp
     sampling.h
     speculative.cpp
@@ -117,8 +121,8 @@ if (LLAMA_LLGUIDANCE)
 
     ExternalProject_Add(llguidance_ext
         GIT_REPOSITORY https://github.com/guidance-ai/llguidance
-        # v0.7.10:
-        GIT_TAG 0309d2a6bf40abda35344a362edc71e06d5009f8
+        # v0.7.20 (+ fix to build on GCC 15):
+        GIT_TAG b5b8b64dba11c4e4ee6b1d1450d3a3ae279891e8
         PREFIX ${CMAKE_BINARY_DIR}/llguidance
         SOURCE_DIR ${LLGUIDANCE_SRC}
         BUILD_IN_SOURCE TRUE
@@ -142,3 +146,27 @@ endif ()
 target_include_directories(${TARGET} PUBLIC .)
 target_compile_features   (${TARGET} PUBLIC cxx_std_17)
 target_link_libraries     (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)
+
+
+#
+# copy the license files
+#
+
+# Check if running in GitHub Actions
+if (DEFINED ENV{GITHUB_ACTIONS} AND "$ENV{GITHUB_ACTIONS}" STREQUAL "true")
+    message(STATUS "Running inside GitHub Actions - copying license files")
+
+    # Copy all files from licenses/ to build/bin/
+    file(GLOB LICENSE_FILES "${CMAKE_SOURCE_DIR}/licenses/*")
+    foreach(LICENSE_FILE ${LICENSE_FILES})
+        get_filename_component(FILENAME ${LICENSE_FILE} NAME)
+        add_custom_command(
+            POST_BUILD
+            TARGET ${TARGET}
+            COMMAND ${CMAKE_COMMAND} -E copy_if_different
+                "${LICENSE_FILE}"
+                "$<TARGET_FILE_DIR:llama>/${FILENAME}"
+            COMMENT "Copying ${FILENAME} to ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}")
+        message(STATUS "Copying ${LICENSE_FILE} to ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${FILENAME}")
+    endforeach()
+endif()

common/arg.cpp

@@ -38,6 +38,30 @@
 
 using json = nlohmann::ordered_json;
 
+std::initializer_list<enum llama_example> mmproj_examples = {
+    LLAMA_EXAMPLE_LLAVA,
+    LLAMA_EXAMPLE_SERVER,
+};
+
+static std::string read_file(const std::string & fname) {
+    std::ifstream file(fname);
+    if (!file) {
+        throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
+    }
+    std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
+    file.close();
+    return content;
+}
+
+static void write_file(const std::string & fname, const std::string & content) {
+    std::ofstream file(fname);
+    if (!file) {
+        throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
+    }
+    file << content;
+    file.close();
+}
+
 common_arg & common_arg::set_examples(std::initializer_list<enum llama_example> examples) {
     this->examples = std::move(examples);
     return *this;
@@ -157,6 +181,10 @@ struct common_hf_file_res {
 
 #ifdef LLAMA_USE_CURL
 
+bool common_has_curl() {
+    return true;
+}
+
 #ifdef __linux__
 #include <linux/limits.h>
 #elif defined(_WIN32)
@@ -189,11 +217,11 @@ struct curl_slist_ptr {
 #define CURL_MAX_RETRY 3
 #define CURL_RETRY_DELAY_SECONDS 2
 
-static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds) {
+static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds, const char * method_name) {
     int remaining_attempts = max_attempts;
 
     while (remaining_attempts > 0) {
-        LOG_INF("%s: Trying to download from %s (attempt %d of %d)...\n", __func__ , url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
+        LOG_INF("%s: %s %s (attempt %d of %d)...\n", __func__ , method_name, url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
 
         CURLcode res = curl_easy_perform(curl);
         if (res == CURLE_OK) {
@@ -204,6 +232,7 @@ static bool curl_perform_with_retry(const std::string & url, CURL * curl, int ma
         LOG_WRN("%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay);
 
         remaining_attempts--;
+        if (remaining_attempts == 0) break;
         std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
     }
 
@@ -222,8 +251,6 @@ static bool common_download_file_single(const std::string & url, const std::stri
         return false;
     }
 
-    bool force_download = false;
-
     // Set the URL, allow to follow http redirection
     curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
     curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
@@ -247,7 +274,7 @@ static bool common_download_file_single(const std::string & url, const std::stri
 
     // If the file exists, check its JSON metadata companion file.
     std::string metadata_path = path + ".json";
-    nlohmann::json metadata;
+    nlohmann::json metadata; // TODO @ngxson : get rid of this json, use regex instead
     std::string etag;
     std::string last_modified;
 
@@ -257,14 +284,7 @@ static bool common_download_file_single(const std::string & url, const std::stri
         if (metadata_in.good()) {
             try {
                 metadata_in >> metadata;
-                LOG_INF("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
-                if (metadata.contains("url") && metadata.at("url").is_string()) {
-                    auto previous_url = metadata.at("url").get<std::string>();
-                    if (previous_url != url) {
-                        LOG_ERR("%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str());
-                        return false;
-                    }
-                }
+                LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
                 if (metadata.contains("etag") && metadata.at("etag").is_string()) {
                     etag = metadata.at("etag");
                 }
@@ -272,10 +292,10 @@ static bool common_download_file_single(const std::string & url, const std::stri
                     last_modified = metadata.at("lastModified");
                 }
             } catch (const nlohmann::json::exception & e) {
-            LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
-                return false;
+                LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
             }
         }
+        // if we cannot open the metadata file, we assume that the downloaded file is not valid (etag and last-modified are left empty, so we will download it again)
     } else {
         LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
     }
@@ -287,7 +307,10 @@ static bool common_download_file_single(const std::string & url, const std::stri
     };
 
     common_load_model_from_url_headers headers;
+    bool head_request_ok = false;
+    bool should_download = !file_exists; // by default, we should download if the file does not exist
 
+    // get ETag to see if the remote file has changed
     {
         typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
         auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
@@ -316,23 +339,28 @@ static bool common_download_file_single(const std::string & url, const std::stri
         curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
         curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
 
-        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
+        // we only allow retrying once for HEAD requests
+        // this is for the use case of using running offline (no internet), retrying can be annoying
+        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), 1, 0, "HEAD");
         if (!was_perform_successful) {
-            return false;
+            head_request_ok = false;
         }
 
         long http_code = 0;
         curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
-        if (http_code != 200) {
-            // HEAD not supported, we don't know if the file has changed
-            // force trigger downloading
-            force_download = true;
-            LOG_ERR("%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
+        if (http_code == 200) {
+            head_request_ok = true;
+        } else {
+            LOG_WRN("%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
+            head_request_ok = false;
         }
     }
 
-    bool should_download = !file_exists || force_download;
-    if (!should_download) {
+    // if head_request_ok is false, we don't have the etag or last-modified headers
+    // we leave should_download as-is, which is true if the file does not exist
+    if (head_request_ok) {
+        // check if ETag or Last-Modified headers are different
+        // if it is, we need to download the file again
         if (!etag.empty() && etag != headers.etag) {
             LOG_WRN("%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
             should_download = true;
@@ -341,6 +369,7 @@ static bool common_download_file_single(const std::string & url, const std::stri
             should_download = true;
         }
     }
+
     if (should_download) {
         std::string path_temporary = path + ".downloadInProgress";
         if (file_exists) {
@@ -394,7 +423,7 @@ static bool common_download_file_single(const std::string & url, const std::stri
         // start the download
         LOG_INF("%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
             llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
-        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
+        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS, "GET");
         if (!was_perform_successful) {
             return false;
         }
@@ -415,13 +444,15 @@ static bool common_download_file_single(const std::string & url, const std::stri
             {"etag", headers.etag},
             {"lastModified", headers.last_modified}
         });
-        std::ofstream(metadata_path) << metadata.dump(4);
-        LOG_INF("%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
+        write_file(metadata_path, metadata.dump(4));
+        LOG_DBG("%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
 
         if (rename(path_temporary.c_str(), path.c_str()) != 0) {
             LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
             return false;
         }
+    } else {
+        LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
     }
 
     return true;
@@ -522,6 +553,50 @@ static bool common_download_model(
     return true;
 }
 
+std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params) {
+    curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
+    curl_slist_ptr http_headers;
+    std::vector<char> res_buffer;
+
+    curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
+    curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L);
+    curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
+    typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data);
+    auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t {
+        auto data_vec = static_cast<std::vector<char> *>(data);
+        data_vec->insert(data_vec->end(), (char *)ptr, (char *)ptr + size * nmemb);
+        return size * nmemb;
+    };
+    curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
+    curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_buffer);
+#if defined(_WIN32)
+    curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
+#endif
+    if (params.timeout > 0) {
+        curl_easy_setopt(curl.get(), CURLOPT_TIMEOUT, params.timeout);
+    }
+    if (params.max_size > 0) {
+        curl_easy_setopt(curl.get(), CURLOPT_MAXFILESIZE, params.max_size);
+    }
+    http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
+    for (const auto & header : params.headers) {
+        http_headers.ptr = curl_slist_append(http_headers.ptr, header.c_str());
+    }
+    curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
+
+    CURLcode res = curl_easy_perform(curl.get());
+
+    if (res != CURLE_OK) {
+        std::string error_msg = curl_easy_strerror(res);
+        throw std::runtime_error("error: cannot make GET request: " + error_msg);
+    }
+
+    long res_code;
+    curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code);
+
+    return { res_code, std::move(res_buffer) };
+}
+
 /**
  * Allow getting the HF file from the HF repo with tag (like ollama), for example:
  * - bartowski/Llama-3.2-3B-Instruct-GGUF:q4
@@ -541,46 +616,48 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_
         throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
     }
 
-    // fetch model info from Hugging Face Hub API
-    curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
-    curl_slist_ptr http_headers;
-    std::string res_str;
+    std::string url = get_model_endpoint() + "v2/" + hf_repo + "/manifests/" + tag;
 
-    std::string model_endpoint = get_model_endpoint();
-
-    std::string url = model_endpoint + "v2/" + hf_repo + "/manifests/" + tag;
-    curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
-    curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L);
-    typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data);
-    auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t {
-        static_cast<std::string *>(data)->append((char * ) ptr, size * nmemb);
-        return size * nmemb;
-    };
-    curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
-    curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_str);
-#if defined(_WIN32)
-    curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
-#endif
+    // headers
+    std::vector<std::string> headers;
+    headers.push_back("Accept: application/json");
     if (!bearer_token.empty()) {
-        std::string auth_header = "Authorization: Bearer " + bearer_token;
-        http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str());
+        headers.push_back("Authorization: Bearer " + bearer_token);
     }
     // Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response
-    http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
-    http_headers.ptr = curl_slist_append(http_headers.ptr, "Accept: application/json");
-    curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
+    // User-Agent header is already set in common_remote_get_content, no need to set it here
 
-    CURLcode res = curl_easy_perform(curl.get());
+    // we use "=" to avoid clashing with other component, while still being allowed on windows
+    std::string cached_response_fname = "manifest=" + hf_repo + "=" + tag + ".json";
+    string_replace_all(cached_response_fname, "/", "_");
+    std::string cached_response_path = fs_get_cache_file(cached_response_fname);
 
-    if (res != CURLE_OK) {
-        throw std::runtime_error("error: cannot make GET request to HF API");
+    // make the request
+    common_remote_params params;
+    params.headers = headers;
+    long res_code = 0;
+    std::string res_str;
+    bool use_cache = false;
+    try {
+        auto res = common_remote_get_content(url, params);
+        res_code = res.first;
+        res_str = std::string(res.second.data(), res.second.size());
+    } catch (const std::exception & e) {
+        LOG_WRN("error: failed to get manifest: %s\n", e.what());
+        LOG_WRN("try reading from cache\n");
+        // try to read from cache
+        try {
+            res_str = read_file(cached_response_path);
+            res_code = 200;
+            use_cache = true;
+        } catch (const std::exception & e) {
+            throw std::runtime_error("error: failed to get manifest (check your internet connection)");
+        }
     }
+    std::string ggufFile;
+    std::string mmprojFile;
 
-    long res_code;
-    std::string ggufFile   = "";
-    std::string mmprojFile = "";
-    curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code);
-    if (res_code == 200) {
+    if (res_code == 200 || res_code == 304) {
         // extract ggufFile.rfilename in json, using regex
         {
             std::regex pattern("\"ggufFile\"[\\s\\S]*?\"rfilename\"\\s*:\\s*\"([^\"]+)\"");
@@ -597,6 +674,10 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_
                 mmprojFile = match[1].str();
             }
         }
+        if (!use_cache) {
+            // if not using cached response, update the cache file
+            write_file(cached_response_path, res_str);
+        }
     } else if (res_code == 401) {
         throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token");
     } else {
@@ -613,6 +694,10 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_
 
 #else
 
+bool common_has_curl() {
+    return false;
+}
+
 static bool common_download_file_single(const std::string &, const std::string &, const std::string &) {
     LOG_ERR("error: built without CURL, cannot download model from internet\n");
     return false;
@@ -635,17 +720,30 @@ static struct common_hf_file_res common_get_hf_file(const std::string &, const s
     return {};
 }
 
+std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params &) {
+    if (!url.empty()) {
+        throw std::runtime_error("error: built without CURL, cannot download model from the internet");
+    }
+
+    return {};
+}
+
 #endif // LLAMA_USE_CURL
 
 //
 // utils
 //
 
-static void common_params_handle_model(
+struct handle_model_result {
+    bool found_mmproj = false;
+    common_params_model mmproj;
+};
+
+static handle_model_result common_params_handle_model(
         struct common_params_model & model,
         const std::string & bearer_token,
-        const std::string & model_path_default,
-        bool is_mmproj = false) { // TODO: move is_mmproj to an enum when we have more files?
+        const std::string & model_path_default) {
+    handle_model_result result;
     // handle pre-fill default model path and url based on hf_repo and hf_file
     {
         if (!model.hf_repo.empty()) {
@@ -657,7 +755,12 @@ static void common_params_handle_model(
                         exit(1); // built without CURL, error message already printed
                     }
                     model.hf_repo = auto_detected.repo;
-                    model.hf_file = is_mmproj ? auto_detected.mmprojFile : auto_detected.ggufFile;
+                    model.hf_file = auto_detected.ggufFile;
+                    if (!auto_detected.mmprojFile.empty()) {
+                        result.found_mmproj   = true;
+                        result.mmproj.hf_repo = model.hf_repo;
+                        result.mmproj.hf_file = auto_detected.mmprojFile;
+                    }
                 } else {
                     model.hf_file = model.path;
                 }
@@ -694,6 +797,8 @@ static void common_params_handle_model(
             exit(1);
         }
     }
+
+    return result;
 }
 
 const std::vector<ggml_type> kv_cache_types = {
@@ -827,16 +932,25 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
     }
 
-    common_params_handle_model(params.model,             params.hf_token, DEFAULT_MODEL_PATH);
-    common_params_handle_model(params.speculative.model, params.hf_token, "");
-    common_params_handle_model(params.vocoder.model,     params.hf_token, "");
-
-    // allow --mmproj to be set from -hf
-    // assuming that mmproj is always in the same repo as text model
-    if (!params.model.hf_repo.empty() && ctx_arg.ex == LLAMA_EXAMPLE_LLAVA) {
-        params.mmproj.hf_repo = params.model.hf_repo;
+    // handle model and download
+    {
+        auto res = common_params_handle_model(params.model, params.hf_token, DEFAULT_MODEL_PATH);
+        if (params.no_mmproj) {
+            params.mmproj = {};
+        } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) {
+            // optionally, handle mmproj model when -hf is specified
+            params.mmproj = res.mmproj;
+        }
+        // only download mmproj if the current example is using it
+        for (auto & ex : mmproj_examples) {
+            if (ctx_arg.ex == ex) {
+                common_params_handle_model(params.mmproj,    params.hf_token, "");
+                break;
+            }
+        }
+        common_params_handle_model(params.speculative.model, params.hf_token, "");
+        common_params_handle_model(params.vocoder.model,     params.hf_token, "");
     }
-    common_params_handle_model(params.mmproj,            params.hf_token, "", true);
 
     if (params.escape) {
         string_process_escapes(params.prompt);
@@ -968,7 +1082,6 @@ static void common_params_print_completion(common_params_context & ctx_arg) {
         "llama-embedding",
         "llama-eval-callback",
         "llama-export-lora",
-        "llama-gbnf-validator",
         "llama-gen-docs",
         "llama-gguf",
         "llama-gguf-hash",
@@ -976,20 +1089,18 @@ static void common_params_print_completion(common_params_context & ctx_arg) {
         "llama-gritlm",
         "llama-imatrix",
         "llama-infill",
-        "llama-llava-cli",
+        "llama-mtmd-cli",
         "llama-llava-clip-quantize-cli",
         "llama-lookahead",
         "llama-lookup",
         "llama-lookup-create",
         "llama-lookup-merge",
         "llama-lookup-stats",
-        "llama-minicpmv-cli",
         "llama-parallel",
         "llama-passkey",
         "llama-perplexity",
         "llama-q8dot",
         "llama-quantize",
-        "llama-quantize-stats",
         "llama-qwen2vl-cli",
         "llama-retrieval",
         "llama-run",
@@ -1078,6 +1189,9 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e
         fprintf(stderr, "%s\n", ex.what());
         ctx_arg.params = params_org;
         return false;
+    } catch (std::exception & ex) {
+        fprintf(stderr, "%s\n", ex.what());
+        exit(1); // for other exceptions, we exit with status code 1
     }
 
     return true;
@@ -1169,7 +1283,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.use_color = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
     add_opt(common_arg(
         {"-t", "--threads"}, "N",
         string_format("number of threads to use during generation (default: %d)", params.cpuparams.n_threads),
@@ -1302,7 +1416,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     add_opt(common_arg(
         {"-n", "--predict", "--n-predict"}, "N",
         string_format(
-            ex == LLAMA_EXAMPLE_MAIN || ex == LLAMA_EXAMPLE_INFILL
+            ex == LLAMA_EXAMPLE_MAIN
                 ? "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)"
                 : "number of tokens to predict (default: %d, -1 = infinity)",
             params.n_predict),
@@ -1331,6 +1445,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.n_keep = value;
         }
     ));
+    add_opt(common_arg(
+        {"--swa-full"},
+        string_format("use full-size SWA cache (default: %s)\n"
+            "[(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)", params.swa_full ? "true" : "false"),
+        [](common_params & params) {
+            params.swa_full = true;
+        }
+    ).set_env("LLAMA_ARG_SWA_FULL"));
     add_opt(common_arg(
         {"--no-context-shift"},
         string_format("disables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
@@ -1378,13 +1500,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"-f", "--file"}, "FNAME",
         "a file containing the prompt (default: none)",
         [](common_params & params, const std::string & value) {
-            std::ifstream file(value);
-            if (!file) {
-                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
-            }
+            params.prompt = read_file(value);
             // store the external file name in params
             params.prompt_file = value;
-            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
             if (!params.prompt.empty() && params.prompt.back() == '\n') {
                 params.prompt.pop_back();
             }
@@ -1394,11 +1512,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"-sysf", "--system-prompt-file"}, "FNAME",
         "a file containing the system prompt (default: none)",
         [](common_params & params, const std::string & value) {
-            std::ifstream file(value);
-            if (!file) {
-                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
-            }
-            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.system_prompt));
+            params.system_prompt = read_file(value);
             if (!params.system_prompt.empty() && params.system_prompt.back() == '\n') {
                 params.system_prompt.pop_back();
             }
@@ -1549,7 +1663,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.input_prefix = value;
             params.enable_chat_template = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
         {"--in-suffix"}, "STRING",
         "string to suffix after user inputs with (default: empty)",
@@ -1557,7 +1671,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.input_suffix = value;
             params.enable_chat_template = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
         {"--no-warmup"},
         "skip warming up the model with an empty run",
@@ -1574,7 +1688,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.spm_infill = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_INFILL}));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--samplers"}, "SAMPLERS",
         string_format("samplers that will be used for generation in the order, separated by \';\'\n(default: %s)", sampler_type_names.c_str()),
@@ -1823,15 +1937,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--grammar-file"}, "FNAME",
         "file to read grammar from",
         [](common_params & params, const std::string & value) {
-            std::ifstream file(value);
-            if (!file) {
-                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
-            }
-            std::copy(
-                std::istreambuf_iterator<char>(file),
-                std::istreambuf_iterator<char>(),
-                std::back_inserter(params.sampling.grammar)
-            );
+            params.sampling.grammar = read_file(value);
         }
     ).set_sparam());
     add_opt(common_arg(
@@ -1841,6 +1947,23 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.grammar = json_schema_to_grammar(json::parse(value));
         }
     ).set_sparam());
+    add_opt(common_arg(
+        {"-jf", "--json-schema-file"}, "FILE",
+        "File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\nFor schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead",
+        [](common_params & params, const std::string & value) {
+            std::ifstream file(value);
+            if (!file) {
+                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
+            }
+            std::string schema;
+            std::copy(
+                std::istreambuf_iterator<char>(file),
+                std::istreambuf_iterator<char>(),
+                std::back_inserter(schema)
+            );
+            params.sampling.grammar = json_schema_to_grammar(json::parse(schema));
+        }
+    ).set_sparam());
     add_opt(common_arg(
         {"--pooling"}, "{none,mean,cls,last,rank}",
         "pooling type for embeddings, use model default if unspecified",
@@ -1942,13 +2065,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.grp_attn_w = value;
         }
     ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_MAIN}));
-    add_opt(common_arg(
-        {"-dkvc", "--dump-kv-cache"},
-        "verbose print of the KV cache",
-        [](common_params & params) {
-            params.dump_kv_cache = true;
-        }
-    ));
     add_opt(common_arg(
         {"-nkvo", "--no-kv-offload"},
         "disable KV offload",
@@ -1982,13 +2098,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.cache_type_v = kv_cache_type_from_str(value);
         }
     ).set_env("LLAMA_ARG_CACHE_TYPE_V"));
-    add_opt(common_arg(
-        {"--perplexity", "--all-logits"},
-        string_format("return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false"),
-        [](common_params & params) {
-            params.logits_all = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_PERPLEXITY}));
     add_opt(common_arg(
         {"--hellaswag"},
         "compute HellaSwag score over random tasks from datafile supplied with -f",
@@ -2096,18 +2205,33 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING"));
     add_opt(common_arg(
         {"--mmproj"}, "FILE",
-        "path to a multimodal projector file for LLaVA. see examples/llava/README.md",
+        "path to a multimodal projector file. see tools/mtmd/README.md\n"
+        "note: if -hf is used, this argument can be omitted",
         [](common_params & params, const std::string & value) {
             params.mmproj.path = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_LLAVA}));
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ"));
     add_opt(common_arg(
         {"--mmproj-url"}, "URL",
-        "URL to a multimodal projector file for LLaVA. see examples/llava/README.md",
+        "URL to a multimodal projector file. see tools/mtmd/README.md",
         [](common_params & params, const std::string & value) {
             params.mmproj.url = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_LLAVA}));
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_URL"));
+    add_opt(common_arg(
+        {"--no-mmproj"},
+        "explicitly disable multimodal projector, useful when using -hf",
+        [](common_params & params) {
+            params.no_mmproj = true;
+        }
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ"));
+    add_opt(common_arg(
+        {"--no-mmproj-offload"},
+        "do not offload multimodal projector to GPU",
+        [](common_params & params) {
+            params.mmproj_use_gpu = false;
+        }
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ_OFFLOAD"));
     add_opt(common_arg(
         {"--image"}, "FILE",
         "path to an image file. use with multimodal models. Specify multiple times for batching",
@@ -2314,6 +2438,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ));
+    add_opt(common_arg(
+        {"--no-op-offload"},
+        string_format("disable offloading host tensor operations to device (default: %s)", params.no_op_offload ? "true" : "false"),
+        [](common_params & params) {
+            params.no_op_offload = true;
+        }
+    ));
     add_opt(common_arg(
         {"--lora"}, "FNAME",
         "path to LoRA adapter (can be repeated to use multiple adapters)",
@@ -2382,6 +2513,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     add_opt(common_arg(
         {"-hf", "-hfr", "--hf-repo"}, "<user>/<model>[:quant]",
         "Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"
+        "mmproj is also downloaded automatically if available. to disable, add --no-mmproj\n"
         "example: unsloth/phi-4-GGUF:q4_k_m\n"
         "(default: unused)",
         [](common_params & params, const std::string & value) {
@@ -2454,7 +2586,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, int value) {
             params.n_junk = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
+    ).set_examples({LLAMA_EXAMPLE_PASSKEY, LLAMA_EXAMPLE_PARALLEL}));
     add_opt(common_arg(
         {"--pos"}, "N",
         string_format("position of the passkey in the junk text (default: %d)", params.i_pos),
@@ -2504,13 +2636,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.i_chunk = value;
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+    add_opt(common_arg(
+        {"--parse-special"},
+        string_format("prase special tokens (chat, tool, etc) (default: %s)", params.parse_special ? "true" : "false"),
+        [](common_params & params) {
+            params.parse_special = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
     add_opt(common_arg(
         {"-pps"},
         string_format("is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false"),
         [](common_params & params) {
             params.is_pp_shared = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_BENCH}));
+    ).set_examples({LLAMA_EXAMPLE_BENCH, LLAMA_EXAMPLE_PARALLEL}));
     add_opt(common_arg(
         {"-npp"}, "n0,n1,...",
         "number of prompt tokens",
@@ -2653,7 +2792,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_THREADS_HTTP"));
     add_opt(common_arg(
         {"--cache-reuse"}, "N",
-        string_format("min chunk size to attempt reusing from the cache via KV shifting (default: %d)", params.n_cache_reuse),
+        string_format(
+            "min chunk size to attempt reusing from the cache via KV shifting (default: %d)\n"
+            "[(card)](https://ggml.ai/f0.png)", params.n_cache_reuse
+        ),
         [](common_params & params, int value) {
             params.n_cache_reuse = value;
         }
@@ -2726,7 +2868,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.chat_template = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_LLAVA}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
     add_opt(common_arg(
         {"--chat-template-file"}, "JINJA_TEMPLATE_FILE",
         string_format(
@@ -2736,16 +2878,19 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             "list of built-in templates:\n%s", list_builtin_chat_templates().c_str()
         ),
         [](common_params & params, const std::string & value) {
-            std::ifstream file(value);
-            if (!file) {
-                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
-            }
-            std::copy(
-                std::istreambuf_iterator<char>(file),
-                std::istreambuf_iterator<char>(),
-                std::back_inserter(params.chat_template));
+            params.chat_template = read_file(value);
         }
     ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE"));
+    add_opt(common_arg(
+        {"--no-prefill-assistant"},
+        string_format(
+            "whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)\n"
+            "when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled\n"
+        ),
+        [](common_params & params) {
+            params.prefill_assistant = false;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_PREFILL_ASSISTANT"));
     add_opt(common_arg(
         {"-sps", "--slot-prompt-similarity"}, "SIMILARITY",
         string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity),
@@ -2766,7 +2911,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.simple_io = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
         {"--positive-file"}, "FNAME",
         string_format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()),

common/arg.h

@@ -78,3 +78,12 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e
 
 // function to be used by test-arg-parser
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
+bool common_has_curl();
+
+struct common_remote_params {
+    std::vector<std::string> headers;
+    long timeout = 0; // CURLOPT_TIMEOUT, in seconds ; 0 means no timeout
+    long max_size = 0; // max size of the response ; unlimited if 0 ; max is 2GB
+};
+// get remote file content, returns <http_code, raw_response_body>
+std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params);

common/chat.cpp

@@ -6,6 +6,15 @@
 
 #include <optional>
 
+static std::string format_time(const std::chrono::system_clock::time_point & now, const std::string & format) {
+    auto time = std::chrono::system_clock::to_time_t(now);
+    auto local_time = *std::localtime(&time);
+    std::ostringstream ss;
+    ss << std::put_time(&local_time, format.c_str());
+    auto res = ss.str();
+    return res;
+}
+
 typedef minja::chat_template common_chat_template;
 
 struct common_chat_templates {
@@ -24,6 +33,7 @@ struct templates_params {
     std::string grammar;
     bool add_generation_prompt = true;
     bool extract_reasoning     = true;
+    std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
 };
 
 common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice) {
@@ -125,7 +135,9 @@ std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const json & messa
             msgs.push_back(msg);
         }
     } catch (const std::exception & e) {
-        throw std::runtime_error("Failed to parse messages: " + std::string(e.what()) + "; messages = " + messages.dump(2));
+        // @ngxson : disable otherwise it's bloating the API response
+        // printf("%s\n", std::string("; messages = ") + messages.dump(2));
+        throw std::runtime_error("Failed to parse messages: " + std::string(e.what()));
     }
 
     return msgs;
@@ -937,78 +949,83 @@ static void expect_tool_parameters(const std::string & name, const json & parame
     }
 }
 
-static common_chat_params common_chat_params_init_llama_3_1_tool_calls(const common_chat_template & tmpl, const struct templates_params & inputs, bool allow_python_tag_builtin_tools) {
+static common_chat_params common_chat_params_init_llama_3_x(const common_chat_template & tmpl, const struct templates_params & inputs, bool allow_python_tag_builtin_tools) {
     auto builtin_tools = json::array();
     common_chat_params data;
-    data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        std::vector<std::string> tool_rules;
+    if (!inputs.tools.is_null()) {
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            std::vector<std::string> tool_rules;
 
-        auto handle_builtin_tool = [&](const std::string & name, const json & parameters) {
-            if (name == "wolfram_alpha" || name == "web_search" || name == "brave_search") {
-                // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py
-                // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py
-                expect_tool_parameters(name, parameters, {"query"});
-            } else if (name == "python" || name == "code_interpreter") {
-                // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py
-                expect_tool_parameters(name, parameters, {"code"});
-            } else {
-                return false;
+            auto handle_builtin_tool = [&](const std::string & name, const json & parameters) {
+                if (name == "wolfram_alpha" || name == "web_search" || name == "brave_search") {
+                    // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/wolfram_alpha/wolfram_alpha.py
+                    // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/tool_runtime/brave_search/brave_search.py
+                    expect_tool_parameters(name, parameters, {"query"});
+                } else if (name == "python" || name == "code_interpreter") {
+                    // https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/inline/tool_runtime/code_interpreter/code_interpreter.py
+                    expect_tool_parameters(name, parameters, {"code"});
+                } else {
+                    return false;
+                }
+
+                std::vector<std::string> kvs;
+                for (const auto & [key, value] : parameters.at("properties").items()) {
+                    kvs.push_back("\"" + key + "=\" " + builder.add_schema(name + "-args-" + key, value)); // NOLINT
+                }
+
+                tool_rules.push_back(
+                    builder.add_rule(
+                        name + "-call",
+                        "\"<|python_tag|>" + name + ".call(\" " + string_join(kvs, " \", \" ") + " \")\""));
+                builtin_tools.push_back(name);
+
+                return true;
+            };
+
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string name = function.at("name");
+                auto parameters = function.at("parameters");
+                builder.resolve_refs(parameters);
+
+                // https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/tool_runtime
+                if (allow_python_tag_builtin_tools) {
+                    handle_builtin_tool(name, parameters);
+                }
+                tool_rules.push_back(
+                    builder.add_rule(
+                        name + "-call",
+                        "\"{\" space "
+                        "( \"\\\"type\\\"\"       space \":\" space \"\\\"function\\\"\"     space \",\" space )? "
+                        "  \"\\\"name\\\"\"       space \":\" space \"\\\"" + name + "\\\"\" space \",\" space "
+                        "  \"\\\"parameters\\\"\" space \":\" space " + builder.add_schema(name + "-args", parameters) + " "
+                        "\"}\" space"));
+            });
+            // Small models may hallucinate function names so we match anything (*at the start*) that looks like the JSON of a function call, regardless of the name.
+            data.grammar_triggers.push_back({
+                COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_START,
+                "\\{\\s*(?:\"type\"\\s*:\\s*\"function\"\\s*,\\s*)?\"name\"\\s*:\\s*\"", // + name + "\"[\\s\\S]*",
+            });
+            if (!builtin_tools.empty()) {
+                data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|python_tag|>"});
+                data.preserved_tokens.push_back("<|python_tag|>");
             }
-
-            std::vector<std::string> kvs;
-            for (const auto & [key, value] : parameters.at("properties").items()) {
-                kvs.push_back("\"" + key + "=\" " + builder.add_schema(name + "-args-" + key, value)); // NOLINT
-            }
-
-            tool_rules.push_back(
-                builder.add_rule(
-                    name + "-call",
-                    "\"<|python_tag|>" + name + ".call(\" " + string_join(kvs, " \", \" ") + " \")\""));
-            builtin_tools.push_back(name);
-
-            return true;
-        };
-
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool.at("function");
-            std::string name = function.at("name");
-            auto parameters = function.at("parameters");
-            builder.resolve_refs(parameters);
-
-            // https://github.com/meta-llama/llama-stack/tree/main/llama_stack/providers/remote/tool_runtime
-            if (allow_python_tag_builtin_tools) {
-                handle_builtin_tool(name, parameters);
-            }
-            tool_rules.push_back(
-                builder.add_rule(
-                    name + "-call",
-                    "\"{\" space "
-                    "( \"\\\"type\\\"\"       space \":\" space \"\\\"function\\\"\"     space \",\" space )? "
-                    "  \"\\\"name\\\"\"       space \":\" space \"\\\"" + name + "\\\"\" space \",\" space "
-                    "  \"\\\"parameters\\\"\" space \":\" space " + builder.add_schema(name + "-args", parameters) + " "
-                    "\"}\" space"));
+            // Allow a few empty lines on top of the usual constrained json schema space rule.
+            builder.add_rule("root", string_join(tool_rules, " | "));
+            data.additional_stops.push_back("<|eom_id|>");
         });
-        // Small models may hallucinate function names so we match anything (*at the start*) that looks like the JSON of a function call, regardless of the name.
-        data.grammar_triggers.push_back({
-            COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_START,
-            "\\{\\s*(?:\"type\"\\s*:\\s*\"function\"\\s*,\\s*)?\"name\"\\s*:\\s*\"", // + name + "\"[\\s\\S]*",
-        });
-        if (!builtin_tools.empty()) {
-            data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|python_tag|>"});
-            data.preserved_tokens.push_back("<|python_tag|>");
-        }
-        // Allow a few empty lines on top of the usual constrained json schema space rule.
-        builder.add_rule("root", string_join(tool_rules, " | "));
-    });
-    data.additional_stops.push_back("<|eom_id|>");
+        data.format = allow_python_tag_builtin_tools && !builtin_tools.empty()
+            ? COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS
+            : COMMON_CHAT_FORMAT_LLAMA_3_X;
+    } else {
+        data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
+    }
     data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt, {
+        {"date_string", format_time(inputs.now, "%d %b %Y")},
         {"tools_in_user_message", false},
         {"builtin_tools", builtin_tools.empty() ? json() : builtin_tools},
     });
-    data.format = allow_python_tag_builtin_tools && !builtin_tools.empty()
-        ? COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS
-        : COMMON_CHAT_FORMAT_LLAMA_3_X;
     return data;
 }
 static common_chat_msg common_chat_parse_llama_3_1(const std::string & input, bool with_builtin_tools = false) {
@@ -1148,7 +1165,7 @@ static common_chat_params common_chat_params_init_firefunction_v2(const common_c
     LOG_DBG("%s\n", __func__);
     common_chat_params data;
     data.prompt = apply(tmpl, inputs.messages, /* tools= */ nullptr, inputs.add_generation_prompt, {
-        {"datetime", "Jan 29 2025 13:00:00 GMT"},
+        {"datetime", format_time(inputs.now, "%b %d %Y %H:%M:%S GMT")},
         {"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))},
     });
     if (inputs.tools.is_array() && !inputs.tools.empty()) {
@@ -1283,55 +1300,59 @@ static common_chat_msg common_chat_parse_functionary_v3_2(const std::string & in
 static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(const common_chat_template & tmpl, const struct templates_params & inputs) {
     // https://github.com/MeetKai/functionary/blob/main/tests/prompt_test_v3-llama3.1.txt
     common_chat_params data;
-    json tools = inputs.tools.is_null() ? inputs.tools : json::array();
-    std::string python_code_argument_name;
-    auto has_raw_python = false;
 
-    data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        std::vector<std::string> tool_rules;
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool.at("function");
-            const auto & parameters = function.at("parameters");
-            std::string name = function.at("name");
-            if (name == "python" || name == "ipython") {
-                if (!parameters.contains("type")) {
-                    throw std::runtime_error("Missing type in python tool");
-                }
-                has_raw_python = true;
-                const auto & type = parameters.at("type");
-                if (type == "object") {
-                    auto properties = parameters.at("properties");
-                    for (auto it = properties.begin(); it != properties.end(); ++it) {
-                        if (it.value().at("type") == "string") {
-                            if (!python_code_argument_name.empty()) {
-                                throw std::runtime_error("Multiple string arguments found in python tool");
+    if (!inputs.tools.is_null()) {
+        std::string python_code_argument_name;
+        auto has_raw_python = false;
+
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            std::vector<std::string> tool_rules;
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                const auto & parameters = function.at("parameters");
+                std::string name = function.at("name");
+                if (name == "python" || name == "ipython") {
+                    if (!parameters.contains("type")) {
+                        throw std::runtime_error("Missing type in python tool");
+                    }
+                    has_raw_python = true;
+                    const auto & type = parameters.at("type");
+                    if (type == "object") {
+                        auto properties = parameters.at("properties");
+                        for (auto it = properties.begin(); it != properties.end(); ++it) {
+                            if (it.value().at("type") == "string") {
+                                if (!python_code_argument_name.empty()) {
+                                    throw std::runtime_error("Multiple string arguments found in python tool");
+                                }
+                                python_code_argument_name = it.key();
                             }
-                            python_code_argument_name = it.key();
                         }
+                        if (python_code_argument_name.empty()) {
+                            throw std::runtime_error("No string argument found in python tool");
+                        }
+                    } else if (type != "string") {
+                        throw std::runtime_error("Invalid type in python tool: " + type.dump());
                     }
-                    if (python_code_argument_name.empty()) {
-                        throw std::runtime_error("No string argument found in python tool");
-                    }
-                } else if (type != "string") {
-                    throw std::runtime_error("Invalid type in python tool: " + type.dump());
                 }
+                tool_rules.push_back(builder.add_rule(name + "-call", "\"<function=" + name + ">\" " + builder.add_schema(name + "-args", parameters) + " \"</function>\" space"));
+            });
+            if (has_raw_python) {
+                tool_rules.push_back(builder.add_rule("python-call", "\"<|python_tag|>\" .*"));
+                data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|python_tag|>"});
+                data.preserved_tokens.push_back("<|python_tag|>");
             }
-            tool_rules.push_back(builder.add_rule(name + "-call", "\"<function=" + name + ">\" " + builder.add_schema(name + "-args", parameters) + " \"</function>\" space"));
+            auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " space";
+            builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call);
+            data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<function="});
         });
-        if (has_raw_python) {
-            tool_rules.push_back(builder.add_rule("python-call", "\"<|python_tag|>\" .*"));
-            data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|python_tag|>"});
-            data.preserved_tokens.push_back("<|python_tag|>");
-        }
-        auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | ")) + " space";
-        builder.add_rule("root", inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call);
-        data.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<function="});
-    });
+        data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1;
+    } else {
+        data.format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
+    }
 
     data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
     // TODO: if (has_raw_python)
-    data.format = COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1;
     return data;
 }
 static common_chat_msg common_chat_parse_functionary_v3_1_llama_3_1(const std::string & input) {
@@ -1591,6 +1612,7 @@ static common_chat_params common_chat_templates_apply_jinja(
     params.extract_reasoning = inputs.extract_reasoning;
     params.tool_choice = inputs.tool_choice;
     params.grammar = inputs.grammar;
+    params.now = inputs.now;
     if (!inputs.json_schema.empty()) {
         params.json_schema = json::parse(inputs.json_schema);
     }
@@ -1642,21 +1664,21 @@ static common_chat_params common_chat_templates_apply_jinja(
         return common_chat_params_init_firefunction_v2(tmpl, params);
     }
 
-    // Plain handler (no tools)
-    if (params.tools.is_null() || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
-        return common_chat_params_init_without_tools(tmpl, params);
-    }
-
     // Functionary v3.1 (w/ tools)
     if (src.find("<|start_header_id|>") != std::string::npos
         && src.find("<function=") != std::string::npos) {
         return common_chat_params_init_functionary_v3_1_llama_3_1(tmpl, params);
     }
 
-    // Llama 3.1, 3.2, 3.3 (w/ tools)
+    // Llama 3.1, 3.2, 3.3 (also requires date_string so using it even w/o tools)
     if (src.find("<|start_header_id|>ipython<|end_header_id|>") != std::string::npos) {
         auto allow_python_tag_builtin_tools = src.find("<|python_tag|>") != std::string::npos;
-        return common_chat_params_init_llama_3_1_tool_calls(tmpl, params, allow_python_tag_builtin_tools);
+        return common_chat_params_init_llama_3_x(tmpl, params, allow_python_tag_builtin_tools);
+    }
+
+    // Plain handler (no tools)
+    if (params.tools.is_null() || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+        return common_chat_params_init_without_tools(tmpl, params);
     }
 
     // Mistral Nemo (w/ tools)

common/chat.h

@@ -3,6 +3,7 @@
 #pragma once
 
 #include "common.h"
+#include <chrono>
 #include <string>
 #include <vector>
 
@@ -71,6 +72,7 @@ struct common_chat_templates_inputs {
     common_chat_tool_choice tool_choice = COMMON_CHAT_TOOL_CHOICE_AUTO;
     bool parallel_tool_calls = false;
     bool extract_reasoning     = true;
+    std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
 };
 
 struct common_chat_params {

common/common.cpp

@@ -443,6 +443,25 @@ void string_replace_all(std::string & s, const std::string & search, const std::
     s = std::move(builder);
 }
 
+bool string_ends_with(const std::string_view & str, const std::string_view & suffix) {
+    return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
+}
+size_t string_find_partial_stop(const std::string_view & str, const std::string_view & stop) {
+    if (!str.empty() && !stop.empty()) {
+        const char text_last_char = str.back();
+        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
+            if (stop[char_index] == text_last_char) {
+                const auto current_partial = stop.substr(0, char_index + 1);
+                if (string_ends_with(str, current_partial)) {
+                    return str.size() - char_index - 1;
+                }
+            }
+        }
+    }
+
+    return std::string::npos;
+}
+
 std::string regex_escape(const std::string & s) {
     static const std::regex special_chars("[.^$|()*+?\\[\\]{}\\\\]");
     return std::regex_replace(s, special_chars, "\\$0");
@@ -830,7 +849,7 @@ std::string fs_get_cache_directory() {
     if (getenv("LLAMA_CACHE")) {
         cache_directory = std::getenv("LLAMA_CACHE");
     } else {
-#if defined(__linux__) || defined(__FreeBSD__)
+#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX)
         if (std::getenv("XDG_CACHE_HOME")) {
             cache_directory = std::getenv("XDG_CACHE_HOME");
         } else {
@@ -1083,6 +1102,9 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
         mparams.tensor_buft_overrides = params.tensor_buft_overrides.data();
     }
 
+    mparams.progress_callback           = params.load_progress_callback;
+    mparams.progress_callback_user_data = params.load_progress_callback_user_data;
+
     return mparams;
 }
 
@@ -1096,7 +1118,6 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     cparams.n_threads         = params.cpuparams.n_threads;
     cparams.n_threads_batch   = params.cpuparams_batch.n_threads == -1 ?
                                 params.cpuparams.n_threads : params.cpuparams_batch.n_threads;
-    cparams.logits_all        = params.logits_all;
     cparams.embeddings        = params.embedding;
     cparams.rope_scaling_type = params.rope_scaling_type;
     cparams.rope_freq_base    = params.rope_freq_base;
@@ -1114,6 +1135,8 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     cparams.offload_kqv       = !params.no_kv_offload;
     cparams.flash_attn        = params.flash_attn;
     cparams.no_perf           = params.no_perf;
+    cparams.op_offload        = !params.no_op_offload;
+    cparams.swa_full          = params.swa_full;
 
     if (params.reranking) {
         cparams.embeddings    = true;
@@ -1306,81 +1329,6 @@ std::string common_detokenize(const struct llama_vocab * vocab, const std::vecto
     return text;
 }
 
-//
-// KV cache utils
-//
-
-void common_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) {
-    static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
-
-    printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
-        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
-
-    llama_kv_cache_view_cell * c_curr = view.cells;
-    llama_seq_id * cs_curr = view.cells_sequences;
-
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
-        if (i % row_size == 0) {
-            printf("\n%5d: ", i);
-        }
-        int seq_count = 0;
-        for (int j = 0; j < view.n_seq_max; j++) {
-            if (cs_curr[j] >= 0) { seq_count++; }
-        }
-        putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
-    }
-
-    printf("\n=== Done dumping\n");
-}
-
-void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) {
-    static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
-
-    printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
-        view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
-
-    std::unordered_map<llama_seq_id, size_t> seqs;
-    llama_kv_cache_view_cell * c_curr = view.cells;
-    llama_seq_id * cs_curr = view.cells_sequences;
-
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
-        for (int j = 0; j < view.n_seq_max; j++) {
-            if (cs_curr[j] < 0) { continue; }
-            if (seqs.find(cs_curr[j]) == seqs.end()) {
-                if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
-                const size_t sz = seqs.size();
-                seqs[cs_curr[j]] = sz;
-            }
-        }
-        if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
-    }
-
-    printf("=== Sequence legend: ");
-    for (const auto & it : seqs) {
-        printf("%zu=%d, ", it.second, it.first);
-    }
-    printf("'+'=other sequence ids");
-
-    c_curr = view.cells;
-    cs_curr = view.cells_sequences;
-    for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
-        if (i % row_size == 0) {
-            printf("\n%5d: ", i);
-        }
-        for (int j = 0; j < view.n_seq_max; j++) {
-            if (cs_curr[j] >= 0) {
-                const auto & it = seqs.find(cs_curr[j]);
-                putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
-            } else {
-                putchar('.');
-            }
-        }
-        putchar(' ');
-    }
-
-    printf("\n=== Done dumping\n");
-}
-
 //
 // Embedding utils
 //
@@ -1565,3 +1513,20 @@ common_control_vector_data common_control_vector_load(const std::vector<common_c
 
     return result;
 }
+
+ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride) {
+    const int64_t ne_datapoint = llama_n_ctx(ctx);
+    const int64_t ndata        = (tokens.size() - ne_datapoint - 1) / stride;
+    ggml_opt_dataset_t result = ggml_opt_dataset_init(
+        GGML_TYPE_I32, GGML_TYPE_I32, ne_datapoint, ne_datapoint, ndata, /*ndata_shard =*/ 1);
+
+    llama_token * data   = (llama_token *) ggml_opt_dataset_data(result)->data;
+    llama_token * labels = (llama_token *) ggml_opt_dataset_labels(result)->data;
+
+    for (int64_t idata = 0; idata < ndata; ++idata) {
+        memcpy(data   + idata*ne_datapoint, tokens.data() + idata*stride + 0, ne_datapoint*sizeof(llama_token));
+        memcpy(labels + idata*ne_datapoint, tokens.data() + idata*stride + 1, ne_datapoint*sizeof(llama_token));
+    }
+
+    return result;
+}

common/common.h

@@ -6,6 +6,7 @@
 
 #include <set>
 #include <string>
+#include <string_view>
 #include <vector>
 #include <sstream>
 
@@ -66,7 +67,6 @@ enum llama_example {
     LLAMA_EXAMPLE_COMMON,
     LLAMA_EXAMPLE_SPECULATIVE,
     LLAMA_EXAMPLE_MAIN,
-    LLAMA_EXAMPLE_INFILL,
     LLAMA_EXAMPLE_EMBEDDING,
     LLAMA_EXAMPLE_PERPLEXITY,
     LLAMA_EXAMPLE_RETRIEVAL,
@@ -96,6 +96,7 @@ enum common_sampler_type {
     COMMON_SAMPLER_TYPE_XTC         = 8,
     COMMON_SAMPLER_TYPE_INFILL      = 9,
     COMMON_SAMPLER_TYPE_PENALTIES   = 10,
+    COMMON_SAMPLER_TYPE_TOP_N_SIGMA = 11,
 };
 
 // dimensionality reduction methods, used by cvector-generator
@@ -161,6 +162,7 @@ struct common_params_sampling {
     std::vector<enum common_sampler_type> samplers = {
         COMMON_SAMPLER_TYPE_PENALTIES,
         COMMON_SAMPLER_TYPE_DRY,
+        COMMON_SAMPLER_TYPE_TOP_N_SIGMA,
         COMMON_SAMPLER_TYPE_TOP_K,
         COMMON_SAMPLER_TYPE_TYPICAL_P,
         COMMON_SAMPLER_TYPE_TOP_P,
@@ -321,17 +323,17 @@ struct common_params {
     bool flash_attn        = false; // flash attention
     bool no_perf           = false; // disable performance metrics
     bool ctx_shift         = true;  // context shift on inifinite text generation
+    bool swa_full          = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
-    bool logits_all        = false; // return logits for all tokens in the batch
     bool use_mmap          = true;  // use mmap for faster loads
     bool use_mlock         = false; // use mlock to keep model in memory
     bool verbose_prompt    = false; // print prompt tokens before generation
     bool display_prompt    = true;  // print prompt before generation
-    bool dump_kv_cache     = false; // dump the KV cache contents for debugging purposes
     bool no_kv_offload     = false; // disable KV offloading
     bool warmup            = true;  // warmup run
     bool check_tensors     = false; // validate tensor data
+    bool no_op_offload     = false; // globally disable offload host tensor operations to device
 
     bool single_turn       = false; // single turn chat conversation
 
@@ -340,8 +342,10 @@ struct common_params {
 
     common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO;
 
-    // multimodal models (see examples/llava)
+    // multimodal models (see tools/mtmd)
     struct common_params_model mmproj;
+    bool mmproj_use_gpu = true;     // use GPU for multimodal model
+    bool no_mmproj = false;         // explicitly disable multimodal model
     std::vector<std::string> image; // path to image file(s)
 
     // embedding
@@ -364,6 +368,7 @@ struct common_params {
     bool use_jinja = false;                                                                                 // NOLINT
     bool enable_chat_template = true;
     common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
+    bool prefill_assistant = true;                                                                          // if true, any trailing assistant message will be prefilled into the response
 
     std::vector<std::string> api_keys;
 
@@ -407,13 +412,14 @@ struct common_params {
 
     bool process_output = false; // collect data for the output tensor
     bool compute_ppl    = true;  // whether to compute perplexity
+    bool parse_special  = false; // whether to parse special tokens during imatrix tokenization
 
     // cvector-generator params
     int n_pca_batch = 100;
     int n_pca_iterations = 1000;
     dimre_method cvector_dimre_method = DIMRE_METHOD_PCA;
-    std::string cvector_positive_file = "examples/cvector-generator/positive.txt";
-    std::string cvector_negative_file = "examples/cvector-generator/negative.txt";
+    std::string cvector_positive_file = "tools/cvector-generator/positive.txt";
+    std::string cvector_negative_file = "tools/cvector-generator/negative.txt";
 
     bool spm_infill = false; // suffix/prefix/middle pattern for infill
 
@@ -422,6 +428,11 @@ struct common_params {
 
     // common params
     std::string out_file; // output filename for all example programs
+    // optional callback for model loading progress and cancellation:
+    // called with a progress value between 0.0 and 1.0.
+    // return false from callback to abort model loading or true to continue
+    llama_progress_callback load_progress_callback = NULL;
+    void *                  load_progress_callback_user_data = NULL;
 };
 
 // call once at the start of a program if it uses libcommon
@@ -499,10 +510,9 @@ static bool string_starts_with(const std::string & str,
     return str.rfind(prefix, 0) == 0;
 }
 
-static bool string_ends_with(const std::string & str,
-                               const std::string & suffix) {  // While we wait for C++20's std::string::ends_with...
-    return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
-}
+// While we wait for C++20's std::string::ends_with...
+bool string_ends_with(const std::string_view & str, const std::string_view & suffix);
+size_t string_find_partial_stop(const std::string_view & str, const std::string_view & stop);
 
 bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
 void string_process_escapes(std::string & input);
@@ -611,16 +621,6 @@ std::string common_detokenize(
         const std::vector<llama_token> & tokens,
                                   bool   special = true);
 
-//
-// KV cache utils
-//
-
-// Dump the KV cache view with the number of sequences per cell.
-void common_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80);
-
-// Dump the KV cache view showing individual sequences in each cell (long output).
-void common_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
-
 //
 // Embedding utils
 //
@@ -662,3 +662,9 @@ const char * const LLM_KV_SPLIT_COUNT         = "split.count";
 const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
 
 }
+
+//
+// training utils
+//
+
+ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride);

common/json-schema-to-grammar.cpp

@@ -16,6 +16,9 @@ using json = nlohmann::ordered_json;
 static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") {
     auto has_max = max_items != std::numeric_limits<int>::max();
 
+    if (max_items == 0) {
+        return "";
+    }
     if (min_items == 0 && max_items == 1) {
         return item_rule + "?";
     }

common/llguidance.cpp

@@ -189,6 +189,7 @@ static LlgTokenizer * llama_sampler_llg_new_tokenizer(const llama_vocab * vocab)
         /* .tokenize_fn                        = */ llama_sampler_llg_tokenize_fn,
         /* .use_approximate_greedy_tokenize_fn = */ false,
         /* .tokenize_user_data                 = */ vocab,
+        /* .slices                             = */ nullptr,
     };
 
     char           error_buffer[1024];

common/minja/chat-template.hpp

@@ -13,10 +13,12 @@
 #include <chrono>
 #include <cstddef>
 #include <cstdio>
+#include <ctime>
 #include <exception>
 #include <iomanip>
 #include <memory>
 #include <sstream>
+#include <stdexcept>
 #include <string>
 #include <vector>
 
@@ -393,8 +395,8 @@ class chat_template {
 
             for (const auto & message_ : adjusted_messages) {
                 auto message = message_;
-                if (!message.contains("role") || !message.contains("content")) {
-                    throw std::runtime_error("message must have 'role' and 'content' fields: " + message.dump());
+                if (!message.contains("role") || (!message.contains("content") && !message.contains("tool_calls"))) {
+                    throw std::runtime_error("message must have 'role' and one of 'content' or 'tool_calls' fields: " + message.dump());
                 }
                 std::string role = message.at("role");
 
@@ -415,7 +417,6 @@ class chat_template {
                         }
                     }
                     if (polyfill_tool_calls) {
-                        auto content = message.at("content");
                         auto tool_calls = json::array();
                         for (const auto & tool_call : message.at("tool_calls")) {
                             if (tool_call.at("type") != "function") {
@@ -434,8 +435,11 @@ class chat_template {
                         auto obj = json {
                             {"tool_calls", tool_calls},
                         };
-                        if (!content.is_null() && !content.empty()) {
-                            obj["content"] = content;
+                        if (message.contains("content")) {
+                            auto content = message.at("content");
+                            if (!content.is_null() && !content.empty()) {
+                                obj["content"] = content;
+                            }
                         }
                         message["content"] = obj.dump(2);
                         message.erase("tool_calls");

common/minja/minja.hpp

@@ -11,6 +11,7 @@
 #include <algorithm>
 #include <cctype>
 #include <cstddef>
+#include <cstdint>
 #include <cmath>
 #include <exception>
 #include <functional>
@@ -233,7 +234,7 @@ public:
       }
     } else if (is_object()) {
       if (!index.is_hashable())
-        throw std::runtime_error("Unashable type: " + index.dump());
+        throw std::runtime_error("Unhashable type: " + index.dump());
       auto it = object_->find(index.primitive_);
       if (it == object_->end())
         throw std::runtime_error("Key not found: " + index.dump());
@@ -252,7 +253,7 @@ public:
       auto index = key.get<int>();
       return array_->at(index < 0 ? array_->size() + index : index);
     } else if (object_) {
-      if (!key.is_hashable()) throw std::runtime_error("Unashable type: " + dump());
+      if (!key.is_hashable()) throw std::runtime_error("Unhashable type: " + dump());
       auto it = object_->find(key.primitive_);
       if (it == object_->end()) return Value();
       return it->second;
@@ -261,7 +262,7 @@ public:
   }
   void set(const Value& key, const Value& value) {
     if (!object_) throw std::runtime_error("Value is not an object: " + dump());
-    if (!key.is_hashable()) throw std::runtime_error("Unashable type: " + dump());
+    if (!key.is_hashable()) throw std::runtime_error("Unhashable type: " + dump());
     (*object_)[key.primitive_] = value;
   }
   Value call(const std::shared_ptr<Context> & context, ArgumentsValue & args) const {
@@ -398,7 +399,7 @@ public:
       }
       return false;
     } else if (object_) {
-      if (!value.is_hashable()) throw std::runtime_error("Unashable type: " + value.dump());
+      if (!value.is_hashable()) throw std::runtime_error("Unhashable type: " + value.dump());
       return object_->find(value.primitive_) != object_->end();
     } else {
       throw std::runtime_error("contains can only be called on arrays and objects: " + dump());
@@ -416,7 +417,7 @@ public:
     return const_cast<Value*>(this)->at(index);
   }
   Value& at(const Value & index) {
-    if (!index.is_hashable()) throw std::runtime_error("Unashable type: " + dump());
+    if (!index.is_hashable()) throw std::runtime_error("Unhashable type: " + dump());
     if (is_array()) return array_->at(index.get<int>());
     if (is_object()) return object_->at(index.primitive_);
     throw std::runtime_error("Value is not an array or object: " + dump());
@@ -676,8 +677,8 @@ public:
 class VariableExpr : public Expression {
     std::string name;
 public:
-    VariableExpr(const Location & location, const std::string& n)
-      : Expression(location), name(n) {}
+    VariableExpr(const Location & loc, const std::string& n)
+      : Expression(loc), name(n) {}
     std::string get_name() const { return name; }
     Value do_evaluate(const std::shared_ptr<Context> & context) const override {
         if (!context->contains(name)) {
@@ -1200,9 +1201,9 @@ public:
 
 class SliceExpr : public Expression {
 public:
-    std::shared_ptr<Expression> start, end;
-    SliceExpr(const Location & loc, std::shared_ptr<Expression> && s, std::shared_ptr<Expression> && e)
-      : Expression(loc), start(std::move(s)), end(std::move(e)) {}
+    std::shared_ptr<Expression> start, end, step;
+    SliceExpr(const Location & loc, std::shared_ptr<Expression> && s, std::shared_ptr<Expression> && e, std::shared_ptr<Expression> && st = nullptr)
+      : Expression(loc), start(std::move(s)), end(std::move(e)), step(std::move(st)) {}
     Value do_evaluate(const std::shared_ptr<Context> &) const override {
         throw std::runtime_error("SliceExpr not implemented");
     }
@@ -1219,18 +1220,35 @@ public:
         if (!index) throw std::runtime_error("SubscriptExpr.index is null");
         auto target_value = base->evaluate(context);
         if (auto slice = dynamic_cast<SliceExpr*>(index.get())) {
-          auto start = slice->start ? slice->start->evaluate(context).get<int64_t>() : 0;
-          auto end = slice->end ? slice->end->evaluate(context).get<int64_t>() : (int64_t) target_value.size();
+          auto len = target_value.size();
+          auto wrap = [len](int64_t i) -> int64_t {
+            if (i < 0) {
+              return i + len;
+            }
+            return i;
+          };
+          int64_t step = slice->step ? slice->step->evaluate(context).get<int64_t>() : 1;
+          if (!step) {
+            throw std::runtime_error("slice step cannot be zero");
+          }
+          int64_t start = slice->start ? wrap(slice->start->evaluate(context).get<int64_t>()) : (step < 0 ? len - 1 : 0);
+          int64_t end = slice->end ? wrap(slice->end->evaluate(context).get<int64_t>()) : (step < 0 ? -1 : len);
           if (target_value.is_string()) {
             std::string s = target_value.get<std::string>();
-            if (start < 0) start = s.size() + start;
-            if (end < 0) end = s.size() + end;
-            return s.substr(start, end - start);
+
+            std::string result;
+            if (start < end && step == 1) {
+              result = s.substr(start, end - start);
+            } else {
+              for (int64_t i = start; step > 0 ? i < end : i > end; i += step) {
+                result += s[i];
+              }
+            }
+            return result;
+
           } else if (target_value.is_array()) {
-            if (start < 0) start = target_value.size() + start;
-            if (end < 0) end = target_value.size() + end;
             auto result = Value::array();
-            for (auto i = start; i < end; ++i) {
+            for (int64_t i = start; step > 0 ? i < end : i > end; i += step) {
               result.push_back(target_value.at(i));
             }
             return result;
@@ -1305,6 +1323,8 @@ public:
               if (name == "iterable") return l.is_iterable();
               if (name == "sequence") return l.is_array();
               if (name == "defined") return !l.is_null();
+              if (name == "true") return l.to_bool();
+              if (name == "false") return !l.to_bool();
               throw std::runtime_error("Unknown type for 'is' operator: " + name);
             };
             auto value = eval();
@@ -1520,6 +1540,10 @@ public:
             vargs.expectArgs("endswith method", {1, 1}, {0, 0});
             auto suffix = vargs.args[0].get<std::string>();
             return suffix.length() <= str.length() && std::equal(suffix.rbegin(), suffix.rend(), str.rbegin());
+          } else if (method->get_name() == "startswith") {
+            vargs.expectArgs("startswith method", {1, 1}, {0, 0});
+            auto prefix = vargs.args[0].get<std::string>();
+            return prefix.length() <= str.length() && std::equal(prefix.begin(), prefix.end(), str.begin());
           } else if (method->get_name() == "title") {
             vargs.expectArgs("title method", {0, 0}, {0, 0});
             auto res = str;
@@ -2082,28 +2106,37 @@ private:
 
       while (it != end && consumeSpaces() && peekSymbols({ "[", "." })) {
         if (!consumeToken("[").empty()) {
-            std::shared_ptr<Expression> index;
+          std::shared_ptr<Expression> index;
+          auto slice_loc = get_location();
+          std::shared_ptr<Expression> start, end, step;
+          bool has_first_colon = false, has_second_colon = false;
+
+          if (!peekSymbols({ ":" })) {
+            start = parseExpression();
+          }
+
+          if (!consumeToken(":").empty()) {
+            has_first_colon = true;
+            if (!peekSymbols({ ":", "]" })) {
+              end = parseExpression();
+            }
             if (!consumeToken(":").empty()) {
-              auto slice_end = parseExpression();
-              index = std::make_shared<SliceExpr>(slice_end->location, nullptr, std::move(slice_end));
-            } else {
-              auto slice_start = parseExpression();
-              if (!consumeToken(":").empty()) {
-                consumeSpaces();
-                if (peekSymbols({ "]" })) {
-                  index = std::make_shared<SliceExpr>(slice_start->location, std::move(slice_start), nullptr);
-                } else {
-                  auto slice_end = parseExpression();
-                  index = std::make_shared<SliceExpr>(slice_start->location, std::move(slice_start), std::move(slice_end));
-                }
-              } else {
-                index = std::move(slice_start);
+              has_second_colon = true;
+              if (!peekSymbols({ "]" })) {
+                step = parseExpression();
               }
             }
-            if (!index) throw std::runtime_error("Empty index in subscript");
-            if (consumeToken("]").empty()) throw std::runtime_error("Expected closing bracket in subscript");
+          }
 
-            value = std::make_shared<SubscriptExpr>(value->location, std::move(value), std::move(index));
+          if ((has_first_colon || has_second_colon) && (start || end || step)) {
+            index = std::make_shared<SliceExpr>(slice_loc, std::move(start), std::move(end), std::move(step));
+          } else {
+            index = std::move(start);
+          }
+          if (!index) throw std::runtime_error("Empty index in subscript");
+          if (consumeToken("]").empty()) throw std::runtime_error("Expected closing bracket in subscript");
+
+          value = std::make_shared<SubscriptExpr>(value->location, std::move(value), std::move(index));
         } else if (!consumeToken(".").empty()) {
             auto identifier = parseIdentifier();
             if (!identifier) throw std::runtime_error("Expected identifier in subscript");

common/regex-partial.cpp

@@ -0,0 +1,204 @@
+#include "regex-partial.h"
+#include "common.h"
+#include <functional>
+#include <optional>
+
+common_regex::common_regex(const std::string & pattern) :
+    pattern(pattern),
+    rx(pattern),
+    rx_reversed_partial(regex_to_reversed_partial_regex(pattern)) {}
+
+common_regex_match common_regex::search(const std::string & input, size_t pos, bool as_match) const {
+    std::smatch match;
+    if (pos > input.size()) {
+        throw std::runtime_error("Position out of bounds");
+    }
+    auto start = input.begin() + pos;
+    auto found = as_match
+        ? std::regex_match(start, input.end(), match, rx)
+        : std::regex_search(start, input.end(), match, rx);
+    if (found) {
+        common_regex_match res;
+        res.type = COMMON_REGEX_MATCH_TYPE_FULL;
+        for (size_t i = 0; i < match.size(); ++i) {
+            auto begin = pos + match.position(i);
+            res.groups.emplace_back(begin, begin + match.length(i));
+        }
+        return res;
+    }
+    std::match_results<std::string::const_reverse_iterator> srmatch;
+    if (std::regex_match(input.rbegin(), input.rend() - pos, srmatch, rx_reversed_partial)) {
+        auto group = srmatch[1].str();
+        if (group.length() != 0) {
+            auto it = srmatch[1].second.base();
+            // auto position = static_cast<size_t>(std::distance(input.begin(), it));
+            if ((!as_match) || it == input.begin()) {
+                common_regex_match res;
+                res.type = COMMON_REGEX_MATCH_TYPE_PARTIAL;
+                const size_t begin = std::distance(input.begin(), it);
+                const size_t end = input.size();
+                if (begin == std::string::npos || end == std::string::npos || begin > end) {
+                    throw std::runtime_error("Invalid range");
+                }
+                res.groups.push_back({begin, end});
+                return res;
+            }
+        }
+    }
+    return {};
+}
+
+/*
+  Transforms a regex pattern to a partial match pattern that operates on a reversed input string to find partial final matches of the original pattern.
+
+  Ideally we'd like to use boost::match_partial (https://beta.boost.org/doc/libs/1_59_0/libs/regex/doc/html/boost_regex/partial_matches.html)
+  to see if a string ends with a partial regex match, but but it's not in std::regex yet.
+  Instead, we'll the regex into a partial match regex operating as a full match on the reverse iterators of the input.
+
+  - /abcd/ -> (dcba|cba|ba|a).* -> ((?:(?:(?:(?:d)?c)?b)?a).*
+  - /a|b/ -> (a|b).*
+  - /a*?/ -> error, could match ""
+  - /a*b/ -> ((?:b)?a*+).* (final repetitions become eager)
+  - /.*?ab/ -> ((?:b)?a).* (merge .*)
+  - /a.*?b/ -> ((?:b)?.*?a).* (keep reluctant matches)
+  - /a(bc)d/ -> ((?:(?:d)?(?:(?:c)?b))?a).*
+  - /a(bc|de)/ -> ((?:(?:(?:e)?d)?|(?:(?:c)?b)?)?a).*
+  - /ab{2,4}c/ -> abbb?b?c -> ((?:(?:(?:(?:(?:c)?b)?b)?b?)?b?)?a).*
+
+  The regex will match a reversed string fully, and the end of the first (And only) capturing group will indicate the reversed start of the original partial pattern
+  (i.e. just where the final .* starts in the inverted pattern; all other groups are turned into non-capturing groups, and reluctant quantifiers are ignored)
+*/
+std::string regex_to_reversed_partial_regex(const std::string & pattern) {
+    auto it = pattern.begin();
+    const auto end = pattern.end();
+
+    std::function<std::string()> process = [&]() {
+        std::vector<std::vector<std::string>> alternatives(1);
+        std::vector<std::string> * sequence = &alternatives.back();
+
+        while (it != end) {
+            if (*it == '[') {
+                auto start = it;
+                ++it;
+                while (it != end) {
+                    if ((*it == '\\') && (++it != end)) {
+                        ++it;
+                    } else if ((it != end) && (*it == ']')) {
+                        break;
+                    } else {
+                        ++it;
+                    }
+                }
+                if (it == end) {
+                    throw std::runtime_error("Unmatched '[' in pattern");
+                }
+                ++it;
+                sequence->push_back(std::string(start, it));
+            } else if (*it == '*' || *it == '?' || *it == '+') {
+                if (sequence->empty()) {
+                    throw std::runtime_error("Quantifier without preceding element");
+                }
+                sequence->back() += *it;
+                auto is_star = *it == '*';
+                ++it;
+                if (is_star) {
+                    if (*it == '?') {
+                        ++it;
+                    }
+                }
+            } else if (*it == '{') {
+                if (sequence->empty()) {
+                    throw std::runtime_error("Repetition without preceding element");
+                }
+                ++it;
+                auto start = it;
+                while (it != end && *it != '}') {
+                    ++it;
+                }
+                if (it == end) {
+                    throw std::runtime_error("Unmatched '{' in pattern");
+                }
+                auto parts = string_split(std::string(start, it), ",");
+                ++it;
+                if (parts.size() > 2) {
+                    throw std::runtime_error("Invalid repetition range in pattern");
+                }
+
+                auto parseOptInt = [&](const std::string & s, const std::optional<int> & def = std::nullopt) -> std::optional<int> {
+                    if (s.empty()) {
+                        return def;
+                    }
+                    return std::stoi(s);
+                };
+                auto min = parseOptInt(parts[0], 0);
+                auto max = parts.size() == 1 ? min : parseOptInt(parts[1]);
+                if (min && max && *max < *min) {
+                    throw std::runtime_error("Invalid repetition range in pattern");
+                }
+                // Brutal but... let's repeat at least min times, then ? for the delta between min & max (or * for unbounded)
+                auto part = sequence->back();
+                sequence->pop_back();
+                for (int i = 0; i < *min; i++) {
+                    sequence->push_back(part);
+                }
+                if (max) {
+                    for (int i = *min; i < *max; i++) {
+                        sequence->push_back(part + "?");
+                    }
+                } else {
+                    sequence->push_back(part + "*");
+                }
+            } else if (*it == '(') {
+                ++it;
+                if (it != end && *it == '?' && (it + 1 != end) && *(it + 1) == ':') {
+                    it += 2;
+                }
+                auto sub = process();
+                if (*it != ')') {
+                    throw std::runtime_error("Unmatched '(' in pattern");
+                }
+                ++it;
+                auto & part = sequence->emplace_back("(?:");
+                part += sub;
+                part += ")";
+            } else if (*it == ')') {
+                break;
+            } else if (*it == '|') {
+                ++it;
+                alternatives.emplace_back();
+                sequence = &alternatives.back();
+            } else if (*it == '\\' && (++it != end)) {
+                auto str = std::string("\\") + *it;
+                sequence->push_back(str);
+                ++it;
+            } else if (it != end) {
+                sequence->push_back(std::string(1, *it));
+                ++it;
+            }
+        }
+
+        // /abcd/ -> (dcba|cba|ba|a).* -> ((?:(?:(?:d)?c)?b)?a).*
+        // if n(=4) parts, opening n-1(=3) non-capturing groups after the 1 capturing group
+        // We'll do the outermost capturing group and final .* in the enclosing function.
+        std::vector<std::string> res_alts;
+        for (const auto & parts : alternatives) {
+            auto & res = res_alts.emplace_back();
+            for (size_t i = 0; i < parts.size() - 1; i++) {
+                res += "(?:";
+            }
+            for (auto it = parts.rbegin(); it != parts.rend(); ++it) {
+                res += *it;
+                if (it != parts.rend() - 1) {
+                    res += ")?";
+                }
+            }
+        }
+        return string_join(res_alts, "|");
+    };
+    auto res = process();
+    if (it != end) {
+        throw std::runtime_error("Unmatched '(' in pattern");
+    }
+
+    return "(" + res + ")[\\s\\S]*";
+}

common/regex-partial.h

@@ -0,0 +1,56 @@
+#pragma once
+
+#include <regex>
+#include <string>
+
+enum common_regex_match_type {
+    COMMON_REGEX_MATCH_TYPE_NONE,
+    COMMON_REGEX_MATCH_TYPE_PARTIAL,
+    COMMON_REGEX_MATCH_TYPE_FULL,
+};
+
+struct common_string_range {
+    size_t begin;
+    size_t end;
+    common_string_range(size_t begin, size_t end) : begin(begin), end(end) {
+        if (begin > end) {
+            throw std::runtime_error("Invalid range");
+        }
+    }
+    // prevent default ctor
+    common_string_range() = delete;
+    bool empty() const {
+        return begin == end;
+    }
+    bool operator==(const common_string_range & other) const {
+        return begin == other.begin && end == other.end;
+    }
+};
+
+struct common_regex_match {
+    common_regex_match_type type = COMMON_REGEX_MATCH_TYPE_NONE;
+    std::vector<common_string_range> groups;
+
+    bool operator==(const common_regex_match & other) const {
+        return type == other.type && groups == other.groups;
+    }
+    bool operator!=(const common_regex_match & other) const {
+        return !(*this == other);
+    }
+};
+
+class common_regex {
+    std::string pattern;
+    std::regex rx;
+    std::regex rx_reversed_partial;
+
+  public:
+    explicit common_regex(const std::string & pattern);
+
+    common_regex_match search(const std::string & input, size_t pos, bool as_match = false) const;
+
+    const std::string & str() const { return pattern; }
+};
+
+// For testing only (pretty print of failures).
+std::string regex_to_reversed_partial_regex(const std::string & pattern);

common/sampling.cpp

@@ -1,6 +1,7 @@
 #include "sampling.h"
 
 #include "common.h"
+#include "log.h"
 
 #include <cmath>
 #include <unordered_map>
@@ -229,51 +230,48 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                 params.logit_bias.data()));
 
     if (params.mirostat == 0) {
-        if (params.top_n_sigma >= 0) {
-            llama_sampler_chain_add(result->chain, llama_sampler_init_top_k        (params.top_k));
-            llama_sampler_chain_add(result->chain, llama_sampler_init_temp         (params.temp));
-            llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma  (params.top_n_sigma));
-        } else {
-            for (const auto & cnstr : params.samplers) {
-                switch (cnstr) {
-                    case COMMON_SAMPLER_TYPE_DRY:
-                        {
-                            std::vector<const char *> c_breakers;
-                            c_breakers.reserve(params.dry_sequence_breakers.size());
-                            for (const auto & str : params.dry_sequence_breakers) {
-                                c_breakers.push_back(str.c_str());
-                            }
-
-                            llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
+        for (const auto & cnstr : params.samplers) {
+            switch (cnstr) {
+                case COMMON_SAMPLER_TYPE_DRY:
+                    {
+                        std::vector<const char *> c_breakers;
+                        c_breakers.reserve(params.dry_sequence_breakers.size());
+                        for (const auto & str : params.dry_sequence_breakers) {
+                            c_breakers.push_back(str.c_str());
                         }
-                        break;
-                    case COMMON_SAMPLER_TYPE_TOP_K:
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_top_k    (params.top_k));
-                        break;
-                    case COMMON_SAMPLER_TYPE_TOP_P:
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_top_p    (params.top_p, params.min_keep));
-                        break;
-                    case COMMON_SAMPLER_TYPE_MIN_P:
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_min_p    (params.min_p, params.min_keep));
-                        break;
-                    case COMMON_SAMPLER_TYPE_XTC:
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_xtc      (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
-                        break;
-                    case COMMON_SAMPLER_TYPE_TYPICAL_P:
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_typical  (params.typ_p, params.min_keep));
-                        break;
-                    case COMMON_SAMPLER_TYPE_TEMPERATURE:
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent));
-                        break;
-                    case COMMON_SAMPLER_TYPE_INFILL:
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_infill   (vocab));
-                        break;
-                    case COMMON_SAMPLER_TYPE_PENALTIES:
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
-                        break;
-                    default:
-                        GGML_ASSERT(false && "unknown sampler type");
-                }
+
+                        llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
+                    }
+                    break;
+                case COMMON_SAMPLER_TYPE_TOP_K:
+                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_k       (params.top_k));
+                    break;
+                case COMMON_SAMPLER_TYPE_TOP_P:
+                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_p       (params.top_p, params.min_keep));
+                    break;
+                case COMMON_SAMPLER_TYPE_TOP_N_SIGMA:
+                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma (params.top_n_sigma));
+                    break;
+                case COMMON_SAMPLER_TYPE_MIN_P:
+                    llama_sampler_chain_add(result->chain, llama_sampler_init_min_p       (params.min_p, params.min_keep));
+                    break;
+                case COMMON_SAMPLER_TYPE_XTC:
+                    llama_sampler_chain_add(result->chain, llama_sampler_init_xtc         (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
+                    break;
+                case COMMON_SAMPLER_TYPE_TYPICAL_P:
+                    llama_sampler_chain_add(result->chain, llama_sampler_init_typical     (params.typ_p, params.min_keep));
+                    break;
+                case COMMON_SAMPLER_TYPE_TEMPERATURE:
+                    llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext    (params.temp, params.dynatemp_range, params.dynatemp_exponent));
+                    break;
+                case COMMON_SAMPLER_TYPE_INFILL:
+                    llama_sampler_chain_add(result->chain, llama_sampler_init_infill      (vocab));
+                    break;
+                case COMMON_SAMPLER_TYPE_PENALTIES:
+                    llama_sampler_chain_add(result->chain, llama_sampler_init_penalties   (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    break;
+                default:
+                    GGML_ASSERT(false && "unknown sampler type");
             }
         }
         llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
@@ -475,6 +473,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TOP_K:       return 'k';
         case COMMON_SAMPLER_TYPE_TYPICAL_P:   return 'y';
         case COMMON_SAMPLER_TYPE_TOP_P:       return 'p';
+        case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: return 's';
         case COMMON_SAMPLER_TYPE_MIN_P:       return 'm';
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
         case COMMON_SAMPLER_TYPE_XTC:         return 'x';
@@ -490,6 +489,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TOP_K:       return "top_k";
         case COMMON_SAMPLER_TYPE_TYPICAL_P:   return "typ_p";
         case COMMON_SAMPLER_TYPE_TOP_P:       return "top_p";
+        case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: return "top_n_sigma";
         case COMMON_SAMPLER_TYPE_MIN_P:       return "min_p";
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
         case COMMON_SAMPLER_TYPE_XTC:         return "xtc";
@@ -504,6 +504,7 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         { "dry",         COMMON_SAMPLER_TYPE_DRY },
         { "top_k",       COMMON_SAMPLER_TYPE_TOP_K },
         { "top_p",       COMMON_SAMPLER_TYPE_TOP_P },
+        { "top_n_sigma", COMMON_SAMPLER_TYPE_TOP_N_SIGMA },
         { "typ_p",       COMMON_SAMPLER_TYPE_TYPICAL_P },
         { "min_p",       COMMON_SAMPLER_TYPE_MIN_P },
         { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
@@ -517,6 +518,7 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
     std::unordered_map<std::string, common_sampler_type> sampler_alt_name_map {
         { "top-k",       COMMON_SAMPLER_TYPE_TOP_K },
         { "top-p",       COMMON_SAMPLER_TYPE_TOP_P },
+        { "top-n-sigma", COMMON_SAMPLER_TYPE_TOP_N_SIGMA },
         { "nucleus",     COMMON_SAMPLER_TYPE_TOP_P },
         { "typical-p",   COMMON_SAMPLER_TYPE_TYPICAL_P },
         { "typical",     COMMON_SAMPLER_TYPE_TYPICAL_P },
@@ -533,14 +535,16 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         auto sampler = sampler_canonical_name_map.find(name);
         if (sampler != sampler_canonical_name_map.end()) {
             samplers.push_back(sampler->second);
-        } else {
-            if (allow_alt_names) {
-                sampler = sampler_alt_name_map.find(name);
-                if (sampler != sampler_alt_name_map.end()) {
-                    samplers.push_back(sampler->second);
-                }
+            continue;
+        }
+        if (allow_alt_names) {
+            sampler = sampler_alt_name_map.find(name);
+            if (sampler != sampler_alt_name_map.end()) {
+                samplers.push_back(sampler->second);
+                continue;
             }
         }
+        LOG_WRN("%s: unable to match sampler by name '%s'\n", __func__, name.c_str());
     }
 
     return samplers;
@@ -552,6 +556,7 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K),       COMMON_SAMPLER_TYPE_TOP_K },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P),   COMMON_SAMPLER_TYPE_TYPICAL_P },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P),       COMMON_SAMPLER_TYPE_TOP_P },
+        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_N_SIGMA), COMMON_SAMPLER_TYPE_TOP_N_SIGMA },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P),       COMMON_SAMPLER_TYPE_MIN_P },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC),         COMMON_SAMPLER_TYPE_XTC },
@@ -566,6 +571,8 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
         const auto sampler = sampler_name_map.find(c);
         if (sampler != sampler_name_map.end()) {
             samplers.push_back(sampler->second);
+        } else {
+            LOG_WRN("%s: unable to match sampler by char '%c'\n", __func__, c);
         }
     }
 

convert_hf_to_gguf_update.py

@@ -115,6 +115,8 @@ models = [
     {"name": "bailingmoe",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-lite", },
     {"name": "llama4",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", },
     {"name": "glm4",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", },
+    {"name": "pixtral",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistral-community/pixtral-12b", },
+    {"name": "seed-coder",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base", },
 ]
 
 

convert_lora_to_gguf.py

@@ -24,7 +24,7 @@ if 'NO_LOCAL_GGUF' not in os.environ:
 import gguf
 
 # reuse model definitions from convert_hf_to_gguf.py
-from convert_hf_to_gguf import LazyTorchTensor, Model
+from convert_hf_to_gguf import LazyTorchTensor, ModelBase
 
 logger = logging.getLogger("lora-to-gguf")
 
@@ -340,11 +340,11 @@ if __name__ == '__main__':
             sys.exit(1)
     else:
         logger.info(f"Loading base model: {dir_base_model.name}")
-        hparams = Model.load_hparams(dir_base_model)
+        hparams = ModelBase.load_hparams(dir_base_model)
 
     with torch.inference_mode():
         try:
-            model_class = Model.from_model_architecture(hparams["architectures"][0])
+            model_class = ModelBase.from_model_architecture(hparams["architectures"][0])
         except NotImplementedError:
             logger.error(f"Model {hparams['architectures'][0]} is not supported")
             sys.exit(1)

docs/backend/CANN.md

@@ -56,60 +56,82 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi
 
 ## Model Supports
 
-| Model Name                  | FP16  | Q8_0 | Q4_0 |
+| Model Name                  | FP16  | Q4_0 | Q8_0 |
 |:----------------------------|:-----:|:----:|:----:|
-| AquilaChat2-7B              |   √   |   √  |   √  |
-| Baichuan-7b                 |   √   |   √  |   √  |
-| Baichuan2-7B-Chat           |   √   |   √  |   √  |
-| bitnet_b1_58-large          |   √   |   √  |   √  |
-| bloom-560m                  |   √   |   x  |   √  |
-| bloomz-alpaca-560m          |   √   |   x  |   √  |
-| c4ai-command-r-35B-v01      |   x   |   x  |   x  |
-| chatglm3-6B                 |   x   |   x  |   x  |
-| chinese-alpaca-2-1.3b       |   √   |   √  |   √  |
-| CodeShell-7B                |   √   |   √  |   √  |
-| deepseek-ai_deepseek-coder-1.3B-base | x |   x  |   x  |
-| deepseek-ai_DeepSeek-V2-Lite | x   |   x  |   x   |
-| deepseek-coder-6.7B-instruct | x   |   x  |   x   |
-| DeepSeek-V2-Lite-64x1.5B    |   x   |   x  |   x  |
-| falcon-7b-instruct          |   √   |   √  |   √  |
-| flan-t5-large               |   √   |   √  |   √  |
-| gemma-2-9b-it               |   √   |   √  |   √  |
-| glm-4-9B                    |   x   |   x  |   x  |
-| gpt2                        |   √   |   √  |   √  |
-| Gpt2-163M                   |   √   |   √  |   √  |
-| granite-3B-code-instruct    |   √   |   √  |   √  |
+| Llama-2                     |   √   |   √  |   √  |
+| Llama-3                     |   √   |   √  |   √  |
+| Mistral-7B                  |   √   |   √  |   √  |
+| Mistral MOE                 |   √   |   √  |   √  |
+| DBRX                        |   -   |   -  |   -  |
+| Falcon                      |   √   |   √  |   √  |
+| Chinese LLaMA/Alpaca        |   √   |   √  |   √  |
+| Vigogne(French)             |   √   |   √  |   √  |
+| BERT                        |   x   |   x  |   x  |
+| Koala                       |   √   |   √  |   √  |
+| Baichuan                    |   √   |   √  |   √  |
+| Aquila 1 & 2                |   √   |   √  |   √  |
+| Starcoder models            |   √   |   √  |   √  |
+| Refact                      |   √   |   √  |   √  |
+| MPT                         |   √   |   √  |   √  |
+| Bloom                       |   √   |   √  |   √  |
+| Yi models                   |   √   |   √  |   √  |
+| stablelm models             |   √   |   √  |   √  |
+| DeepSeek models             |   x   |   x  |   x  |
+| Qwen models                 |   √   |   √  |   √  |
+| PLaMo-13B                   |   √   |   √  |   √  |
+| Phi models                  |   √   |   √  |   √  |
+| PhiMoE                      |   √   |   √  |   √  |
+| GPT-2                       |   √   |   √  |   √  |
+| Orion                       |   √   |   √  |   √  |
+| InternlLM2                  |   √   |   √  |   √  |
+| CodeShell                   |   √   |   √  |   √  |
+| Gemma                       |   √   |   √  |   √  |
+| Mamba                       |   √   |   √  |   √  |
+| Xverse                      |   √   |   √  |   √  |
+| command-r models            |   √   |   √  |   √  |
+| Grok-1                      |   -   |   -  |   -  |
+| SEA-LION                    |   √   |   √  |   √  |
 | GritLM-7B                   |   √   |   √  |   √  |
-| internlm2_5-7b-chat         |   √   |   √  |   √  |
-| koala-7B-HF                 |   √   |   √  |   √  |
-| Llama-2-7b-chat-hf          |   √   |   √  |   √  |
-| Llama-3-Smaug-8B            |   √   |   √  |   √  |
-| Llama2-Chinese-7b-Chat      |   √   |   √  |   √  |
-| Llama3-8B                   |   √   |   √  |   √  |
-| Llama3-8b-chinese           |   √   |   √  |   √  |
-| mamba-130m-hf               |   √   |   √  |   √  |
-| Mistral-7B-Instruct-v0.2    |   √   |   √  |   √  |
-| Mixtral-8x7B-Instruct-v0.1  |   x   |   √  |   √  |
-| mpt-7B                      |   √   |   √  |   √  |
-| OLMo-1B-hf                  |   √   |   √  |   √  |
-| OpenELM-3B-Instruct         |   √   |   √  |   √  |
-| Orion-14b-base              |   √   |   √  |   √  |
-| phi1                        |   x   |   x  |   x  |
-| phi2                        |   x   |   x  |   x  |
-| Phi-3-mini-4k-instruct      |   √   |   √  |   √  |
-| plamo-13b                   |   √   |   √  |   √  |
-| pythia-70M                  |   x   |   x  |   x  |
-| Qwen-7B                     |   √   |   √  |   √  |
-| Qwen2-1.5B-Instruct         |   √   |   x  |   √  |
-| Refact-1_6B-fim             |   √   |   √  |   √  |
-| SmolLM-135M                 |   √   |   √  |   √  |
-| stablelm-zephyr             |   x   |   x  |   x  |
-| stablelm-2-zephyr-1_6b      |   x   |   x  |   x  |
-| starcoderbase-1b            |   √   |   √  |   √  |
-| starcoder2-3b               |   √   |   √  |   √  |
-| vigogne-7b-chat             |   √   |   √  |   √  |
-| xverse-7b-chat              |   √   |   √  |   √  |
-| Yi-6b-Chat                  |   √   |   √  |   √  |
+| OLMo                        |   √   |   √  |   √  |
+| OLMo 2                      |   √   |   √  |   √  |
+| OLMoE                       |   √   |   √  |   √  |
+| Granite models              |   √   |   √  |   √  |
+| GPT-NeoX                    |   √   |   √  |   √  |
+| Pythia                      |   √   |   √  |   √  |
+| Snowflake-Arctic MoE        |   -   |   -  |   -  |
+| Smaug                       |   √   |   √  |   √  |
+| Poro 34B                    |   √   |   √  |   √  |
+| Bitnet b1.58 models         |   √   |   x  |   x  |
+| Flan-T5                     |   √   |   √  |   √  |
+| Open Elm models             |   x   |   √  |   √  |
+| chatGLM3-6B + ChatGLM4-9b +  GLMEdge-1.5b + GLMEdge-4b    |   √   |   √  |   √  |
+| GLM-4-0414                  |   √   |   √  |   √  |
+| SmolLM                      |   √   |   √  |   √  |
+| EXAONE-3.0-7.8B-Instruct    |   √   |   √  |   √  |
+| FalconMamba Models          |   √   |   √  |   √  |
+| Jais Models                 |   -   |   x  |   x  |
+| Bielik-11B-v2.3             |   √   |   √  |   √  |
+| RWKV-6                      |   -   |   √  |   √  |
+| QRWKV-6                     |   √   |   √  |   √  |
+| GigaChat-20B-A3B            |   x   |   x  |   x  |
+| Trillion-7B-preview         |   √   |   √  |   √  |
+| Ling models                 |   √   |   √  |   √  |
+
+
+**Multimodal**
+| Model Name                  | FP16  | Q4_0 | Q8_0 |
+|:----------------------------|:-----:|:----:|:----:|
+| LLaVA 1.5 models, LLaVA 1.6 models      |   x   |   x  |   x  |
+|  BakLLaVA                   |   √   |   √  |   √  |
+|  Obsidian                   |   √   |   -  |   -  |
+|  ShareGPT4V                 |   x   |   -  |   -  |
+|  MobileVLM 1.7B/3B models   |   -   |   -  |   -  |
+|  Yi-VL                      |   -   |   -  |   -  |
+|  Mini CPM                   |   √   |   √  |   √  |
+|  Moondream                  |   √   |   √  |   √  |
+|  Bunny                      |   √   |   -  |   -  |
+|  GLM-EDGE                   |   √   |   √  |   √  |
+|  Qwen2-VL                   |   √   |   √  |   √  |
 
 
 

docs/backend/SYCL.md

@@ -17,25 +17,25 @@
 
 **SYCL** is a high-level parallel programming model designed to improve developers productivity writing code across various hardware accelerators such as CPUs, GPUs, and FPGAs. It is a single-source language designed for heterogeneous computing and based on standard C++17.
 
-**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:
+**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to Intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:
 
 - **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
 - **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. Intel oneMKL, oneMath and oneDNN)*.
-- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over intel iGPUs and dGPUs.
+- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over Intel iGPUs and dGPUs.
 - **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.
 
 ### Llama.cpp + SYCL
 
-The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based on the cross-platform feature of SYCL, it also supports other vendor GPUs: Nvidia and AMD.
+The llama.cpp SYCL backend is primarily designed for **Intel GPUs**.
+SYCL cross-platform capabilities enable support for Nvidia GPUs as well, with limited support for AMD.
 
 ## Recommended Release
 
-The SYCL backend would be broken by some PRs due to no online CI.
-
-The following release is verified with good quality:
+The following releases are verified and recommended:
 
 |Commit ID|Tag|Release|Verified  Platform| Update date|
 |-|-|-|-|-|
+|24e86cae7219b0f3ede1d5abdf5bf3ad515cccb8|b5377 |[llama-b5377-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b5377/llama-b5377-bin-win-sycl-x64.zip) |ArcB580/Linux/oneAPI 2025.1<br>LNL Arc GPU/Windows 11/oneAPI 2025.1.1|2025-05-15|
 |3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19|
 |fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1||
 
@@ -106,15 +106,14 @@ SYCL backend supports Intel GPU Family:
 |-------------------------------|---------|---------------------------------------|
 | Intel Data Center Max Series  | Support | Max 1550, 1100                        |
 | Intel Data Center Flex Series | Support | Flex 170                              |
-| Intel Arc Series              | Support | Arc 770, 730M, Arc A750               |
-| Intel built-in Arc GPU        | Support | built-in Arc GPU in Meteor Lake, Arrow Lake    |
-| Intel iGPU                    | Support | iGPU in 13700k,iGPU in 13400, i5-1250P, i7-1260P, i7-1165G7 |
+| Intel Arc Series              | Support | Arc 770, 730M, Arc A750, B580         |
+| Intel built-in Arc GPU        | Support | built-in Arc GPU in Meteor Lake, Arrow Lake, Lunar Lake |
+| Intel iGPU                    | Support | iGPU in 13700k, 13400, i5-1250P, i7-1260P, i7-1165G7  |
 
 *Notes:*
 
 - **Memory**
   - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/llama-cli`.
-
   - Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU.
 
 - **Execution Unit (EU)**
@@ -138,9 +137,11 @@ Note: AMD GPU support is highly experimental and is incompatible with F16.
 Additionally, it only supports GPUs with a sub_group_size (warp size) of 32.
 
 ## Docker
-The docker build option is currently limited to *intel GPU* targets.
+
+The docker build option is currently limited to *Intel GPU* targets.
 
 ### Build image
+
 ```sh
 # Using FP16
 docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile .
@@ -148,9 +149,10 @@ docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f
 
 *Notes*:
 
-To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="GGML_SYCL_F16=ON"` argument from the previous command.
+To build in default FP32 *(Slower than FP16 alternative)*, set `--build-arg="GGML_SYCL_F16=OFF"` in the previous command.
 
 You can also use the `.devops/llama-server-intel.Dockerfile`, which builds the *"server"* alternative.
+Check the [documentation for Docker](../docker.md) to see the available images.
 
 ### Run container
 
@@ -250,7 +252,7 @@ sycl-ls
 
 - **Intel GPU**
 
-When targeting an intel GPU, the user should expect one or more level-zero devices among the available SYCL devices. Please make sure that at least one GPU is present, for instance [`level_zero:gpu`] in the sample output below:
+When targeting an intel GPU, the user should expect one or more devices among the available SYCL devices. Please make sure that at least one GPU is present via `sycl-ls`, for instance `[level_zero:gpu]` in the sample output below:
 
 ```
 [opencl:acc][opencl:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.10.0.17_160000]
@@ -282,7 +284,7 @@ For AMD GPUs we should expect at least one SYCL-HIP device [`hip:gpu`]:
 
 #### Intel GPU
 
-```
+```sh
 ./examples/sycl/build.sh
 ```
 
@@ -351,7 +353,7 @@ cmake --build build --config Release -j -v
 
 #### Retrieve and prepare model
 
-You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
+You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model preparation, or download an already quantized model like [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) or [Meta-Llama-3-8B-Instruct-Q4_0.gguf](https://huggingface.co/aptha/Meta-Llama-3-8B-Instruct-Q4_0-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_0.gguf).
 
 ##### Check device
 
@@ -398,11 +400,15 @@ Choose one of following methods to run.
 
 ```sh
 ./examples/sycl/run-llama2.sh 0
+# OR
+./examples/sycl/run-llama3.sh 0
 ```
 - Use multiple devices:
 
 ```sh
 ./examples/sycl/run-llama2.sh
+# OR
+./examples/sycl/run-llama3.sh
 ```
 
 2. Command line
@@ -425,13 +431,13 @@ Examples:
 - Use device 0:
 
 ```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm none -mg 0
 ```
 
 - Use multiple devices:
 
 ```sh
-ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer
+ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm layer
 ```
 
 *Notes:*
@@ -452,7 +458,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 
 1. Install GPU driver
 
-Intel GPU drivers instructions guide and download page can be found here: [Get intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
+Intel GPU drivers instructions guide and download page can be found here: [Get Intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
 
 2. Install Visual Studio
 
@@ -629,7 +635,7 @@ Once it is completed, final results will be in **build/Release/bin**
 
 #### Retrieve and prepare model
 
-You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model prepration, or simply download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) model as example.
+You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model preparation, or download an already quantized model like [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) or [Meta-Llama-3-8B-Instruct-Q4_0.gguf](https://huggingface.co/aptha/Meta-Llama-3-8B-Instruct-Q4_0-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_0.gguf).
 
 ##### Check device
 
@@ -648,7 +654,7 @@ Similar to the native `sycl-ls`, available SYCL devices can be queried as follow
 build\bin\llama-ls-sycl-device.exe
 ```
 
-This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following:
+This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *Intel GPU* it would look like the following:
 ```
 found 2 SYCL devices:
 |  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |
@@ -658,13 +664,14 @@ found 2 SYCL devices:
 | 1|[level_zero:gpu:1]|                    Intel(R) UHD Graphics 770|       1.3|         32|     512|     32|    53651849216|
 
 ```
+
 #### Choose level-zero devices
 
 |Chosen Device ID|Setting|
 |-|-|
-|0|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action|
+|0|Default option. You may also want to `set ONEAPI_DEVICE_SELECTOR="level_zero:0"`|
 |1|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"`|
-|0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`|
+|0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"` or `set ONEAPI_DEVICE_SELECTOR="level_zero:*"`|
 
 #### Execute
 
@@ -673,7 +680,13 @@ Choose one of following methods to run.
 1. Script
 
 ```
-examples\sycl\win-run-llama2.bat
+examples\sycl\win-run-llama-2.bat
+```
+
+or
+
+```
+examples\sycl\win-run-llama-3.bat
 ```
 
 2. Command line
@@ -697,13 +710,13 @@ Examples:
 - Use device 0:
 
 ```
-build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0
+build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm none -mg 0
 ```
 
 - Use multiple devices:
 
 ```
-build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer
+build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm layer
 ```
 
 
@@ -714,7 +727,9 @@ Note:
 ```sh
 detect 1 SYCL GPUs: [0] with top Max compute units:512
 ```
+
 Or
+
 ```sh
 use 1 SYCL GPUs: [0] with Max compute units:512
 ```
@@ -726,14 +741,17 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 
 | Name               | Value                                 | Function                                    |
 |--------------------|---------------------------------------|---------------------------------------------|
-| GGML_SYCL          | ON (mandatory)                        | Enable build with SYCL code path.<br>FP32 path - recommended for better perforemance than FP16 on quantized model|
+| GGML_SYCL          | ON (mandatory)                        | Enable build with SYCL code path.           |
 | GGML_SYCL_TARGET   | INTEL *(default)* \| NVIDIA \| AMD    | Set the SYCL target device type.            |
 | GGML_SYCL_DEVICE_ARCH | Optional (except for AMD)             | Set the SYCL device architecture, optional except for AMD. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. |
-| GGML_SYCL_F16      | OFF *(default)* \|ON *(optional)*     | Enable FP16 build with SYCL code path.      |
+| GGML_SYCL_F16      | OFF *(default)* \|ON *(optional)*     | Enable FP16 build with SYCL code path. (1.) |
 | GGML_SYCL_GRAPH    | ON *(default)* \|OFF *(Optional)*     | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
+| GGML_SYCL_DNN      | ON *(default)* \|OFF *(Optional)*     | Enable build with oneDNN.                   |
 | CMAKE_C_COMPILER   | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path.      |
 | CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)*   | Set `icpx/icx` compiler for SYCL code path. |
 
+1. FP16 is recommended for better prompt processing performance on quantized models. Performance is equivalent in text generation but set `GGML_SYCL_F16=OFF` if you are experiencing issues with FP16 builds.
+
 #### Runtime
 
 | Name              | Value            | Function                                                                                                                  |
@@ -741,6 +759,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | GGML_SYCL_DEBUG   | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG                                                                             |
 | GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features based on Intel GPU type, to compare the performance increase |
 | GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because graph performance isn't yet better than non-graph performance. |
+| GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. |
 | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
 
 
@@ -750,7 +769,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 
 ## Q&A
 
-- Error:  `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.
+- Error:  `error while loading shared libraries: libsycl.so: cannot open shared object file: No such file or directory`.
 
   - Potential cause: Unavailable oneAPI installation or not set ENV variables.
   - Solution: Install *oneAPI base toolkit* and enable its ENV through: `source /opt/intel/oneapi/setvars.sh`.
@@ -779,18 +798,18 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 
   It's same for other projects including llama.cpp SYCL backend.
 
-- Meet issue: `Native API failed. Native API returns: -6 (PI_ERROR_OUT_OF_HOST_MEMORY) -6 (PI_ERROR_OUT_OF_HOST_MEMORY) -999 (UNKNOWN PI error)` or `failed to allocate SYCL0 buffer`
+- `Native API failed. Native API returns: 39 (UR_RESULT_ERROR_OUT_OF_DEVICE_MEMORY)`, `ggml_backend_sycl_buffer_type_alloc_buffer: can't allocate 3503030272 Bytes of memory on device`, or `failed to allocate SYCL0 buffer`
 
-  Device Memory is not enough.
+  You are running out of Device Memory.
 
   |Reason|Solution|
   |-|-|
-  |Default Context is too big. It leads to more memory usage.|Set `-c 8192` or smaller value.|
-  |Model is big and require more memory than device's.|Choose smaller quantized model, like Q5 -> Q4;<br>Use more than one devices to load model.|
+  | The default context is too big. It leads to excessive memory usage.|Set `-c 8192` or a smaller value.|
+  | The model is too big and requires more memory than what is available.|Choose a smaller model or change to a smaller quantization, like Q5 -> Q4;<br>Alternatively, use more than one device to load model.|
 
 ### **GitHub contribution**:
-Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay.
+Please add the `SYCL :` prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay.
 
 ## TODO
 
-- NA
+- Review ZES_ENABLE_SYSMAN: https://github.com/intel/compute-runtime/blob/master/programmers-guide/SYSMAN.md#support-and-limitations

docs/build.md

@@ -259,8 +259,6 @@ You can download it from your Linux distro's package manager or from here: [ROCm
       cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
       && cmake --build build --config Release -- -j 16
   ```
-  On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`.
-  However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
 
   To enhance flash attention performance on RDNA3+ or CDNA architectures, you can utilize the rocWMMA library by enabling the `-DGGML_HIP_ROCWMMA_FATTN=ON` option. This requires rocWMMA headers to be installed on the build system.
 
@@ -296,6 +294,10 @@ You can download it from your Linux distro's package manager or from here: [ROCm
 The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
 If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
 
+### Unified Memory
+
+On Linux it is possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1`. However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
+
 ## Vulkan
 
 **Windows**

docs/development/HOWTO-add-model.md

@@ -9,10 +9,10 @@ Adding a model requires few steps:
 After following these steps, you can open PR.
 
 Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially:
-- [main](/examples/main/)
-- [imatrix](/examples/imatrix/)
-- [quantize](/examples/quantize/)
-- [server](/examples/server/)
+- [main](/tools/main/)
+- [imatrix](/tools/imatrix/)
+- [quantize](/tools/quantize/)
+- [server](/tools/server/)
 
 ### 1. Convert the model to GGUF
 

docs/docker.md

@@ -22,6 +22,9 @@ Additionally, there the following images, similar to the above:
 - `ghcr.io/ggml-org/llama.cpp:full-musa`: Same as `full` but compiled with MUSA support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:light-musa`: Same as `light` but compiled with MUSA support. (platforms: `linux/amd64`)
 - `ghcr.io/ggml-org/llama.cpp:server-musa`: Same as `server` but compiled with MUSA support. (platforms: `linux/amd64`)
+- `ghcr.io/ggml-org/llama.cpp:full-intel`: Same as `full` but compiled with SYCL support. (platforms: `linux/amd64`)
+- `ghcr.io/ggml-org/llama.cpp:light-intel`: Same as `light` but compiled with SYCL support. (platforms: `linux/amd64`)
+- `ghcr.io/ggml-org/llama.cpp:server-intel`: Same as `server` but compiled with SYCL support. (platforms: `linux/amd64`)
 
 The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](../.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](../.github/workflows/docker.yml). If you need different settings (for example, a different CUDA, ROCm or MUSA library, you'll need to build the images locally for now).
 
@@ -104,7 +107,7 @@ You may want to pass in some different `ARGS`, depending on the MUSA environment
 
 The defaults are:
 
-- `MUSA_VERSION` set to `rc3.1.1`
+- `MUSA_VERSION` set to `rc4.0.1`
 
 The resulting images, are essentially the same as the non-MUSA images:
 

docs/multimodal.md

@@ -0,0 +1,80 @@
+# Multimodal
+
+llama.cpp supports multimodal input via `libmtmd`. Currently, there are 2 tools support this feature:
+- [llama-mtmd-cli](../tools/mtmd/README.md)
+- [llama-server](../tools/server/README.md) via OpenAI-compatible `/chat/completions` API
+
+To enable it, can use use one of the 2 methods below:
+
+- Use `-hf` option with a supported model (see a list of pre-quantized model below)
+    - To load a model using `-hf` while disabling multimodal, use `--no-mmproj`
+    - To load a model using `-hf` while using a custom mmproj file, use `--mmproj local_file.gguf`
+- Use `-m model.gguf` option with `--mmproj file.gguf` to specify text and multimodal projector respectively
+
+By default, multimodal projector will be offloaded to GPU. To disable this, add `--no-mmproj-offload`
+
+For example:
+
+```sh
+# simple usage with CLI
+llama-mtmd-cli -hf ggml-org/gemma-3-4b-it-GGUF
+
+# simple usage with server
+llama-server -hf ggml-org/gemma-3-4b-it-GGUF
+
+# using local file
+llama-server -m gemma-3-4b-it-Q4_K_M.gguf --mmproj mmproj-gemma-3-4b-it-Q4_K_M.gguf
+
+# no GPU offload
+llama-server -hf ggml-org/gemma-3-4b-it-GGUF --no-mmproj-offload
+```
+
+## Pre-quantized models
+
+These are ready-to-use models, most of them come with `Q4_K_M` quantization by default. They can be found at the Hugging Face page of the ggml-org: https://huggingface.co/ggml-org
+
+Replaces the `(tool_name)` with the name of binary you want to use. For example, `llama-mtmd-cli` or `llama-server`
+
+NOTE: some models may require large context window, for example: `-c 8192`
+
+```sh
+# Gemma 3
+(tool_name) -hf ggml-org/gemma-3-4b-it-GGUF
+(tool_name) -hf ggml-org/gemma-3-12b-it-GGUF
+(tool_name) -hf ggml-org/gemma-3-27b-it-GGUF
+
+# SmolVLM
+(tool_name) -hf ggml-org/SmolVLM-Instruct-GGUF
+(tool_name) -hf ggml-org/SmolVLM-256M-Instruct-GGUF
+(tool_name) -hf ggml-org/SmolVLM-500M-Instruct-GGUF
+(tool_name) -hf ggml-org/SmolVLM2-2.2B-Instruct-GGUF
+(tool_name) -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF
+(tool_name) -hf ggml-org/SmolVLM2-500M-Video-Instruct-GGUF
+
+# Pixtral 12B
+(tool_name) -hf ggml-org/pixtral-12b-GGUF
+
+# Qwen 2 VL
+(tool_name) -hf ggml-org/Qwen2-VL-2B-Instruct-GGUF
+(tool_name) -hf ggml-org/Qwen2-VL-7B-Instruct-GGUF
+
+# Qwen 2.5 VL
+(tool_name) -hf ggml-org/Qwen2.5-VL-3B-Instruct-GGUF
+(tool_name) -hf ggml-org/Qwen2.5-VL-7B-Instruct-GGUF
+(tool_name) -hf ggml-org/Qwen2.5-VL-32B-Instruct-GGUF
+(tool_name) -hf ggml-org/Qwen2.5-VL-72B-Instruct-GGUF
+
+# Mistral Small 3.1 24B (IQ2_M quantization)
+(tool_name) -hf ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF
+
+# InternVL 2.5 and 3
+(tool_name) -hf ggml-org/InternVL2_5-1B-GGUF
+(tool_name) -hf ggml-org/InternVL2_5-4B-GGUF
+(tool_name) -hf ggml-org/InternVL3-1B-Instruct-GGUF
+(tool_name) -hf ggml-org/InternVL3-2B-Instruct-GGUF
+(tool_name) -hf ggml-org/InternVL3-8B-Instruct-GGUF
+(tool_name) -hf ggml-org/InternVL3-14B-Instruct-GGUF
+
+# Llama 4 Scout
+(tool_name) -hf ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF
+```

docs/multimodal/MobileVLM.md

@@ -9,15 +9,15 @@ The implementation is based on llava, and is compatible with llava and mobileVLM
 Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using **MobileVLM-1.7B** as an example, the different conversion step will be shown.
 
 ## Usage
-Build with cmake or run `make llama-llava-cli` to build it.
 
-After building, run: `./llama-llava-cli` to see the usage. For example:
+Build the `llama-mtmd-cli` binary.
+
+After building, run: `./llama-mtmd-cli` to see the usage. For example:
 
 ```sh
-./llama-llava-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \
+./llama-mtmd-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \
     --mmproj MobileVLM-1.7B/mmproj-model-f16.gguf \
-    --image path/to/an/image.jpg \
-    -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? Answer the question using a single word or phrase. ASSISTANT:"
+    --chat-template deepseek
 ```
 
 ## Model conversion
@@ -33,13 +33,13 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336
 2. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
 
 ```sh
-python ./examples/llava/llava_surgery.py -m path/to/MobileVLM-1.7B
+python ./tools/mtmd/llava_surgery.py -m path/to/MobileVLM-1.7B
 ```
 
 3. Use `convert_image_encoder_to_gguf.py` with `--projector-type ldp` (for **V2** please use `--projector-type ldpv2`) to convert the LLaVA image encoder to GGUF:
 
 ```sh
-python ./examples/llava/convert_image_encoder_to_gguf.py \
+python ./tools/mtmd/convert_image_encoder_to_gguf.py \
     -m path/to/clip-vit-large-patch14-336 \
     --llava-projector path/to/MobileVLM-1.7B/llava.projector \
     --output-dir path/to/MobileVLM-1.7B \
@@ -47,7 +47,7 @@ python ./examples/llava/convert_image_encoder_to_gguf.py \
 ```
 
 ```sh
-python ./examples/llava/convert_image_encoder_to_gguf.py \
+python ./tools/mtmd/convert_image_encoder_to_gguf.py \
     -m path/to/clip-vit-large-patch14-336 \
     --llava-projector path/to/MobileVLM-1.7B_V2/llava.projector \
     --output-dir path/to/MobileVLM-1.7B_V2 \
@@ -69,10 +69,10 @@ Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directo
 
 ## Android compile and run
 ### compile
-refer to `examples/llava/android/build_64.sh`
+refer to `tools/mtmd/android/build_64.sh`
 ```sh
-mkdir examples/llava/android/build_64
-cd examples/llava/android/build_64
+mkdir tools/mtmd/android/build_64
+cd tools/mtmd/android/build_64
 ../build_64.sh
 ```
 ### run on Android
@@ -82,7 +82,7 @@ refer to `android/adb_run.sh`, modify resources' `name` and `path`
 ### case 1
 **input**
 ```sh
-/data/local/tmp/llama-llava-cli \
+/data/local/tmp/llama-mtmd-cli \
     -m /data/local/tmp/ggml-model-q4_k.gguf \
     --mmproj /data/local/tmp/mmproj-model-f16.gguf \
     -t 4 \
@@ -102,7 +102,7 @@ llama_print_timings:       total time =   34731.93 ms
 ### case 2
 **input**
 ```sh
-/data/local/tmp/llama-llava-cli \
+/data/local/tmp/llama-mtmd-cli \
     -m /data/local/tmp/ggml-model-q4_k.gguf \
     --mmproj /data/local/tmp/mmproj-model-f16.gguf \
     -t 4 \
@@ -123,10 +123,10 @@ llama_print_timings:       total time =   34570.79 ms
 
 ## Some result on Android with `Snapdragon 778G` chip
 ### MobileVLM-1.7B case
-#### llava-cli release-b2005
+#### mtmd-cli release-b2005
 **input**
 ```sh
-/data/local/tmp/llama-llava-cli \
+/data/local/tmp/llama-mtmd-cli \
     -m /data/local/tmp/ggml-model-q4_k.gguf \
     --mmproj /data/local/tmp/mmproj-model-f16.gguf \
     -t 4 \
@@ -147,7 +147,7 @@ llama_print_timings: prompt eval time =    8119.49 ms /   191 tokens (   42.51 m
 llama_print_timings:        eval time =    1005.75 ms /    14 runs   (   71.84 ms per token,    13.92 tokens per second)
 llama_print_timings:       total time =   28038.34 ms /   205 tokens
 ```
-#### llava-cli latest-version
+#### mtmd-cli latest-version
 **input**
 
 Just the same as above.
@@ -169,7 +169,7 @@ llama_print_timings:        eval time =   43894.02 ms /    13 runs   ( 3376.46 m
 llama_print_timings:       total time =  865441.76 ms /   204 tokens
 ```
 ### MobileVLM_V2-1.7B case
-#### llava-cli release-2005b
+#### mtmd-cli release-2005b
 **input**
 
 Just the same as above.
@@ -200,7 +200,7 @@ make GGML_CUDA=1 CUDA_DOCKER_ARCH=sm_87 GGML_CUDA_F16=1 -j 32
 ### case 1
 **input**
 ```sh
-./llama-llava-cli \
+./llama-mtmd-cli \
     -m /data/local/tmp/ggml-model-q4_k.gguf \
     --mmproj /data/local/tmp/mmproj-model-f16.gguf \
     --image /data/local/tmp/demo.jpeg \
@@ -224,7 +224,7 @@ llama_print_timings:       total time =    1352.63 ms /   252 tokens
 ### case 2
 **input**
 ```sh
-./llama-llava-cli \
+./llama-mtmd-cli \
     -m /data/local/tmp/ggml-model-q4_k.gguf \
     --mmproj /data/local/tmp/mmproj-model-f16.gguf \
     -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:" \

docs/multimodal/gemma3.md

@@ -11,26 +11,27 @@ You can use pre-quantized model from [ggml-org](https://huggingface.co/ggml-org)
 ```bash
 # build
 cmake -B build
-cmake --build build --target llama-gemma3-cli
+cmake --build build --target llama-mtmd-cli
 
 # alternatively, install from brew (MacOS)
 brew install llama.cpp
 
 # run it
-llama-gemma3-cli -hf ggml-org/gemma-3-4b-it-GGUF
-llama-gemma3-cli -hf ggml-org/gemma-3-12b-it-GGUF
-llama-gemma3-cli -hf ggml-org/gemma-3-27b-it-GGUF
+llama-mtmd-cli -hf ggml-org/gemma-3-4b-it-GGUF
+llama-mtmd-cli -hf ggml-org/gemma-3-12b-it-GGUF
+llama-mtmd-cli -hf ggml-org/gemma-3-27b-it-GGUF
 
 # note: 1B model does not support vision
 ```
 
 ## How to get mmproj.gguf?
 
+Simply to add `--mmproj` in when converting model via `convert_hf_to_gguf.py`:
+
 ```bash
 cd gemma-3-4b-it
-python ../llama.cpp/examples/llava/gemma3_convert_encoder_to_gguf.py .
-
-# output file is mmproj.gguf
+python ../llama.cpp/convert_hf_to_gguf.py --outfile model.gguf --outtype f16 --mmproj .
+# output file: mmproj-model.gguf
 ```
 
 ## How to run it?
@@ -43,8 +44,8 @@ What you need:
 ```bash
 # build
 cmake -B build
-cmake --build build --target llama-gemma3-cli
+cmake --build build --target llama-mtmd-cli
 
 # run it
-./build/bin/llama-gemma3-cli -m {text_model}.gguf --mmproj mmproj.gguf --image your_image.jpg
+./build/bin/llama-mtmd-cli -m {text_model}.gguf --mmproj mmproj.gguf --image your_image.jpg
 ```

docs/multimodal/glmedge.md

@@ -3,12 +3,12 @@
 Currently this implementation supports [glm-edge-v-2b](https://huggingface.co/THUDM/glm-edge-v-2b) and [glm-edge-v-5b](https://huggingface.co/THUDM/glm-edge-v-5b).
 
 ## Usage
-Build with cmake or run `make llama-llava-cli` to build it.
+Build the `llama-mtmd-cli` binary.
 
-After building, run: `./llama-llava-cli` to see the usage. For example:
+After building, run: `./llama-mtmd-cli` to see the usage. For example:
 
 ```sh
-./llama-llava-cli -m model_path/ggml-model-f16.gguf --mmproj model_path/mmproj-model-f16.gguf --image img_path/image.jpg -p "<|system|>\n system prompt <image><|user|>\n prompt <|assistant|>\n"
+./llama-mtmd-cli -m model_path/ggml-model-f16.gguf --mmproj model_path/mmproj-model-f16.gguf
 ```
 
 **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
@@ -25,13 +25,13 @@ git clone https://huggingface.co/THUDM/glm-edge-v-5b or https://huggingface.co/T
 2. Use `glmedge-surgery.py` to split the GLMV-EDGE model to LLM and multimodel projector constituents:
 
 ```sh
-python ./examples/llava/glmedge-surgery.py -m ../model_path
+python ./tools/mtmd/glmedge-surgery.py -m ../model_path
 ```
 
 4. Use `glmedge-convert-image-encoder-to-gguf.py` to convert the GLMV-EDGE image encoder to GGUF:
 
 ```sh
-python ./examples/llava/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
+python ./tools/mtmd/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
 ```
 
 5. Use `examples/convert_hf_to_gguf.py` to convert the LLM part of GLMV-EDGE to GGUF:

docs/multimodal/granitevision.md

@@ -176,15 +176,11 @@ Note that currently you cannot quantize the visual encoder because granite visio
 
 
 ### 5. Running the Model in Llama cpp
-Build llama cpp normally; you should have a target binary named `llama-llava-cli`, which you can pass two binaries to. As an example, we pass the the llama.cpp banner.
+Build llama cpp normally; you should have a target binary named `llama-mtmd-cli`, which you can pass two binaries to. As an example, we pass the the llama.cpp banner.
 
 ```bash
-$ ./build/bin/llama-llava-cli -m $LLM_GGUF_PATH \
+$ ./build/bin/llama-mtmd-cli -m $LLM_GGUF_PATH \
     --mmproj $VISUAL_GGUF_PATH \
-    --image ./media/llama0-banner.png \
     -c 16384 \
-    -p "<|system|>\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n<|user|>\n\<image>\nWhat does the text in this image say?\n<|assistant|>\n" \
     --temp 0
 ```
-
-Sample output: `The text in the image reads "LLAMA C++ Can it run DOOM Llama?"`

docs/multimodal/llava.md

@@ -11,12 +11,14 @@ For llava-1.6 a variety of prepared gguf models are available as well [7b-34b](h
 After API is confirmed, more models will be supported / uploaded.
 
 ## Usage
-Build with cmake or run `make llama-llava-cli` to build it.
+Build the `llama-mtmd-cli` binary.
 
-After building, run: `./llama-llava-cli` to see the usage. For example:
+After building, run: `./llama-mtmd-cli` to see the usage. For example:
 
 ```sh
-./llama-llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
+./llama-mtmd-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf \
+    --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf \
+    --chat-template vicuna
 ```
 
 **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
@@ -35,19 +37,19 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336
 2. Install the required Python packages:
 
 ```sh
-pip install -r examples/llava/requirements.txt
+pip install -r tools/mtmd/requirements.txt
 ```
 
 3. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
 
 ```sh
-python ./examples/llava/llava_surgery.py -m ../llava-v1.5-7b
+python ./tools/mtmd/llava_surgery.py -m ../llava-v1.5-7b
 ```
 
 4. Use `convert_image_encoder_to_gguf.py` to convert the LLaVA image encoder to GGUF:
 
 ```sh
-python ./examples/llava/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
+python ./tools/mtmd/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
 ```
 
 5. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF:
@@ -67,12 +69,12 @@ git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
 2) Install the required Python packages:
 
 ```sh
-pip install -r examples/llava/requirements.txt
+pip install -r tools/mtmd/requirements.txt
 ```
 
 3) Use `llava_surgery_v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
 ```console
-python examples/llava/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/
+python tools/mtmd/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/
 ```
 - you will find a llava.projector and a llava.clip file in your model directory
 
@@ -86,7 +88,7 @@ curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.jso
 
 5) Create the visual gguf model:
 ```console
-python ./examples/llava/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
+python ./tools/mtmd/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
 ```
 - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP
 
@@ -97,7 +99,7 @@ python ./examples/convert_legacy_llama.py ../llava-v1.6-vicuna-7b/ --skip-unknow
 
 7) And finally we can run the llava cli using the 1.6 model version:
 ```console
-./llama-llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096
+./llama-mtmd-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf
 ```
 
 **note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096)
@@ -122,17 +124,9 @@ model.language_model.save_pretrained(llm_export_path)
 
 Then, you can convert the LLM using the `convert_hf_to_gguf.py` script, which handles more LLM architectures.
 
-## llava-cli templating and llava-1.6 prompting
+## Chat template
 
-llava-1.5 models all use the same vicuna prompt, here you can just add your image question like `-p "Provide a full description."`
-For llava-1.5 models which are not vicuna (mistral and Yi) you need to adapt system prompt as well as user prompt, for this purpose llava-cli has a basic templating system:
-
-**For Mistral and using llava-cli binary:**
-Add this: `-p "<image>\nUSER:\nProvide a full description.\nASSISTANT:\n"`
-The mistral template for llava-1.6 seems to be no system print and a USER/ASSISTANT role
-
-**For the 34B this should work:**
-Add this: `-e -p <|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n<image>\nProvide a full description.<|im_end|><|im_start|>assistant\n`
+For llava-1.5 and llava-1.6, you need to use `vicuna` chat template. Simply add `--chat-template vicuna` to activate this template.
 
 
 ## How to know if you are running in llava-1.5 or llava-1.6 mode
@@ -147,12 +141,3 @@ When running llava-cli you will see a visual information right before the prompt
 
 
 Alternatively just pay notice to how many "tokens" have been used for your prompt, it will also show 1000+ tokens for llava-1.6
-
-
-
-
-## TODO
-
-- [x] Support non-CPU backend for the image encoding part.
-- [ ] Support different sampling methods.
-- [ ] Support more model variants.

docs/multimodal/minicpmo2.6.md

@@ -29,8 +29,8 @@ cmake --build build --config Release
 Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) by us)
 
 ```bash
-python ./examples/llava/minicpmv-surgery.py -m ../MiniCPM-o-2_6
-python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4
+python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-o-2_6
+python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4
 python ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model
 
 # quantize int4 version
@@ -40,9 +40,9 @@ python ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model
 
 Inference on Linux or Mac
 ```bash
-# run f16 version
-./build/bin/llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+# run in single-turn mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-o-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
 
-# run quantized int4 version
-./build/bin/llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
+# run in conversation mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf
 ```

docs/multimodal/minicpmv2.5.md

@@ -28,8 +28,8 @@ cmake --build build --config Release
 Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) by us)
 
 ```bash
-python ./examples/llava/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
-python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 2
+python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
+python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 2
 python ./convert_hf_to_gguf.py ../MiniCPM-Llama3-V-2_5/model
 
 # quantize int4 version
@@ -39,9 +39,9 @@ python ./convert_hf_to_gguf.py ../MiniCPM-Llama3-V-2_5/model
 
 Inference on Linux or Mac
 ```bash
-# run f16 version
-./build/bin/llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+# run in single-turn mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
 
-# run quantized int4 version
-./build/bin/llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
+# run in conversation mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf
 ```

docs/multimodal/minicpmv2.6.md

@@ -28,8 +28,8 @@ cmake --build build --config Release
 Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) by us)
 
 ```bash
-python ./examples/llava/minicpmv-surgery.py -m ../MiniCPM-V-2_6
-python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 3
+python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-V-2_6
+python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 3
 python ./convert_hf_to_gguf.py ../MiniCPM-V-2_6/model
 
 # quantize int4 version
@@ -39,9 +39,9 @@ python ./convert_hf_to_gguf.py ../MiniCPM-V-2_6/model
 
 Inference on Linux or Mac
 ```bash
-# run f16 version
-./build/bin/llama-minicpmv-cli -m ../MiniCPM-V-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+# run in single-turn mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
 
-# run quantized int4 version
-./build/bin/llama-minicpmv-cli -m ../MiniCPM-V-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
+# run in conversation mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf
 ```

examples/CMakeLists.txt

@@ -12,60 +12,30 @@ llama_add_compile_flags()
 
 # examples
 
-include_directories(${CMAKE_CURRENT_SOURCE_DIR})
-
 if (EMSCRIPTEN)
 else()
-    add_subdirectory(batched-bench)
     add_subdirectory(batched)
     add_subdirectory(embedding)
     add_subdirectory(eval-callback)
 
-    if (NOT WIN32)
-        # disabled on Windows because it uses internal functions not exported with LLAMA_API
-        add_subdirectory(gbnf-validator)
-    endif()
-
     add_subdirectory(gguf-hash)
-    add_subdirectory(gguf-split)
     add_subdirectory(gguf)
     add_subdirectory(gritlm)
-    add_subdirectory(imatrix)
-    add_subdirectory(infill)
-    add_subdirectory(llama-bench)
     add_subdirectory(lookahead)
     add_subdirectory(lookup)
-    add_subdirectory(main)
     add_subdirectory(parallel)
     add_subdirectory(passkey)
-    add_subdirectory(perplexity)
-    add_subdirectory(quantize)
     add_subdirectory(retrieval)
-    if (LLAMA_BUILD_SERVER)
-        add_subdirectory(server)
-    endif()
     add_subdirectory(save-load-state)
-    add_subdirectory(run)
     add_subdirectory(simple)
     add_subdirectory(simple-chat)
     add_subdirectory(speculative)
     add_subdirectory(speculative-simple)
-    add_subdirectory(tokenize)
-    add_subdirectory(tts)
     add_subdirectory(gen-docs)
+    add_subdirectory(training)
     if (NOT GGML_BACKEND_DL)
-        # these examples use the backends directly and cannot be built with dynamic loading
         add_subdirectory(convert-llama2c-to-ggml)
-        add_subdirectory(cvector-generator)
-        add_subdirectory(export-lora)
-        if (NOT WIN32)
-            # disabled on Windows because it uses internal functions not exported with LLAMA_API
-            add_subdirectory(quantize-stats)
-        endif()
-        add_subdirectory(llava)
-        if (GGML_RPC)
-            add_subdirectory(rpc)
-        endif()
+        # these examples use the backends directly and cannot be built with dynamic loading
         if (GGML_SYCL)
             add_subdirectory(sycl)
         endif()

examples/embedding/embedding.cpp

@@ -35,23 +35,14 @@ static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & toke
 
 static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) {
     const enum llama_pooling_type pooling_type = llama_pooling_type(ctx);
-    const struct llama_model * model = llama_get_model(ctx);
 
     // clear previous kv_cache values (irrelevant for embeddings)
     llama_kv_self_clear(ctx);
 
     // run model
     LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
-    if (llama_model_has_encoder(model) && !llama_model_has_decoder(model)) {
-        // encoder-only model
-        if (llama_encode(ctx, batch) < 0) {
-            LOG_ERR("%s : failed to encode\n", __func__);
-        }
-    } else if (!llama_model_has_encoder(model) && llama_model_has_decoder(model)) {
-        // decoder-only model
-        if (llama_decode(ctx, batch) < 0) {
-            LOG_ERR("%s : failed to decode\n", __func__);
-        }
+    if (llama_encode(ctx, batch) < 0) {
+        LOG_ERR("%s : failed to encode\n", __func__);
     }
 
     for (int i = 0; i < batch.n_tokens; i++) {
@@ -89,6 +80,13 @@ int main(int argc, char ** argv) {
     common_init();
 
     params.embedding = true;
+
+    // utilize the full context
+    if (params.n_batch < params.n_ctx) {
+        LOG_WRN("%s: setting batch size to %d\n", __func__, params.n_ctx);
+        params.n_batch = params.n_ctx;
+    }
+
     // For non-causal models, batch size must be equal to ubatch size
     params.n_ubatch = params.n_batch;
 
@@ -134,7 +132,6 @@ int main(int argc, char ** argv) {
 
     // max batch size
     const uint64_t n_batch = params.n_batch;
-    GGML_ASSERT(params.n_batch >= params.n_ctx);
 
     // tokenize the prompts and trim
     std::vector<std::vector<int32_t>> inputs;

examples/gbnf-validator/CMakeLists.txt

@@ -1,5 +0,0 @@
-set(TARGET llama-gbnf-validator)
-add_executable(${TARGET} gbnf-validator.cpp)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)

examples/infill/CMakeLists.txt

@@ -1,5 +0,0 @@
-set(TARGET llama-infill)
-add_executable(${TARGET} infill.cpp)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)

examples/infill/README.md

@@ -1,47 +0,0 @@
-# llama.cpp/example/infill
-
-This example shows how to use the infill mode with Code Llama models supporting infill mode.
-Currently the 7B and 13B models support infill mode.
-
-Infill supports most of the options available in the main example.
-
-For further information have a look at the main README.md in llama.cpp/example/main/README.md
-
-## Common Options
-
-In this section, we cover the most commonly used options for running the `infill` program with the LLaMA models:
-
--   `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
--   `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
--   `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.
--   `-c N, --ctx-size N`: Set the size of the prompt context. The default is 4096, but if a LLaMA model was built with a longer context, increasing this value will provide better results for longer input/inference.
--   `--spm-infill`: Use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this.
-
-## Input Prompts
-
-The `infill` program provides several ways to interact with the LLaMA models using input prompts:
-
--   `--in-prefix PROMPT_BEFORE_CURSOR`: Provide the prefix directly as a command-line option.
--   `--in-suffix PROMPT_AFTER_CURSOR`: Provide the suffix directly as a command-line option.
--   `--interactive-first`: Run the program in interactive mode and wait for input right away. (More on this below.)
-
-## Interaction
-
-The `infill` program offers a seamless way to interact with LLaMA models, allowing users to receive real-time infill suggestions. The interactive mode can be triggered using `--interactive`, and `--interactive-first`
-
-### Interaction Options
-
--   `-i, --interactive`: Run the program in interactive mode, allowing users to get real time code suggestions from model.
--   `--interactive-first`: Run the program in interactive mode and immediately wait for user input before starting the text generation.
--   `--color`: Enable colorized output to differentiate visually distinguishing between prompts, user input, and generated text.
-
-### Example
-
-Download a model that supports infill, for example CodeLlama:
-```console
-scripts/hf.sh --repo TheBloke/CodeLlama-13B-GGUF --file codellama-13b.Q5_K_S.gguf --outdir models
-```
-
-```bash
-./llama-infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n    print(\"hell" --in-suffix "\n   print(\"goodbye world\")\n    "
-```

examples/infill/infill.cpp

@@ -1,590 +0,0 @@
-#include "arg.h"
-#include "common.h"
-#include "console.h"
-#include "sampling.h"
-#include "log.h"
-#include "llama.h"
-
-#include <cassert>
-#include <cinttypes>
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <ctime>
-#include <fstream>
-#include <iostream>
-#include <sstream>
-#include <string>
-#include <vector>
-
-#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
-#include <signal.h>
-#include <unistd.h>
-#elif defined (_WIN32)
-#define WIN32_LEAN_AND_MEAN
-#ifndef NOMINMAX
-#define NOMINMAX
-#endif
-#include <windows.h>
-#include <signal.h>
-#endif
-
-#if defined(_MSC_VER)
-#pragma warning(disable: 4244 4267) // possible loss of data
-#endif
-
-static llama_context           ** g_ctx;
-static llama_model             ** g_model;
-static common_sampler          ** g_smpl;
-static common_params            * g_params;
-static std::vector<llama_token> * g_input_tokens;
-static std::ostringstream       * g_output_ss;
-static std::vector<llama_token> * g_output_tokens;
-
-static bool is_interacting = false;
-
-#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
-static void sigint_handler(int signo) {
-    if (signo == SIGINT) {
-        if (!is_interacting) {
-            is_interacting = true;
-        } else {
-            console::cleanup();
-            LOG("\n");
-            common_perf_print(*g_ctx, *g_smpl);
-
-            // make sure all logs are flushed
-            LOG("Interrupted by user\n");
-            common_log_pause(common_log_main());
-
-            _exit(130);
-        }
-    }
-}
-#endif
-
-int main(int argc, char ** argv) {
-    common_params params;
-    g_params = &params;
-
-    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_INFILL)) {
-        return 1;
-    }
-
-    common_init();
-
-    auto & sparams = params.sampling;
-
-    console::init(params.simple_io, params.use_color);
-    atexit([]() { console::cleanup(); });
-
-    if (params.logits_all) {
-        LOG_ERR("\n************\n");
-        LOG_ERR("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__);
-        LOG_ERR("************\n\n");
-
-        return 0;
-    }
-
-    if (params.embedding) {
-        LOG_ERR("\n************\n");
-        LOG_ERR("%s: please use the 'embedding' tool for embedding calculations\n", __func__);
-        LOG_ERR("************\n\n");
-
-        return 0;
-    }
-
-    if (params.n_ctx != 0 && params.n_ctx < 8) {
-        LOG_WRN("%s: minimum context size is 8, using minimum size.\n", __func__);
-        params.n_ctx = 8;
-    }
-
-    if (!params.interactive_first && (params.input_prefix.empty() && params.input_suffix.empty())) {
-        LOG_ERR("\n************\n");
-        LOG_ERR("%s: please use '--interactive_first' or specify '--in_prefix' and/or '--in_suffix'\n", __func__);
-        LOG_ERR("************\n\n");
-
-        return 0;
-    }
-
-    if (params.rope_freq_base != 0.0) {
-        LOG_WRN("%s: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base);
-    }
-
-    if (params.rope_freq_scale != 0.0) {
-        LOG_WRN("%s: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale);
-    }
-
-    LOG_INF("%s: llama backend init\n", __func__);
-    llama_backend_init();
-    llama_numa_init(params.numa);
-
-    llama_model * model = nullptr;
-    llama_context * ctx = nullptr;
-    common_sampler * smpl = nullptr;
-
-    g_model = &model;
-    g_ctx = &ctx;
-    g_smpl = &smpl;
-
-    // load the model and apply lora adapter, if any
-    LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
-    common_init_result llama_init = common_init_from_params(params);
-
-    model = llama_init.model.get();
-    ctx = llama_init.context.get();
-
-    if (model == NULL) {
-        LOG_ERR("%s: unable to load model\n", __func__);
-        return 1;
-    }
-
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
-    const int n_ctx_train = llama_model_n_ctx_train(model);
-    const int n_ctx = llama_n_ctx(ctx);
-    LOG_DBG("n_ctx: %d\n", n_ctx);
-
-    if (n_ctx > n_ctx_train) {
-        LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, n_ctx);
-    }
-
-    // print system information
-    {
-        LOG_INF("\n");
-        LOG_INF("%s\n", common_params_get_system_info(params).c_str());
-    }
-    const bool add_bos = llama_vocab_get_add_bos(vocab);
-    GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
-
-    std::vector<llama_token> embd_inp;
-    std::vector<llama_token> embd_end;
-    std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false);
-    std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false);
-
-    GGML_ASSERT(llama_vocab_fim_pre(vocab) >= 0);
-    GGML_ASSERT(llama_vocab_fim_suf(vocab) >= 0);
-
-    inp_pfx.insert(inp_pfx.begin(), llama_vocab_fim_pre(vocab));
-    inp_sfx.insert(inp_sfx.begin(), llama_vocab_fim_suf(vocab));
-
-    embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
-    embd_end = params.spm_infill ? inp_pfx : inp_sfx;
-    if (add_bos) {
-        embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab));
-    }
-    embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
-
-    const llama_token middle_token = llama_vocab_fim_mid(vocab);
-    if (middle_token >= 0) {
-        embd_inp.push_back(middle_token);
-    }
-
-    LOG_DBG("add_bos: %d\n", add_bos);
-    LOG_DBG("prefix: \"%s\"\n", params.input_prefix.c_str());
-    LOG_DBG("suffix: \"%s\"\n", params.input_suffix.c_str());
-    LOG_DBG("tokens: %s\n", string_from(ctx, embd_inp).c_str());
-
-    // Should not run without any tokens
-    if (embd_inp.empty()) {
-        embd_inp.push_back(llama_vocab_bos(vocab));
-        LOG_WRN("embd_inp was considered empty and bos was added: %s\n", string_from(ctx, embd_inp).c_str());
-    }
-
-    if ((int) embd_inp.size() > n_ctx - 4) {
-        LOG_ERR("%s: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
-        return 1;
-    }
-
-    // number of tokens to keep when resetting context
-    if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size()) {
-        params.n_keep = (int)embd_inp.size();
-    }
-
-    LOG_INF("inp_pfx: %s\n", string_from(ctx, inp_pfx).c_str());
-    LOG_INF("inp_sfx: %s\n", string_from(ctx, inp_sfx).c_str());
-
-    // enable interactive mode if interactive start is specified
-    if (params.interactive_first) {
-        params.interactive = true;
-    }
-
-    if (params.verbose_prompt) {
-        LOG_INF("\n");
-        LOG_INF("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
-        LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
-        for (int i = 0; i < (int) embd_inp.size(); i++) {
-            LOG_INF("%6d -> '%s'\n", embd_inp[i], common_token_to_piece(ctx, embd_inp[i]).c_str());
-        }
-
-        if (params.n_keep > 0) {
-        LOG_INF("%s: static prompt based on n_keep: '", __func__);
-            for (int i = 0; i < params.n_keep; i++) {
-                LOG_CNT("%s", common_token_to_piece(ctx, embd_inp[i]).c_str());
-            }
-            LOG_CNT("'\n");
-        }
-        LOG_INF("\n");
-    }
-
-    if (params.interactive) {
-#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
-        struct sigaction sigint_action;
-        sigint_action.sa_handler = sigint_handler;
-        sigemptyset (&sigint_action.sa_mask);
-        sigint_action.sa_flags = 0;
-        sigaction(SIGINT, &sigint_action, NULL);
-#elif defined (_WIN32)
-        auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
-            return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
-        };
-        SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
-#endif
-
-        LOG_INF("%s: interactive mode on.\n", __func__);
-
-        if (params.input_prefix_bos) {
-            LOG_INF("Input prefix with BOS\n");
-        }
-
-        if (!params.input_prefix.empty()) {
-            LOG_INF("Input prefix: '%s'\n", params.input_prefix.c_str());
-        }
-
-        if (!params.input_suffix.empty()) {
-            LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str());
-        }
-    }
-    smpl = common_sampler_init(model, sparams);
-
-    LOG_INF("sampler seed: %u\n",     common_sampler_get_seed(smpl));
-    LOG_INF("sampler params: \n%s\n", sparams.print().c_str());
-    LOG_INF("sampler chain: %s\n",    common_sampler_print(smpl).c_str());
-
-    LOG_INF("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
-
-    LOG_INF("\n");
-    LOG_INF("\n#####  Infill mode  #####\n\n");
-    if (params.interactive) {
-        const char *control_message;
-        if (params.multiline_input) {
-            control_message = " - To return control to LLaMA, end your input with '\\'.\n"
-                              " - To return control without starting a new line, end your input with '/'.\n";
-        } else {
-            control_message = " - Press Return to return control to LLaMA.\n"
-                              " - To return control without starting a new line, end your input with '/'.\n"
-                              " - If you want to submit another line, end your input with '\\'.\n";
-        }
-        LOG_INF("== Running in interactive mode. ==\n");
-#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
-        LOG_INF(       " - Press Ctrl+C to interject at any time.\n");
-#endif
-        LOG_INF(       "%s\n", control_message);
-
-        is_interacting = params.interactive_first;
-    }
-
-    bool input_echo = true;
-
-    int n_past     = 0;
-    int n_remain   = params.n_predict;
-    int n_consumed = 0;
-
-    std::vector<int>   input_tokens;  g_input_tokens  = &input_tokens;
-    std::vector<int>   output_tokens; g_output_tokens = &output_tokens;
-    std::ostringstream output_ss;     g_output_ss     = &output_ss;
-
-    // the first thing we will do is to output the prompt, so set color accordingly
-    console::set_display(console::prompt);
-
-    std::vector<llama_token> embd;
-
-    while (n_remain != 0 || params.interactive) {
-        // predict
-        if (!embd.empty()) {
-            // Note: n_ctx - 4 here is to match the logic for commandline prompt handling via
-            // --prompt or --file which uses the same value.
-            int max_embd_size = n_ctx - 4;
-
-            // Ensure the input doesn't exceed the context size by truncating embd if necessary.
-            if ((int) embd.size() > max_embd_size) {
-                const int skipped_tokens = (int) embd.size() - max_embd_size;
-                embd.resize(max_embd_size);
-
-                console::set_display(console::error);
-                LOG_WRN("<<input too long: skipped %d token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
-                console::set_display(console::reset);
-            }
-
-            // infinite text generation via context swapping
-            // if we run out of context:
-            // - take the n_keep first tokens from the original prompt (via n_past)
-            // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
-            if (n_past + (int) embd.size() > n_ctx) {
-                if (params.n_predict == -2) {
-                    LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
-                    break;
-                }
-
-                const int n_left    = n_past - params.n_keep - 1;
-                const int n_discard = n_left/2;
-
-                LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
-                    n_past, n_left, n_ctx, params.n_keep, n_discard);
-
-                llama_kv_self_seq_rm (ctx, 0, params.n_keep + 1            , params.n_keep + n_discard + 1);
-                llama_kv_self_seq_add(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
-
-                n_past -= n_discard;
-
-                LOG_DBG("after swap: n_past = %d\n", n_past);
-
-                LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str());
-
-            }
-
-            // evaluate tokens in batches
-            // embd is typically prepared beforehand to fit within a batch, but not always
-            for (int i = 0; i < (int) embd.size(); i += params.n_batch) {
-                int n_eval = (int) embd.size() - i;
-                if (n_eval > params.n_batch) {
-                    n_eval = params.n_batch;
-                }
-
-                LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str());
-
-                if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) {
-                    LOG_ERR("%s : failed to eval\n", __func__);
-                    return 1;
-                }
-
-                n_past += n_eval;
-
-                LOG_DBG("n_past = %d\n", n_past);
-            }
-
-        }
-
-        embd.clear();
-
-        if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
-            const llama_token id = common_sampler_sample(smpl, ctx, -1);
-
-            common_sampler_accept(smpl, id, true);
-
-            // LOG_DBG("last: %s\n", string_from(ctx, smpl->prev.to_vector()).c_str());
-
-            embd.push_back(id);
-
-            // echo this to console
-            input_echo = true;
-
-            // decrement remaining sampling budget
-            --n_remain;
-
-            LOG_DBG("n_remain: %d\n", n_remain);
-        } else {
-            // some user input remains from prompt or interaction, forward it to processing
-            LOG_DBG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed);
-            while ((int) embd_inp.size() > n_consumed) {
-                embd.push_back(embd_inp[n_consumed]);
-
-                // push the prompt in the sampling context in order to apply repetition penalties later
-                // for the prompt, we don't apply grammar rules
-                common_sampler_accept(smpl, embd_inp[n_consumed], false);
-
-                ++n_consumed;
-                if ((int) embd.size() >= params.n_batch) {
-                    break;
-                }
-            }
-        }
-
-        // display text
-        if (input_echo) {
-            for (auto id : embd) {
-                const std::string token_str = common_token_to_piece(ctx, id);
-                LOG("%s", token_str.c_str());
-
-                if (embd.size() > 1) {
-                    input_tokens.push_back(id);
-                } else {
-                    output_tokens.push_back(id);
-                    output_ss << token_str;
-                }
-            }
-        }
-        // reset color to default if we there is no pending user input
-        if (input_echo && (int) embd_inp.size() == n_consumed) {
-            console::set_display(console::reset);
-        }
-
-        // if not currently processing queued inputs;
-        if ((int) embd_inp.size() <= n_consumed) {
-            // deal with eot token in infill mode
-            if ((common_sampler_last(smpl) == llama_vocab_eot(vocab) || is_interacting) && params.interactive){
-                if (is_interacting && !params.interactive_first) {
-                    // print an eot token
-                    LOG("%s", common_token_to_piece(ctx, llama_vocab_eot(vocab)).c_str());
-                }
-                LOG("\n");
-                console::set_display(console::user_input);
-                std::string buffer;
-                std::string line;
-                bool another_line=true;
-                // set a new prefix via stdin
-                do {
-                    another_line = console::readline(line, params.multiline_input);
-                    buffer += line;
-                } while (another_line);
-                // check if we got an empty line, if so we use the old input
-                if (!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) {
-                    params.input_prefix = buffer;
-                }
-                buffer.clear();
-                // set a new suffix via stdin
-                do {
-                    another_line = console::readline(line, params.multiline_input);
-                    buffer += line;
-                } while (another_line);
-                // check if we got an empty line
-                if (!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) {
-                    params.input_suffix = buffer;
-                }
-                buffer.clear();
-                // done taking input, reset color
-                console::set_display(console::reset);
-
-                if (params.escape) {
-                    //process escape sequences, for the initial prompt this is done in common.cpp when we load the params, but for the interactive mode we need to do it here
-                    string_process_escapes(params.input_prefix);
-                    string_process_escapes(params.input_suffix);
-                }
-
-                // tokenize new prefix and suffix
-                std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false);
-                std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false);
-
-                inp_pfx.insert(inp_pfx.begin(), llama_vocab_fim_pre(vocab));
-                inp_sfx.insert(inp_sfx.begin(), llama_vocab_fim_suf(vocab));
-
-                embd_inp = params.spm_infill ? inp_sfx : inp_pfx;
-                embd_end = params.spm_infill ? inp_pfx : inp_sfx;
-                if (add_bos) {
-                    embd_inp.insert(embd_inp.begin(), llama_vocab_bos(vocab));
-                }
-                embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end());
-
-                if (middle_token >= 0) {
-                    embd_inp.push_back(middle_token);
-                }
-
-                embd.clear();
-                n_remain = params.n_predict;
-                n_past = 0;
-                n_consumed = 0;
-                is_interacting = false;
-            }
-            // deal with end of generation tokens in interactive mode
-            else if (llama_vocab_is_eog(vocab, common_sampler_last(smpl))) {
-                LOG_DBG("found EOS token\n");
-
-                if (params.interactive) {
-
-                    is_interacting = true;
-                    LOG("\n");
-                    console::set_display(console::user_input);
-               }
-            }
-
-            if (n_past > 0 && is_interacting && !params.interactive) {
-                LOG_DBG("waiting for user input\n");
-
-                if (params.input_prefix_bos) {
-                    LOG_DBG("adding input prefix BOS token\n");
-                    embd_inp.push_back(llama_vocab_bos(vocab));
-                }
-
-                std::string buffer;
-                if (!params.input_prefix.empty()) {
-                    LOG_DBG("appending input prefix: '%s'\n", params.input_prefix.c_str());
-                    buffer += params.input_prefix;
-                    LOG("%s", buffer.c_str());
-                }
-
-                std::string line;
-                bool another_line = true;
-                do {
-                    another_line = console::readline(line, params.multiline_input);
-                    buffer += line;
-                } while (another_line);
-
-                // done taking input, reset color
-                console::set_display(console::reset);
-
-                // Add tokens to embd only if the input buffer is non-empty
-                // Entering a empty line lets the user pass control back
-                if (buffer.length() > 1) {
-                    // append input suffix if any
-                    if (!params.input_suffix.empty()) {
-                        LOG_DBG("appending input suffix: '%s'\n", params.input_suffix.c_str());
-                        buffer += params.input_suffix;
-                        LOG("%s", params.input_suffix.c_str());
-                    }
-
-                    LOG_DBG("buffer: '%s'\n", buffer.c_str());
-
-                    const size_t original_size = embd_inp.size();
-
-                    const auto line_inp = common_tokenize(ctx, buffer, false);
-                    LOG_DBG("input tokens: %s\n", string_from(ctx, line_inp).c_str());
-
-                    embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
-
-                    for (size_t i = original_size; i < embd_inp.size(); ++i) {
-                        const llama_token token = embd_inp[i];
-                        output_tokens.push_back(token);
-                        output_ss << common_token_to_piece(ctx, token);
-                    }
-
-                    n_remain -= line_inp.size();
-                    LOG_DBG("n_remain: %d\n", n_remain);
-                } else {
-                    LOG_DBG("empty line, passing control back\n");
-                }
-
-                input_echo = false; // do not echo this again
-            }
-
-            if (n_past > 0) {
-                if (is_interacting) {
-                    common_sampler_reset(smpl);
-                }
-                is_interacting = false;
-            }
-        }
-
-        // end of generation
-        if (!embd.empty() && llama_vocab_is_eog(vocab, embd.back()) && !params.interactive) {
-            break;
-        }
-
-        // In interactive mode, respect the maximum number of tokens and drop back to user input when reached.
-        // We skip this logic when n_predict == -1 (infinite) or -2 (stop at context size).
-        if (params.interactive && n_remain <= 0 && params.n_predict >= 0) {
-            n_remain = params.n_predict;
-            is_interacting = true;
-        }
-    }
-    if (!params.interactive && n_remain <= 0) {
-        LOG("%s", common_token_to_piece(ctx, llama_vocab_eot(vocab)).c_str());
-    }
-
-    LOG("\n");
-    common_perf_print(ctx, smpl);
-
-    common_sampler_free(smpl);
-    llama_backend_free();
-
-    return 0;
-}

examples/json_schema_to_grammar.py

@@ -10,6 +10,9 @@ from typing import Any, List, Optional, Set, Tuple, Union
 
 def _build_repetition(item_rule, min_items, max_items, separator_rule=None):
 
+    if max_items == 0:
+        return ""
+
     if min_items == 0 and max_items == 1:
         return f'{item_rule}?'
 

examples/llava/CMakeLists.txt

@@ -1,97 +0,0 @@
-# llava (legacy)
-
-add_library(llava OBJECT
-            llava.cpp
-            llava.h
-            clip.cpp
-            clip.h
-            )
-
-target_link_libraries(llava PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
-
-target_include_directories(llava PUBLIC .)
-target_include_directories(llava PUBLIC ../..)
-target_include_directories(llava PUBLIC ../../common)
-
-target_compile_features(llava PRIVATE cxx_std_17)
-
-add_library(llava_static STATIC $<TARGET_OBJECTS:llava>)
-if (BUILD_SHARED_LIBS)
-    set_target_properties(llava PROPERTIES POSITION_INDEPENDENT_CODE ON)
-    target_compile_definitions(llava PRIVATE LLAMA_SHARED LLAMA_BUILD)
-    add_library(llava_shared SHARED $<TARGET_OBJECTS:llava>)
-    target_link_libraries(llava_shared PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
-    install(TARGETS llava_shared LIBRARY)
-endif()
-
-# mtmd
-
-add_library(mtmd OBJECT
-            mtmd.cpp
-            mtmd.h
-            clip.cpp
-            clip.h
-            clip-impl.h
-            )
-
-target_link_libraries(mtmd PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
-
-target_include_directories(mtmd PUBLIC .)
-target_include_directories(mtmd PRIVATE ../..)
-target_include_directories(mtmd PRIVATE ../../common) # for stb_image.h
-
-target_compile_features(mtmd PRIVATE cxx_std_17)
-
-add_library(mtmd_static STATIC $<TARGET_OBJECTS:mtmd>)
-if (BUILD_SHARED_LIBS)
-    set_target_properties(mtmd PROPERTIES POSITION_INDEPENDENT_CODE ON)
-    target_compile_definitions(mtmd PRIVATE LLAMA_SHARED LLAMA_BUILD)
-    add_library(mtmd_shared SHARED $<TARGET_OBJECTS:mtmd>)
-    target_link_libraries(mtmd_shared PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
-    install(TARGETS mtmd_shared LIBRARY)
-endif()
-
-if (NOT MSVC)
-    target_compile_options(llava PRIVATE -Wno-cast-qual) # stb_image.h
-    target_compile_options(mtmd PRIVATE -Wno-cast-qual) # stb_image.h
-endif()
-
-if(TARGET BUILD_INFO)
-    add_dependencies(llava BUILD_INFO)
-    add_dependencies(mtmd BUILD_INFO)
-endif()
-
-set(TARGET llama-llava-cli)
-add_executable(${TARGET} llava-cli.cpp)
-set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-cli)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)
-
-set(TARGET llama-minicpmv-cli)
-add_executable(${TARGET} minicpmv-cli.cpp)
-set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-minicpmv-cli)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)
-
-set(TARGET llama-qwen2vl-cli)
-add_executable(${TARGET} qwen2vl-cli.cpp)
-set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-qwen2vl-cli)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)
-
-set(TARGET llama-gemma3-cli)
-add_executable(${TARGET} gemma3-cli.cpp)
-set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-gemma3-cli)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common mtmd ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)
-
-set(TARGET llama-llava-clip-quantize-cli)
-add_executable(${TARGET} clip-quantize-cli.cpp)
-set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-clip-quantize-cli)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_17)

examples/llava/README-quantize.md

@@ -1,44 +0,0 @@
-# Quantizing CLIP Visual Projector
-
-This is the tool for quantizing the CLIP visual projector model. Quantization reduces the precision of the model's weights, which can significantly decrease the model size and improve inference speed, often with minimal impact on performance.
-
-## Usage
-
-To quantize a CLIP visual projector model, use the following command:
-
-```sh
-./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf <type>
-```
-
-After the quantization, the visual projector can be used freely with the existing LLAVA cli (LLAVA, Qwen2VL, etc).
-
-### Arguments
-
-- `/path/to/ggml-model-f32.gguf`: The path to the input model file in FP32 or FP16 format.
-- `/path/to/ggml-model-quantized.gguf`: The path where the quantized model will be saved.
-- `<type>`: The quantization type to apply. This should be an integer corresponding to one of the quantization types defined in the `enum ggml_type`.
-
-### Quantization Types
-
-The following quantization types are supported, based on the `enum ggml_type` definition:
-
-- `2` - `q4_0`: 4-bit quantization with a single scale value.
-- `3` - `q4_1`: 4-bit quantization with a separate scale value for each block.
-- `6` - `q5_0`: 5-bit quantization with a single scale value.
-- `7` - `q5_1`: 5-bit quantization with a separate scale value for each block.
-- `8` - `q8_0`: 8-bit quantization with a single scale value.
-
-### Example
-
-To quantize a model using the `q4_0` quantization type, you would run:
-
-```sh
-./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf 2
-```
-
-This command will generate a quantized model at `/path/to/ggml-model-quantized.gguf` using the `q4_0` quantization method.
-
-## Notes
-
-- Quantization can lead to a loss in model accuracy, depending on the chosen quantization type. It is recommended to evaluate the quantized model's performance on your specific task to ensure it meets your requirements.
-- The quantized model will typically be smaller in size and faster to run, making it more suitable for deployment in resource-constrained environments.

examples/llava/android/adb_run.sh

@@ -1,53 +0,0 @@
-#!/bin/bash
-
-model_dir="/Users/cxt/model/llm/mobileVLM/MobileVLM-1.7B_processed"
-projector_name="mmproj-model-f16.gguf"
-llama_name="ggml-model-q4_k.gguf"
-img_dir="/Users/cxt/model/llm"
-img_name="demo.jpg"
-prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:"
-# img_name="cat.jpeg"
-# prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:"
-
-program_dir="build_64/bin"
-binName="llama-llava-cli"
-n_threads=4
-
-
-deviceDir="/data/local/tmp"
-saveDir="output"
-if [ ! -d ${saveDir} ]; then
-    mkdir ${saveDir}
-fi
-
-
-function android_run() {
-    # # copy resource into device
-    # adb push ${model_dir}/${projector_name} ${deviceDir}/${projector_name}
-    # adb push ${model_dir}/${llama_name} ${deviceDir}/${llama_name}
-    adb push ${img_dir}/${img_name} ${deviceDir}/${img_name}
-    # copy program into device
-    adb push ${program_dir}/${binName} ${deviceDir}/${binName}
-    adb shell "chmod 0777 ${deviceDir}/${binName}"
-
-    # run
-    adb shell "echo cd ${deviceDir} ${deviceDir}/${binName} \
-                                                 -m ${deviceDir}/${llama_name} \
-                                                 --mmproj ${deviceDir}/${projector_name} \
-                                                 -t ${n_threads} \
-                                                 --image ${deviceDir}/${img_name} \
-                                                 -p \"${prompt}\" \
-                                                 > ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt"
-    adb shell "cd ${deviceDir}; pwd; ${deviceDir}/${binName} \
-                                                 -m ${deviceDir}/${llama_name} \
-                                                 --mmproj ${deviceDir}/${projector_name} \
-                                                 -t ${n_threads} \
-                                                 --image ${deviceDir}/${img_name} \
-                                                 -p \"${prompt}\" \
-                                                 >> ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt 2>&1"
-    adb pull ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt ${saveDir}
-}
-
-android_run
-
-echo "android_run is Done!"

examples/llava/android/build_64.sh

@@ -1,8 +0,0 @@
-#!/bin/bash
-cmake ../../../../ \
--DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
--DCMAKE_BUILD_TYPE=Release \
--DANDROID_ABI="arm64-v8a" \
--DANDROID_PLATFORM=android-23 $1
-
-make -j4

examples/llava/clip-quantize-cli.cpp

@@ -1,59 +0,0 @@
-#include "arg.h"
-#include "base64.hpp"
-#include "log.h"
-#include "common.h"
-#include "sampling.h"
-#include "clip.h"
-#include "llava.h"
-#include "llama.h"
-#include "ggml.h"
-
-static void print_usage(int argc, char ** argv) {
-    (void) argc;
-
-    fprintf(stderr, "usage: %s /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf type\n", argv[0]);
-    fprintf(stderr, "  type = 2 - q4_0\n");
-    fprintf(stderr, "  type = 3 - q4_1\n");
-    fprintf(stderr, "  type = 6 - q5_0\n");
-    fprintf(stderr, "  type = 7 - q5_1\n");
-    fprintf(stderr, "  type = 8 - q8_0\n");
-}
-
-int main(int argc, char ** argv) {
-    if (argc != 4) {
-        print_usage(argc, argv);
-        return 1;
-    }
-
-    const std::string fname_inp = argv[1];
-    const std::string fname_out = argv[2];
-
-    const int itype = atoi(argv[3]);
-
-    const int64_t t_main_start_us = ggml_time_us();
-
-    int64_t t_quantize_us = 0;
-
-    // load the model
-    {
-        const int64_t t_start_us = ggml_time_us();
-
-        if (!clip_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype)) {
-            fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
-            return 1;
-        }
-
-        t_quantize_us = ggml_time_us() - t_start_us;
-    }
-
-    // report timing
-    {
-        const int64_t t_main_end_us = ggml_time_us();
-
-        printf("\n");
-        printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us / 1000.0f);
-        printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f);
-    }
-
-    return 0;
-}

examples/llava/clip.h

@@ -1,125 +0,0 @@
-#ifndef CLIP_H
-#define CLIP_H
-
-#include "ggml.h"
-#include <stddef.h>
-#include <stdint.h>
-
-#ifdef LLAMA_SHARED
-#    if defined(_WIN32) && !defined(__MINGW32__)
-#        ifdef LLAMA_BUILD
-#            define CLIP_API __declspec(dllexport)
-#        else
-#            define CLIP_API __declspec(dllimport)
-#        endif
-#    else
-#        define CLIP_API __attribute__ ((visibility ("default")))
-#    endif
-#else
-#    define CLIP_API
-#endif
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-struct clip_ctx;
-
-struct clip_image_size {
-    int width;
-    int height;
-};
-
-struct clip_image_u8_batch;
-struct clip_image_f32_batch;
-
-struct clip_context_params {
-    bool use_gpu;
-    ggml_log_level verbosity;
-};
-
-// deprecated, use clip_init
-CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity);
-
-CLIP_API struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params);
-
-CLIP_API void clip_free(struct clip_ctx * ctx);
-
-CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
-CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w);
-
-CLIP_API int32_t clip_get_image_size (const struct clip_ctx * ctx);
-CLIP_API int32_t clip_get_patch_size (const struct clip_ctx * ctx);
-CLIP_API int32_t clip_get_hidden_size(const struct clip_ctx * ctx);
-
-// TODO: should be enum, not string
-CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx);
-
-CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx);
-CLIP_API size_t get_clip_image_grid_size(const struct clip_ctx * ctx);
-
-CLIP_API int clip_n_patches        (const struct clip_ctx * ctx);
-CLIP_API int clip_n_patches_by_img (const struct clip_ctx * ctx, struct clip_image_f32 * img);
-CLIP_API int clip_n_mmproj_embd    (const struct clip_ctx * ctx);
-
-CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip);
-CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size);
-CLIP_API struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip);
-
-CLIP_API struct clip_image_size      * clip_image_size_init();
-CLIP_API struct clip_image_u8        * clip_image_u8_init ();
-CLIP_API struct clip_image_f32       * clip_image_f32_init();
-CLIP_API struct clip_image_f32_batch * clip_image_f32_batch_init(); // only used by libllava
-
-// nx, ny are the output image dimensions
-CLIP_API unsigned char * clip_image_u8_get_data(struct clip_image_u8 * img, uint32_t * nx, uint32_t * ny);
-
-CLIP_API void clip_image_size_free (struct clip_image_size * img_size);
-CLIP_API void clip_image_u8_free (struct clip_image_u8  * img);
-CLIP_API void clip_image_f32_free(struct clip_image_f32 * img);
-CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch  * batch);
-CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch * batch);
-
-// use for accessing underlay data of clip_image_f32_batch
-CLIP_API size_t clip_image_f32_batch_n_images(const struct clip_image_f32_batch * batch); // equivalent to batch->size()
-CLIP_API size_t clip_image_f32_batch_nx(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->nx
-CLIP_API size_t clip_image_f32_batch_ny(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->ny
-CLIP_API clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->data
-
-/**
- * Build image from pixels decoded by other libraries instead of stb_image.h for better performance.
- * The memory layout is RGBRGBRGB..., input buffer length must be 3*nx*ny bytes
- */
-CLIP_API void clip_build_img_from_pixels(const unsigned char * rgb_pixels, int nx, int ny, struct clip_image_u8 * img);
-
-CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
-
-/** interpret bytes as an image file with length bytes_length, and use the result to populate img */
-CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
-
-/** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */
-CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
-
-CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);
-
-CLIP_API bool clip_image_encode      (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec);
-CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec);
-
-CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype);
-
-CLIP_API int clip_is_minicpmv(const struct clip_ctx * ctx);
-CLIP_API bool clip_is_glm(const struct clip_ctx * ctx);
-CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx);
-CLIP_API bool clip_is_llava(const struct clip_ctx * ctx);
-CLIP_API bool clip_is_gemma3(const struct clip_ctx * ctx);
-
-CLIP_API int get_deepest_feature_layer(const struct clip_ctx * ctx);
-
-CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);
-
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif // CLIP_H

examples/llava/gemma3_convert_encoder_to_gguf.py

@@ -1,307 +0,0 @@
-import gguf
-import argparse
-import logging
-import sys
-import torch
-import json
-import os
-import numpy as np
-from typing import cast, ContextManager, Any, Iterator
-from pathlib import Path
-from torch import Tensor
-
-logger = logging.getLogger("gemma3-mmproj")
-
-
-# (copied from convert_hf_to_gguf.py)
-# tree of lazy tensors
-class LazyTorchTensor(gguf.LazyBase):
-    _tensor_type = torch.Tensor
-    # to keep the type-checker happy
-    dtype: torch.dtype
-    shape: torch.Size
-
-    # only used when converting a torch.Tensor to a np.ndarray
-    _dtype_map: dict[torch.dtype, type] = {
-        torch.float16: np.float16,
-        torch.float32: np.float32,
-    }
-
-    # used for safetensors slices
-    # ref: https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/src/lib.rs#L1046
-    # TODO: uncomment U64, U32, and U16, ref: https://github.com/pytorch/pytorch/issues/58734
-    _dtype_str_map: dict[str, torch.dtype] = {
-        "F64": torch.float64,
-        "F32": torch.float32,
-        "BF16": torch.bfloat16,
-        "F16": torch.float16,
-        # "U64": torch.uint64,
-        "I64": torch.int64,
-        # "U32": torch.uint32,
-        "I32": torch.int32,
-        # "U16": torch.uint16,
-        "I16": torch.int16,
-        "U8": torch.uint8,
-        "I8": torch.int8,
-        "BOOL": torch.bool,
-        "F8_E4M3": torch.float8_e4m3fn,
-        "F8_E5M2": torch.float8_e5m2,
-    }
-
-    def numpy(self) -> gguf.LazyNumpyTensor:
-        dtype = self._dtype_map[self.dtype]
-        return gguf.LazyNumpyTensor(
-            meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape),
-            args=(self,),
-            func=(lambda s: s.numpy())
-        )
-
-    @classmethod
-    def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: tuple[int, ...]) -> Tensor:
-        return torch.empty(size=shape, dtype=dtype, device="meta")
-
-    @classmethod
-    def from_safetensors_slice(cls, st_slice: Any) -> Tensor:
-        dtype = cls._dtype_str_map[st_slice.get_dtype()]
-        shape: tuple[int, ...] = tuple(st_slice.get_shape())
-        lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[:])
-        return cast(torch.Tensor, lazy)
-
-    @classmethod
-    def __torch_function__(cls, func, types, args=(), kwargs=None):
-        del types  # unused
-
-        if kwargs is None:
-            kwargs = {}
-
-        if func is torch.Tensor.numpy:
-            return args[0].numpy()
-
-        return cls._wrap_fn(func)(*args, **kwargs)
-
-
-class Gemma3VisionTower:
-    hparams: dict
-    gguf_writer: gguf.GGUFWriter
-    fname_out: Path
-    ftype: gguf.LlamaFileType
-
-    @staticmethod
-    def load_hparams(dir_model: Path):
-        with open(dir_model / "config.json", "r", encoding="utf-8") as f:
-            return json.load(f)
-
-    @staticmethod
-    def get_model_part_names(dir_model: Path, prefix: str, suffix: str) -> list[str]:
-        part_names: list[str] = []
-        for filename in os.listdir(dir_model):
-            if filename.startswith(prefix) and filename.endswith(suffix):
-                part_names.append(filename)
-        part_names.sort()
-        return part_names
-
-    def __init__(self,
-                 dir_model: Path,
-                 fname_out: Path,
-                 ftype: gguf.LlamaFileType,
-                 is_big_endian: bool,):
-        hparams = Gemma3VisionTower.load_hparams(dir_model)
-        self.hparams = hparams
-        self.fname_out = fname_out
-        self.ftype = ftype
-        endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
-        self.gguf_writer = gguf.GGUFWriter(path=None, arch="clip", endianess=endianess)
-
-        text_config = hparams["text_config"]
-        vision_config = hparams["vision_config"]
-
-        assert hparams["architectures"][0] == "Gemma3ForConditionalGeneration"
-        assert text_config is not None
-        assert vision_config is not None
-
-        self.gguf_writer.add_string ("clip.projector_type",              "gemma3")
-        self.gguf_writer.add_bool   ("clip.has_text_encoder",            False)
-        self.gguf_writer.add_bool   ("clip.has_vision_encoder",          True)
-        self.gguf_writer.add_bool   ("clip.has_llava_projector",         False) # legacy
-        self.gguf_writer.add_uint32 ("clip.vision.image_size",           vision_config["image_size"])
-        self.gguf_writer.add_uint32 ("clip.vision.patch_size",           vision_config["patch_size"])
-        self.gguf_writer.add_uint32 ("clip.vision.embedding_length",     vision_config["hidden_size"])
-        self.gguf_writer.add_uint32 ("clip.vision.feed_forward_length",  vision_config["intermediate_size"])
-        self.gguf_writer.add_uint32 ("clip.vision.projection_dim",       text_config["hidden_size"])
-        self.gguf_writer.add_uint32 ("clip.vision.block_count",          vision_config["num_hidden_layers"])
-        self.gguf_writer.add_uint32 ("clip.vision.attention.head_count", vision_config["num_attention_heads"])
-        self.gguf_writer.add_float32("clip.vision.attention.layer_norm_epsilon", vision_config.get("layer_norm_eps", 1e-6))
-        # default values taken from HF tranformers code
-        self.gguf_writer.add_array  ("clip.vision.image_mean", [0.5, 0.5, 0.5])
-        self.gguf_writer.add_array  ("clip.vision.image_std",  [0.5, 0.5, 0.5])
-        self.gguf_writer.add_bool   ("clip.use_gelu", True)
-
-        # load tensors
-        for name, data_torch in self.get_tensors(dir_model):
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
-            self.add_tensor(name, data_torch)
-
-    def get_tensors(self, dir_model: Path) -> Iterator[tuple[str, Tensor]]:
-        part_names = Gemma3VisionTower.get_model_part_names(dir_model, "model", ".safetensors")
-        tensor_names_from_parts: set[str] = set()
-        for part_name in part_names:
-            logger.info(f"gguf: loading model part '{part_name}'")
-            from safetensors import safe_open
-            ctx = cast(ContextManager[Any], safe_open(dir_model / part_name, framework="pt", device="cpu"))
-            with ctx as model_part:
-                tensor_names_from_parts.update(model_part.keys())
-
-                for name in model_part.keys():
-                    data = model_part.get_slice(name)
-                    data = LazyTorchTensor.from_safetensors_slice(data)
-                    yield name, data
-
-    def add_tensor(self, name: str, data_torch: Tensor):
-        is_1d = len(data_torch.shape) == 1
-        is_embd = ".embeddings." in name
-        old_dtype = data_torch.dtype
-        can_quantize = not is_1d and not is_embd
-        data_qtype = gguf.GGMLQuantizationType.F32
-
-        # this is to support old checkpoint
-        # TODO: remove this when we have the final model
-        name = name.replace("vision_model.vision_model.", "vision_tower.vision_model.")
-        name = name.replace("multimodal_projector.", "multi_modal_projector.")
-
-        # filter only vision tensors
-        if not name.startswith("vision_tower.vision_model.") and not name.startswith("multi_modal_projector."):
-            return
-        # prefix
-        name = name.replace("vision_tower.vision_model.encoder.layers.", "v.blk.")
-        name = name.replace("vision_tower.vision_model.", "v.")
-        # projector and input embd
-        name = name.replace(".embeddings.patch_embedding.", ".patch_embd.")
-        name = name.replace(".embeddings.position_embedding.", ".position_embd.")
-        name = name.replace(
-            "multi_modal_projector.mm_input_projection_weight",
-            "mm.input_projection.weight"
-        )
-        name = name.replace(
-            "multi_modal_projector.mm_soft_emb_norm.weight",
-            "mm.soft_emb_norm.weight"
-        )
-        name = name.replace("post_layernorm.", "post_ln.")
-        # each block
-        name = name.replace(".self_attn.k_proj.", ".attn_k.")
-        name = name.replace(".self_attn.v_proj.", ".attn_v.")
-        name = name.replace(".self_attn.q_proj.", ".attn_q.")
-        name = name.replace(".self_attn.out_proj.", ".attn_out.")
-        name = name.replace(".layer_norm1.", ".ln1.")
-        name = name.replace(".layer_norm2.", ".ln2.")
-        name = name.replace(".mlp.fc1.", ".ffn_down.")
-        name = name.replace(".mlp.fc2.", ".ffn_up.")
-
-        if can_quantize:
-            if self.ftype == gguf.LlamaFileType.ALL_F32:
-                data_qtype = gguf.GGMLQuantizationType.F32
-            elif self.ftype == gguf.LlamaFileType.MOSTLY_F16:
-                data_qtype = gguf.GGMLQuantizationType.F16
-            elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
-                data_qtype = gguf.GGMLQuantizationType.BF16
-            elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0:
-                data_qtype = gguf.GGMLQuantizationType.Q8_0
-            else:
-                raise ValueError(f"Unsupported file type: {self.ftype}")
-
-        # corrent norm value ; only this "soft_emb_norm" need to be corrected as it's part of Gemma projector
-        # the other norm values are part of SigLIP model, and they are already correct
-        # ref code: Gemma3RMSNorm
-        if "soft_emb_norm.weight" in name:
-            logger.info(f"Correcting norm value for '{name}'")
-            data_torch = data_torch + 1
-
-        data = data_torch.numpy()
-
-        try:
-            data = gguf.quants.quantize(data, data_qtype)
-        except Exception as e:
-            logger.error(f"Error quantizing tensor '{name}': {e}, fallback to F16")
-            data_qtype = gguf.GGMLQuantizationType.F16
-            data = gguf.quants.quantize(data, data_qtype)
-
-        # reverse shape to make it similar to the internal ggml dimension order
-        shape_str = f"{{{', '.join(str(n) for n in reversed(data_torch.shape))}}}"
-        logger.info(f"{f'%-32s' % f'{name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
-
-        self.gguf_writer.add_tensor(name, data, raw_dtype=data_qtype)
-
-    def write(self):
-        self.gguf_writer.write_header_to_file(path=self.fname_out)
-        self.gguf_writer.write_kv_data_to_file()
-        self.gguf_writer.write_tensors_to_file(progress=True)
-        self.gguf_writer.close()
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Convert Gemma 3 vision tower safetensors to GGUF format",)
-    parser.add_argument(
-        "--outfile", type=Path, default="mmproj.gguf",
-        help="path to write to",
-    )
-    parser.add_argument(
-        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0"], default="f16",
-        help="output format",
-    )
-    parser.add_argument(
-        "--bigendian", action="store_true",
-        help="model is executed on big endian machine",
-    )
-    parser.add_argument(
-        "model", type=Path,
-        help="directory containing model file",
-        nargs="?",
-    )
-    parser.add_argument(
-        "--verbose", action="store_true",
-        help="increase output verbosity",
-    )
-
-    args = parser.parse_args()
-    if args.model is None:
-        parser.error("the following arguments are required: model")
-    return args
-
-
-def main() -> None:
-    args = parse_args()
-
-    if args.verbose:
-        logging.basicConfig(level=logging.DEBUG)
-    else:
-        logging.basicConfig(level=logging.INFO)
-
-    dir_model = args.model
-
-    if not dir_model.is_dir():
-        logger.error(f'Error: {args.model} is not a directory')
-        sys.exit(1)
-
-    ftype_map: dict[str, gguf.LlamaFileType] = {
-        "f32": gguf.LlamaFileType.ALL_F32,
-        "f16": gguf.LlamaFileType.MOSTLY_F16,
-        "bf16": gguf.LlamaFileType.MOSTLY_BF16,
-        "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
-    }
-
-    logger.info(f"Loading model: {dir_model.name}")
-
-    with torch.inference_mode():
-        gemma3_vision_tower = Gemma3VisionTower(
-            dir_model=dir_model,
-            fname_out=args.outfile,
-            ftype=ftype_map[args.outtype],
-            is_big_endian=args.bigendian,
-        )
-        gemma3_vision_tower.write()
-
-
-if __name__ == '__main__':
-    main()
-

examples/llava/llava-cli.cpp

@@ -1,332 +0,0 @@
-#include "arg.h"
-#include "base64.hpp"
-#include "log.h"
-#include "common.h"
-#include "sampling.h"
-#include "clip.h"
-#include "llava.h"
-#include "llama.h"
-#include "ggml.h"
-
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <vector>
-
-static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past) {
-    int N = (int) tokens.size();
-    for (int i = 0; i < N; i += n_batch) {
-        int n_eval = (int) tokens.size() - i;
-        if (n_eval > n_batch) {
-            n_eval = n_batch;
-        }
-        if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) {
-            LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
-            return false;
-        }
-        *n_past += n_eval;
-    }
-    return true;
-}
-
-static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
-    std::vector<llama_token> tokens;
-    tokens.push_back(id);
-    return eval_tokens(ctx_llama, tokens, 1, n_past);
-}
-
-static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
-    std::string              str2     = str;
-    std::vector<llama_token> embd_inp = common_tokenize(ctx_llama, str2, add_bos, true);
-    eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
-    return true;
-}
-
-static const char * sample(struct common_sampler * smpl,
-                           struct llama_context * ctx_llama,
-                           int * n_past) {
-    const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
-    common_sampler_accept(smpl, id, true);
-
-    const llama_model * model = llama_get_model(ctx_llama);
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
-    static std::string ret;
-    if (llama_vocab_is_eog(vocab, id)) {
-        ret = "</s>";
-    } else {
-        ret = common_token_to_piece(ctx_llama, id);
-    }
-    eval_id(ctx_llama, id, n_past);
-    return ret.c_str();
-}
-
-static const char* IMG_BASE64_TAG_BEGIN = "<img src=\"data:image/jpeg;base64,";
-static const char* IMG_BASE64_TAG_END = "\">";
-
-static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) {
-    begin_out = prompt.find(IMG_BASE64_TAG_BEGIN);
-    end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out);
-}
-
-static bool prompt_contains_image(const std::string& prompt) {
-    size_t begin, end;
-    find_image_tag_in_prompt(prompt, begin, end);
-    return (begin != std::string::npos);
-}
-
-// replaces the base64 image tag in the prompt with `replacement`
-static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) {
-    size_t img_base64_str_start, img_base64_str_end;
-    find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
-    if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
-        LOG_ERR("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
-        return NULL;
-    }
-
-    auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN);
-    auto base64_bytes_count = img_base64_str_end - base64_bytes_start;
-    auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count );
-
-    auto required_bytes = base64::required_encode_size(base64_str.size());
-    auto img_bytes = std::vector<unsigned char>(required_bytes);
-    base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin());
-
-    auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
-    if (!embed) {
-        LOG_ERR("%s: could not load image from base64 string.\n", __func__);
-        return NULL;
-    }
-
-    return embed;
-}
-
-static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") {
-    size_t begin, end;
-    find_image_tag_in_prompt(prompt, begin, end);
-    if (begin == std::string::npos || end == std::string::npos) {
-        return prompt;
-    }
-    auto pre = prompt.substr(0, begin);
-    auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END));
-    return pre + replacement + post;
-}
-
-struct llava_context {
-    struct clip_ctx * ctx_clip = NULL;
-    struct llama_context * ctx_llama = NULL;
-    struct llama_model * model = NULL;
-};
-
-static void print_usage(int, char ** argv) {
-    LOG("\n example usage:\n");
-    LOG("\n     %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
-    LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
-}
-
-static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) {
-
-    // load and preprocess the image
-    llava_image_embed * embed = NULL;
-    auto prompt = params->prompt;
-    if (prompt_contains_image(prompt)) {
-        if (!params->image.empty()) {
-            LOG_INF("using base64 encoded image instead of command line image path\n");
-        }
-        embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt);
-        if (!embed) {
-            LOG_ERR("%s: can't load image from prompt\n", __func__);
-            return NULL;
-        }
-        params->prompt = remove_image_from_prompt(prompt);
-    } else {
-        embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str());
-        if (!embed) {
-            fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str());
-            return NULL;
-        }
-    }
-
-    return embed;
-}
-
-static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) {
-    int n_past = 0;
-
-    const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
-
-    std::string system_prompt, user_prompt;
-    size_t image_pos = prompt.find("<image>");
-    if (image_pos != std::string::npos) {
-        // new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
-        system_prompt = prompt.substr(0, image_pos);
-        user_prompt = prompt.substr(image_pos + std::string("<image>").length());
-        LOG_INF("system_prompt: %s\n", system_prompt.c_str());
-        if (params->verbose_prompt) {
-            auto tmp = common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
-            for (int i = 0; i < (int) tmp.size(); i++) {
-                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
-            }
-        }
-        LOG_INF("user_prompt: %s\n", user_prompt.c_str());
-        if (params->verbose_prompt) {
-            auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
-            for (int i = 0; i < (int) tmp.size(); i++) {
-                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
-            }
-        }
-    } else {
-        // llava-1.5 native mode
-        system_prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:";
-        user_prompt = prompt + "\nASSISTANT:";
-        if (params->verbose_prompt) {
-            auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
-            for (int i = 0; i < (int) tmp.size(); i++) {
-                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
-            }
-        }
-    }
-
-    eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, true);
-    llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past);
-    eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false);
-
-    // generate the response
-
-    LOG("\n");
-
-    struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling);
-    if (!smpl) {
-        LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
-        exit(1);
-    }
-
-    std::string response = "";
-    for (int i = 0; i < max_tgt_len; i++) {
-        const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past);
-        response += tmp;
-        if (strcmp(tmp, "</s>") == 0) break;
-        if (strstr(tmp, "###")) break; // Yi-VL behavior
-        LOG("%s", tmp);
-        if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works)
-        if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6
-        if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6
-
-        fflush(stdout);
-    }
-
-    common_sampler_free(smpl);
-    LOG("\n");
-}
-
-static struct llama_model * llava_init(common_params * params) {
-    llama_backend_init();
-    llama_numa_init(params->numa);
-
-    llama_model_params model_params = common_model_params_to_llama(*params);
-
-    llama_model * model = llama_model_load_from_file(params->model.path.c_str(), model_params);
-    if (model == NULL) {
-        LOG_ERR("%s: unable to load model\n" , __func__);
-        return NULL;
-    }
-    return model;
-}
-
-static struct llava_context * llava_init_context(common_params * params, llama_model * model) {
-    const char * clip_path = params->mmproj.path.c_str();
-
-    auto prompt = params->prompt;
-    if (prompt.empty()) {
-        prompt = "describe the image in detail.";
-    }
-
-    auto ctx_clip = clip_model_load(clip_path, GGML_LOG_LEVEL_INFO);
-
-    llama_context_params ctx_params = common_context_params_to_llama(*params);
-    ctx_params.n_ctx           = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
-
-    llama_context * ctx_llama = llama_init_from_model(model, ctx_params);
-
-    if (ctx_llama == NULL) {
-        LOG_ERR("%s: failed to create the llama_context\n" , __func__);
-        return NULL;
-    }
-
-    auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context));
-
-    ctx_llava->ctx_llama = ctx_llama;
-    ctx_llava->ctx_clip = ctx_clip;
-    ctx_llava->model = model;
-    return ctx_llava;
-}
-
-static void llava_free(struct llava_context * ctx_llava) {
-    if (ctx_llava->ctx_clip) {
-        clip_free(ctx_llava->ctx_clip);
-        ctx_llava->ctx_clip = NULL;
-    }
-
-    llama_free(ctx_llava->ctx_llama);
-    llama_model_free(ctx_llava->model);
-    llama_backend_free();
-}
-
-int main(int argc, char ** argv) {
-    ggml_time_init();
-
-    common_params params;
-
-    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
-        return 1;
-    }
-
-    common_init();
-
-    if (params.mmproj.path.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
-        print_usage(argc, argv);
-        return 1;
-    }
-
-    auto * model = llava_init(&params);
-    if (model == NULL) {
-        fprintf(stderr, "%s: error: failed to init llava model\n", __func__);
-        return 1;
-    }
-
-    if (prompt_contains_image(params.prompt)) {
-        auto * ctx_llava = llava_init_context(&params, model);
-
-        auto * image_embed = load_image(ctx_llava, &params, "");
-
-        // process the prompt
-        process_prompt(ctx_llava, image_embed, &params, params.prompt);
-
-        llama_perf_context_print(ctx_llava->ctx_llama);
-        llava_image_embed_free(image_embed);
-        ctx_llava->model = NULL;
-        llava_free(ctx_llava);
-    } else {
-        for (auto & image : params.image) {
-            auto * ctx_llava = llava_init_context(&params, model);
-
-            auto * image_embed = load_image(ctx_llava, &params, image);
-            if (!image_embed) {
-                LOG_ERR("%s: failed to load image %s. Terminating\n\n", __func__, image.c_str());
-                return 1;
-            }
-
-            // process the prompt
-            process_prompt(ctx_llava, image_embed, &params, params.prompt);
-
-            llama_perf_context_print(ctx_llava->ctx_llama);
-            llava_image_embed_free(image_embed);
-            ctx_llava->model = NULL;
-            llava_free(ctx_llava);
-        }
-    }
-
-    llama_model_free(model);
-
-    return 0;
-}

examples/llava/llava.cpp

@@ -1,585 +0,0 @@
-#include "clip.h"
-#include "llava.h"
-
-#include "llama.h"
-
-#include <algorithm>
-#include <cerrno>
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <limits>
-#include <vector>
-#include <memory>
-
-#if defined(LLAVA_LOG_OFF)
-#   define LOG_INF(...)
-#   define LOG_WRN(...)
-#   define LOG_ERR(...)
-#   define LOG_DBG(...)
-#else // defined(LLAVA_LOG_OFF)
-#   define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0)
-#   define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0)
-#   define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0)
-#   define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0)
-#endif // defined(LLAVA_LOG_OFF)
-
-// RGB uint8 image
-struct clip_image_u8 {
-    int nx;
-    int ny;
-
-    std::vector<uint8_t> buf;
-};
-
-// RGB float32 image (NHWC)
-// Memory layout: RGBRGBRGB...
-struct clip_image_f32 {
-    int nx;
-    int ny;
-
-    std::vector<float> buf;
-};
-
-struct clip_image_grid_shape {
-    int first;
-    int second;
-};
-
-// convenience cpp wrapper
-struct clip_image_f32_batch_deleter {
-    void operator()(clip_image_f32_batch * val) { clip_image_f32_batch_free(val); }
-};
-typedef std::unique_ptr<clip_image_f32_batch, clip_image_f32_batch_deleter> clip_image_f32_batch_ptr;
-
-struct clip_image_size_deleter {
-    void operator()(clip_image_f32_batch * val) { clip_image_f32_batch_free(val); }
-};
-typedef std::unique_ptr<clip_image_size, clip_image_size_deleter> clip_image_size_ptr;
-
-/**
- * Selects the best resolution from a list of possible resolutions based on the original size.
- *
- * @param original_size The original size of the image in the format (width, height).
- * @param possible_resolutions A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
- * @return The best fit resolution in the format (width, height).
- */
-static std::pair<int, int> select_best_resolution(const std::pair<int, int>& original_size, const std::vector<std::pair<int, int>>& possible_resolutions) {
-    int original_width  = original_size.first;
-    int original_height = original_size.second;
-
-    std::pair<int, int> best_fit;
-    int max_effective_resolution = 0;
-    int min_wasted_resolution = std::numeric_limits<int>::max();
-
-    for (const auto& resolution : possible_resolutions) {
-        int width = resolution.first;
-        int height = resolution.second;
-        float scale = std::min(static_cast<float>(width) / original_width, static_cast<float>(height) / original_height);
-        int downscaled_width  = static_cast<int>(original_width * scale);
-        int downscaled_height = static_cast<int>(original_height * scale);
-        int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
-        int wasted_resolution = (width * height) - effective_resolution;
-        // LOG_DBG("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
-        if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
-            max_effective_resolution = effective_resolution;
-            min_wasted_resolution = wasted_resolution;
-            best_fit = resolution;
-        }
-    }
-
-    return best_fit;
-}
-
-/**
- * @brief Get the anyres image grid shape object
- *
- * @param image_size
- * @param grid_pinpoints
- * @param image_patch_size
- * @return <int, int>
- */
-static struct clip_image_grid_shape get_anyres_image_grid_shape(const std::pair<int, int> & image_size, const std::vector<std::pair<int, int>> & grid_pinpoints, int image_patch_size) {
-    /**
-        Conversion from gguf flat array to vector:
-        std::vector<std::pair<int, int>> possible_resolutions;
-        for (int i = 0; i < 32 && params.image_grid_pinpoints[i] != 0; i+=2) {
-            possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]});
-        }
-     */
-    auto best_resolution = select_best_resolution(image_size, grid_pinpoints);
-    return {best_resolution.first / image_patch_size, best_resolution.second / image_patch_size};
-}
-
-// Take the image segments in a grid configuration and return the embeddings and the number of embeddings into preallocated memory (image_embd_out)
-static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *> & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out) {
-    struct {
-        struct ggml_context * ctx;
-    } model;
-
-    const int32_t image_size = clip_get_image_size(ctx_clip);
-    const int32_t patch_size = clip_get_patch_size(ctx_clip);
-
-    int32_t num_patches_per_side = image_size / patch_size; // 336 / 14 = 24 - used for embedding-patching boxes (24*24 = 576 patches)
-
-    int num_patches_width  = grid_shape.first;  // grid 1-4
-    int num_patches_height = grid_shape.second; // grid 1-4
-
-    const size_t num_images = num_patches_width * num_patches_height + 1;
-
-    // TODO: size calculation is not calculated - it's only tens of MB
-    size_t ctx_size = 0;
-
-    {
-        ctx_size += clip_embd_nbytes(ctx_clip) * num_images * 8; // image_features
-        ctx_size += 1024*1024 * ggml_type_size(GGML_TYPE_F32);
-    }
-
-    struct ggml_init_params params {
-        /*.mem_size   =*/ ctx_size,
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ false, // NOTE: this should be false when using the legacy API
-    };
-
-    // Python reference code for full unpad:
-    /*
-        base_image_feature = image_feature[0]
-        image_feature = image_feature[1:]
-        image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
-        image_feature = image_feature.flatten(1, 2).flatten(2, 3)
-        image_feature = unpad_image(image_feature, image_sizes[image_idx])
-        image_feature = torch.cat((
-            image_feature,
-            self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1)
-        ), dim=-1)
-        image_feature = image_feature.flatten(1, 2).transpose(0, 1)
-        image_feature = torch.cat((base_image_feature, image_feature), dim=0)
-    */
-    // We now have two options: unpad or no unpad. Unpad removes tokens for faster llm eval.
-    // In terms of result quality it appears to make no difference, so we'll start with the easier approach given 5D tensors are not supported in ggml yet.
-    // Without unpad we have to split the sub-image embeddings into patches of 24 features each and permute them.
-    // Once all images are processed to prepended the base_image_features without any changes.
-
-    // Pytorch reference simplified, modified for ggml compatibility - confirmed identical output in python (for a 2x2 grid image (676x676 scaling))
-    /*
-        image_feature = image_feature.view(2, 2, 24, 24, 4096)
-        image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
-        image_feature = image_feature.view(2, 24, 2, 24, 4096)
-        image_feature = image_feature.flatten(0, 3)
-
-        // Reshape to 4D tensor by merging the last two dimensions
-        image_feature = image_feature.view(2, 2, 24, 24*4096)
-        image_feature = image_feature.permute(0, 2, 1, 3).contiguous()
-        image_feature = image_feature.view(-1, 4096)
-    */
-
-    model.ctx = ggml_init(params);
-
-    struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_patches(ctx_clip), num_images - 1); // example: 4096 x 576 x 4
-    // ggml_tensor_printf(image_features,"image_features",__LINE__,false,false);
-    // fill it with the image embeddings, ignoring the base
-    for (size_t i = 1; i < num_images; i++) {
-        size_t offset = (i-1) * clip_embd_nbytes(ctx_clip);
-        memcpy((uint8_t *)(image_features->data) + offset, image_embd_v[i], clip_embd_nbytes(ctx_clip));
-    }
-
-    struct ggml_cgraph  * gf = ggml_new_graph(model.ctx);
-    size_t size_ele = ggml_type_size(GGML_TYPE_F32);
-
-    struct ggml_tensor *image_features_patchview = ggml_view_4d(model.ctx, image_features,
-                                                                num_patches_per_side * clip_n_mmproj_embd(ctx_clip),
-                                                                num_patches_per_side,
-                                                                num_patches_width,
-                                                                num_patches_height,
-                                                                size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip),
-                                                                size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side,
-                                                                size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side * num_patches_width, 0);
-    // ggml_tensor_printf(image_features_patchview,"image_features_patchview",__LINE__,false,false);
-    struct ggml_tensor *permuted_cont = ggml_cont(model.ctx, ggml_permute(model.ctx, image_features_patchview, 0, 2, 1, 3));
-    /**
-     At the end of each row we have to add the row_end embeddings, which are the same as the newline embeddings
-         image_feature = torch.cat((
-        image_feature,
-        self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)
-    ), dim=-1)
-     *
-     */
-
-    // ggml_tensor_printf(permuted_cont,"permuted_cont",__LINE__,false,false);
-    struct ggml_tensor *flatten = ggml_view_2d(model.ctx, permuted_cont, clip_n_mmproj_embd(ctx_clip), num_patches_height * num_patches_width * num_patches_per_side * num_patches_per_side,  size_ele * clip_n_mmproj_embd(ctx_clip), 0);
-    // ggml_tensor_printf(flatten,"flatten",__LINE__,false,false);
-    ggml_build_forward_expand(gf, flatten);
-    ggml_graph_compute_with_ctx(model.ctx, gf, 1);
-    struct ggml_tensor* result = ggml_graph_node(gf, -1);
-
-    memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context
-    // append without newline tokens (default behavior in llava_arch when not using unpad ):
-    memcpy(image_embd_out + clip_n_patches(ctx_clip) * clip_n_mmproj_embd(ctx_clip), (float*)result->data, clip_embd_nbytes(ctx_clip) * (num_images-1)); // grid patches
-    *n_img_pos_out = static_cast<int>(result->ne[1]+clip_n_patches(ctx_clip));
-
-    // Debug: Test single segments
-    // Current findings: sending base image, sending a segment embedding all works similar to python
-    // However, permuted embeddings do not work yet (stride issue?)
-    // memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as context
-    // memcpy(image_embd_out, (float*)prepared_cont->data, clip_embd_nbytes(ctx_clip)); // main image as context
-    // *n_img_pos_out=576;
-
-    ggml_free(model.ctx);
-    return true;
-}
-
-static clip_image_f32 * reshape_by_patch(clip_image_f32 * image, int patch_size) {
-    int width = image->nx;
-    int height = image->ny;
-    int num_patches = (height / patch_size) * (width / patch_size);
-    clip_image_f32 * patch = clip_image_f32_init();
-    patch->nx = patch_size * num_patches;
-    patch->ny = patch_size;
-    patch->buf.resize(3 * patch->nx * patch->ny);
-
-    int patch_index = 0;
-
-    for (int i = 0; i < height; i += patch_size) {
-        for (int j = 0; j < width; j += patch_size) {
-            for (int pi = 0; pi < patch_size; ++pi) {
-                for (int pj = 0; pj < patch_size; ++pj) {
-                    int input_index = ((i + pi) * width + (j + pj)) * 3;
-                    int output_index = (pi * patch_size * num_patches + patch_index * patch_size + pj) * 3;
-                    patch->buf[output_index] = image->buf[input_index];
-                    patch->buf[output_index+1] = image->buf[input_index+1];
-                    patch->buf[output_index+2] = image->buf[input_index+2];
-                }
-            }
-            patch_index++;
-        }
-    }
-    return patch;
-}
-
-static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {
-    // std::vector<clip_image_f32*> img_res_v; // format VectN x H x W x RGB (N x 336 x 336 x 3), so interleaved RGB - different to the python implementation which is N x 3 x 336 x 336
-    clip_image_f32_batch_ptr img_res_v(clip_image_f32_batch_init());
-    if (!clip_image_preprocess(ctx_clip, img, img_res_v.get())) {
-        LOG_ERR("%s: unable to preprocess image\n", __func__);
-        return false;
-    }
-
-    const int64_t t_img_enc_start_us = ggml_time_us();
-
-    const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip);
-
-    const size_t n_imgs = clip_image_f32_batch_n_images(img_res_v.get());
-
-    if (clip_is_minicpmv(ctx_clip) || clip_is_qwen2vl(ctx_clip)) {
-        std::vector<float *> image_embd_v;
-        image_embd_v.resize(n_imgs);
-        clip_image_size load_image_size;
-
-        for (size_t i = 0; i < n_imgs; i++) {
-            const int64_t t_img_enc_step_start_us = ggml_time_us();
-            int nx = clip_image_f32_batch_nx(img_res_v.get(), i);
-            int ny = clip_image_f32_batch_ny(img_res_v.get(), i);
-            image_embd_v[i] = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, nx, ny));
-            int patch_size = 14;
-            load_image_size.width = nx;
-            load_image_size.height = ny;
-            clip_add_load_image_size(ctx_clip, &load_image_size);
-
-            bool encoded = false;
-            clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i);
-            if (clip_is_qwen2vl(ctx_clip)) {
-                encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd_v[i]);
-            }
-            else {
-                encoded = clip_image_encode(ctx_clip, n_threads, reshape_by_patch(img_res, patch_size), image_embd_v[i]);
-            }
-
-            if (!encoded) {
-                LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) n_imgs);
-                return false;
-            }
-            const int64_t t_img_enc_steop_batch_us = ggml_time_us();
-            LOG_INF("%s: step %d of %d encoded in %8.2f ms\n", __func__, (int)i+1, (int)n_imgs, (t_img_enc_steop_batch_us - t_img_enc_step_start_us) / 1000.0);
-        }
-        const int64_t t_img_enc_batch_us = ggml_time_us();
-        LOG_INF("%s: all %d segments encoded in %8.2f ms\n", __func__, (int)n_imgs, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
-
-        int n_img_pos_out = 0;
-        for (size_t i = 0; i < image_embd_v.size(); i++) {
-            int nx = clip_image_f32_batch_nx(img_res_v.get(), i);
-            int ny = clip_image_f32_batch_ny(img_res_v.get(), i);
-            clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i);
-            std::memcpy(
-                image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip),
-                image_embd_v[i],
-                clip_embd_nbytes_by_img(ctx_clip, nx, ny));
-            n_img_pos_out += clip_n_patches_by_img(ctx_clip, img_res);
-        }
-        *n_img_pos = n_img_pos_out;
-        for (size_t i = 0; i < image_embd_v.size(); i++) {
-            free(image_embd_v[i]);
-        }
-        image_embd_v.clear();
-        load_image_size.width = img->nx;
-        load_image_size.height = img->ny;
-        clip_add_load_image_size(ctx_clip, &load_image_size);
-        LOG_INF("%s: load_image_size %d %d\n", __func__, load_image_size.width, load_image_size.height);
-    }
-    else if (clip_is_glm(ctx_clip)){
-        struct clip_image_size * load_image_size = clip_image_size_init();
-        load_image_size->width  = clip_image_f32_batch_nx(img_res_v.get(), 0);
-        load_image_size->height = clip_image_f32_batch_ny(img_res_v.get(), 0);
-        clip_add_load_image_size(ctx_clip, load_image_size);
-
-        clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), 0);
-        bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd);
-        int pos = int(load_image_size->width/clip_get_patch_size(ctx_clip)/2);
-        *n_img_pos = (pos * pos + 2);
-        if (!encoded){
-            LOG_ERR("Unable to encode image \n");
-            return false;
-        }
-    }
-    else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
-        // flat / default llava-1.5 type embedding
-        *n_img_pos = clip_n_patches(ctx_clip);
-        clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), 0);
-        bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd); // image_embd shape is 576 x 4096
-        if (!encoded) {
-            LOG_ERR("Unable to encode image\n");
-
-            return false;
-        }
-    }
-    else {
-        // spatial_unpad llava-1.6 type embedding
-        // TODO: CLIP needs batching support - in HF the llm projection is separate after encoding, which might be a solution to quickly get batching working
-        std::vector<float *> image_embd_v;
-        image_embd_v.resize(n_imgs);
-        for (size_t i = 0; i < n_imgs; i++) {
-            clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i);
-            image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
-            const bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
-            if (!encoded) {
-                LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) n_imgs);
-                return false;
-            }
-        }
-        const int64_t t_img_enc_batch_us = ggml_time_us();
-        LOG_INF("%s: %d segments encoded in %8.2f ms\n", __func__, (int)n_imgs, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
-
-        const int32_t * image_grid = clip_image_grid(ctx_clip);
-        const size_t num_gridpoints = get_clip_image_grid_size(ctx_clip);
-
-        std::vector<std::pair<int, int>> grid_pinpoints;
-        for (size_t i = 0; i < num_gridpoints; i += 2) {
-            grid_pinpoints.push_back({image_grid[i], image_grid[i+1]});
-        }
-
-        const int32_t image_size = clip_get_image_size(ctx_clip);
-
-        struct clip_image_grid_shape grid_shape = get_anyres_image_grid_shape({img->nx,img->ny}, grid_pinpoints, image_size);
-
-        int n_img_pos_out;
-        clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out);
-        *n_img_pos = n_img_pos_out;
-
-        for (size_t i = 0; i < image_embd_v.size(); i++) {
-            free(image_embd_v[i]);
-        }
-        image_embd_v.clear();
-
-        // debug image/segment/normalization content:
-        // clip_image_u8 * tmp = clip_image_u8_init();
-        // clip_image_convert_f32_to_u8(*image_feature, *tmp);
-        // clip_image_save_to_bmp(*tmp, "image_feature.bmp");
-    }
-
-    LOG_INF("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
-
-    const int64_t t_img_enc_end_us = ggml_time_us();
-    float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
-
-    LOG_INF("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
-
-    return true;
-}
-
-bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip) {
-        // make sure that the correct mmproj was used, i.e., compare apples to apples
-    int n_llama_embd = llama_model_n_embd(llama_get_model(ctx_llama));
-    auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
-    if (n_image_embd != n_llama_embd) {
-        LOG_ERR("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
-        return false;
-    }
-    return true;
-}
-
-bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
-    // Granite vision uses up to 10 patches + base patch
-    int num_max_patches = 11;
-    if (clip_is_minicpmv(ctx_clip)) {
-        num_max_patches = 10;
-    }
-    if (clip_is_glm(ctx_clip)) {
-        num_max_patches = 1;
-    }
-    float * image_embd;
-    if (clip_is_qwen2vl(ctx_clip)) {
-        // qwen2vl don't split image into chunks, so `num_max_patches` is not needed.
-        image_embd = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img->nx, img->ny));
-    } else {
-        image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model
-    }
-    if (!image_embd) {
-        LOG_ERR("Unable to allocate memory for image embeddings\n");
-        return false;
-    }
-
-    int n_img_pos;
-    if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {
-        LOG_ERR("%s: cannot encode image, aborting\n", __func__);
-        free(image_embd);
-        return false;
-    }
-    *image_embd_out = image_embd;
-    *n_img_pos_out = n_img_pos;
-
-    return true;
-}
-
-struct llava_embd_batch {
-    std::vector<llama_pos>      pos;
-    std::vector<int32_t>        n_seq_id;
-    std::vector<llama_seq_id>   seq_id_0;
-    std::vector<llama_seq_id *> seq_ids;
-    std::vector<int8_t>         logits;
-    llama_batch batch;
-    llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
-        pos     .resize(n_tokens);
-        n_seq_id.resize(n_tokens);
-        seq_ids .resize(n_tokens + 1);
-        logits  .resize(n_tokens);
-        seq_id_0.resize(1);
-        seq_id_0[0] = seq_id;
-        seq_ids [n_tokens] = nullptr;
-        batch = {
-            /*n_tokens       =*/ n_tokens,
-            /*tokens         =*/ nullptr,
-            /*embd           =*/ embd,
-            /*pos            =*/ pos.data(),
-            /*n_seq_id       =*/ n_seq_id.data(),
-            /*seq_id         =*/ seq_ids.data(),
-            /*logits         =*/ logits.data(),
-        };
-        for (int i = 0; i < n_tokens; i++) {
-            batch.pos     [i] = pos_0 + i;
-            batch.n_seq_id[i] = 1;
-            batch.seq_id  [i] = seq_id_0.data();
-            batch.logits  [i] = false;
-        }
-    }
-};
-
-bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) {
-    int n_embd  = llama_model_n_embd(llama_get_model(ctx_llama));
-
-    for (int i = 0; i < image_embed->n_image_pos; i += n_batch) {
-        int n_eval = image_embed->n_image_pos - i;
-        if (n_eval > n_batch) {
-            n_eval = n_batch;
-        }
-        float * embd = image_embed->embed+i*n_embd;
-        llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0);
-        if (llama_decode(ctx_llama, llava_batch.batch)) {
-            LOG_ERR("%s : failed to eval\n", __func__);
-            return false;
-        }
-        *n_past += n_eval;
-    }
-    return true;
-}
-
-struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) {
-    clip_image_u8 * img = clip_image_u8_init();
-    if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
-        clip_image_u8_free(img);
-        LOG_ERR("%s: can't load image from bytes, is it a valid image?", __func__);
-        return NULL;
-    }
-
-    float* image_embed = NULL;
-    int n_image_pos = 0;
-    bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
-    if (!image_embed_result) {
-        clip_image_u8_free(img);
-        LOG_ERR("%s: couldn't embed the image\n", __func__);
-        return NULL;
-    }
-
-    clip_image_u8_free(img);
-    auto result = (llava_image_embed*)malloc(sizeof(llava_image_embed));
-    result->embed = image_embed;
-    result->n_image_pos = n_image_pos;
-    return result;
-}
-
-static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) {
-    auto file = fopen(path, "rb");
-    if (file == NULL) {
-        LOG_ERR("%s: can't read file %s\n", __func__, path);
-        return false;
-    }
-
-    fseek(file, 0, SEEK_END);
-    auto fileSize = ftell(file);
-    fseek(file, 0, SEEK_SET);
-
-    auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data
-    if (buffer == NULL) {
-        LOG_ERR("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
-        perror("Memory allocation error");
-        fclose(file);
-        return false;
-    }
-    errno = 0;
-    size_t ret = fread(buffer, 1, fileSize, file); // Read the file into the buffer
-    if (ferror(file)) {
-        LOG_ERR("read error: %s", strerror(errno));
-        free(buffer);
-        fclose(file);
-        return false;
-    }
-    if (ret != (size_t) fileSize) {
-        LOG_ERR("unexpectedly reached end of file");
-        free(buffer);
-        fclose(file);
-        return false;
-    }
-    fclose(file); // Close the file
-
-    *bytesOut = buffer;
-    *sizeOut = fileSize;
-    return true;
-}
-
-struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) {
-    unsigned char* image_bytes;
-    long image_bytes_length;
-    auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
-    if (!loaded) {
-        LOG_ERR("%s: failed to load %s\n", __func__, image_path);
-        return NULL;
-    }
-
-    llava_image_embed *embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length);
-    free(image_bytes);
-
-    return embed;
-}
-
-void llava_image_embed_free(struct llava_image_embed * embed) {
-    free(embed->embed);
-    free(embed);
-}

examples/llava/llava.h

@@ -1,49 +0,0 @@
-#ifndef LLAVA_H
-#define LLAVA_H
-
-#include "ggml.h"
-
-#ifdef LLAMA_SHARED
-#    if defined(_WIN32) && !defined(__MINGW32__)
-#        ifdef LLAMA_BUILD
-#            define LLAVA_API __declspec(dllexport)
-#        else
-#            define LLAVA_API __declspec(dllimport)
-#        endif
-#    else
-#        define LLAVA_API __attribute__ ((visibility ("default")))
-#    endif
-#else
-#    define LLAVA_API
-#endif
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-struct clip_ctx;
-struct llava_image_embed {
-    float * embed;
-    int n_image_pos;
-};
-
-/** sanity check for clip <-> llava embed size match */
-LLAVA_API bool llava_validate_embed_size(const struct llama_context * ctx_llama, const struct clip_ctx * ctx_clip);
-
-LLAVA_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
-
-/** build an image embed from image file bytes */
-LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);
-/** build an image embed from a path to an image filename */
-LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path);
-/** free an embedding made with llava_image_embed_make_* */
-LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
-
-/** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */
-LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past);
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif

examples/llava/minicpmv-cli.cpp

@@ -1,354 +0,0 @@
-#include "arg.h"
-#include "log.h"
-#include "common.h"
-#include "sampling.h"
-#include "clip.h"
-#include "llava.h"
-#include "llama.h"
-#include "ggml.h"
-
-#include <algorithm>
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <vector>
-#include <iostream> // TODO: remove me
-
-struct llava_context {
-    struct clip_ctx * ctx_clip = NULL;
-    struct llama_context * ctx_llama = NULL;
-    struct llama_model * model = NULL;
-};
-
-static void show_additional_info(int /*argc*/, char ** argv) {
-    LOG("\nexample usage:\n\n%s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
-    LOG("\nnote: a lower temperature value like 0.1 is recommended for better quality.\n");
-}
-
-static struct llama_model * llava_init(common_params * params) {
-    llama_backend_init();
-    llama_numa_init(params->numa);
-
-    llama_model_params model_params = common_model_params_to_llama(*params);
-
-    llama_model * model = llama_model_load_from_file(params->model.path.c_str(), model_params);
-    if (model == NULL) {
-        LOG_ERR("%s: unable to load model\n" , __func__);
-        return NULL;
-    }
-    return model;
-}
-
-static struct llava_context * llava_init_context(common_params * params, llama_model * model) {
-    auto prompt = params->prompt;
-    if (prompt.empty()) {
-        prompt = "describe the image in detail.";
-    }
-
-    llama_context_params ctx_params = common_context_params_to_llama(*params);
-    if (params->n_ctx < 2048) {
-        // warn user here, "Image processing requires at least 2048 context, setting context to 2048"
-        LOG_WRN("%s: Image processing requires at least 2048 context, setting context to 2048\n" , __func__);
-        ctx_params.n_ctx = 2048;
-    } else {
-        ctx_params.n_ctx = params->n_ctx;
-    }
-
-    llama_context * ctx_llama = llama_init_from_model(model, ctx_params);
-
-    if (ctx_llama == NULL) {
-        LOG_ERR("%s: failed to create the llama_context\n" , __func__);
-        return NULL;
-    }
-
-    auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context));
-
-    ctx_llava->ctx_llama = ctx_llama;
-    ctx_llava->model = model;
-    return ctx_llava;
-}
-
-static void llava_free(struct llava_context * ctx_llava) {
-    if (ctx_llava->ctx_clip) {
-        clip_free(ctx_llava->ctx_clip);
-        ctx_llava->ctx_clip = NULL;
-    }
-
-    llama_free(ctx_llava->ctx_llama);
-    llama_model_free(ctx_llava->model);
-    llama_backend_free();
-}
-
-static struct clip_ctx * clip_init_context(common_params * params) {
-    const char * clip_path = params->mmproj.path.c_str();
-
-    auto prompt = params->prompt;
-    if (prompt.empty()) {
-        prompt = "describe the image in detail.";
-    }
-    struct clip_context_params clip_params = {
-        /* use_gpu */   params->n_gpu_layers != 0,
-        /* verbosity */ GGML_LOG_LEVEL_INFO, // TODO: make this configurable
-    };
-    auto * ctx_clip = clip_init(clip_path, clip_params);
-    return ctx_clip;
-}
-
-static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past) {
-    int N = (int) tokens.size();
-    for (int i = 0; i < N; i += n_batch) {
-        int n_eval = (int) tokens.size() - i;
-        if (n_eval > n_batch) {
-            n_eval = n_batch;
-        }
-        if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) {
-            LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
-            return false;
-        }
-        *n_past += n_eval;
-    }
-    return true;
-}
-
-static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
-    std::vector<llama_token> tokens;
-    tokens.push_back(id);
-    return eval_tokens(ctx_llama, tokens, 1, n_past);
-}
-
-static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
-    std::string              str2     = str;
-    std::vector<llama_token> embd_inp = common_tokenize(ctx_llama, str2, add_bos, true);
-    return eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
-}
-
-static void process_eval_image_embed(struct llava_context * ctx_llava, const struct llava_image_embed * embeds, int n_batch, int * n_past, int idx) {
-    float * image_embed = (float *)malloc(clip_embd_nbytes(ctx_llava->ctx_clip));
-    std::memcpy(image_embed, embeds->embed + idx * clip_n_patches(ctx_llava->ctx_clip) * clip_n_mmproj_embd(ctx_llava->ctx_clip), clip_embd_nbytes(ctx_llava->ctx_clip));
-
-    auto * slice_embed = (llava_image_embed*)malloc(sizeof(llava_image_embed));
-    slice_embed->embed = image_embed;
-    slice_embed->n_image_pos = clip_n_patches(ctx_llava->ctx_clip);
-    llava_eval_image_embed(ctx_llava->ctx_llama, slice_embed, n_batch, n_past);
-    llava_image_embed_free(slice_embed);
-}
-
-static void process_image(struct llava_context * ctx_llava, struct llava_image_embed * embeds, common_params * params, int &n_past) {
-    std::string system_prompt;
-    int idx = 0;
-    int num_image_embeds = embeds->n_image_pos / clip_n_patches(ctx_llava->ctx_clip);
-    int has_minicpmv_projector = clip_is_minicpmv(ctx_llava->ctx_clip);
-    if (has_minicpmv_projector == 2) {
-        system_prompt = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n";
-    }
-    else if (has_minicpmv_projector == 3) {
-        system_prompt = "<|im_start|>user\n";
-    }
-    else if (has_minicpmv_projector == 4) {
-        system_prompt = "<|im_start|>user\n";
-    }
-    LOG_INF("%s: image token past: %d\n", __func__, n_past);
-    eval_string(ctx_llava->ctx_llama, (system_prompt+"<image>").c_str(), params->n_batch, &n_past, false);
-    process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
-    eval_string(ctx_llava->ctx_llama, std::string("</image>").c_str(), params->n_batch, &n_past, false);
-    if (num_image_embeds > 1) {
-        if (has_minicpmv_projector == 2) {
-            size_t num_image_embeds_col = clip_uhd_num_image_embeds_col(ctx_llava->ctx_clip);
-            eval_string(ctx_llava->ctx_llama, std::string("<slice>").c_str(), params->n_batch, &n_past, false);
-            for (size_t i = 0; i < (num_image_embeds-1)/num_image_embeds_col; ++i) {
-                for (size_t j = 0; j < num_image_embeds_col; ++j) {
-                    eval_string(ctx_llava->ctx_llama, std::string("<image>").c_str(), params->n_batch, &n_past, false);
-                    process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
-                    eval_string(ctx_llava->ctx_llama, std::string("</image>").c_str(), params->n_batch, &n_past, false);
-                    if (j == num_image_embeds_col - 1) {
-                        eval_string(ctx_llava->ctx_llama, std::string("\n").c_str(), params->n_batch, &n_past, false);
-                    }
-                }
-            }
-            eval_string(ctx_llava->ctx_llama, std::string("</slice>").c_str(), params->n_batch, &n_past, false);
-        }
-        else if (has_minicpmv_projector == 3 || has_minicpmv_projector == 4) {
-            size_t num_image_embeds_col = clip_uhd_num_image_embeds_col(ctx_llava->ctx_clip);
-            for (size_t i = 0; i < (num_image_embeds-1)/num_image_embeds_col; ++i) {
-                for (size_t j = 0; j < num_image_embeds_col; ++j) {
-                    eval_string(ctx_llava->ctx_llama, std::string("<slice>").c_str(), params->n_batch, &n_past, false);
-                    process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
-                    eval_string(ctx_llava->ctx_llama, std::string("</slice>").c_str(), params->n_batch, &n_past, false);
-                    if (j == num_image_embeds_col - 1) {
-                        eval_string(ctx_llava->ctx_llama, std::string("\n").c_str(), params->n_batch, &n_past, false);
-                    }
-                }
-            }
-        }
-    }
-    LOG_INF("%s: image token past: %d\n", __func__, n_past);
-}
-
-static const char * sample(struct common_sampler * smpl,
-                           struct llama_context * ctx_llama,
-                           int * n_past) {
-    const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
-    common_sampler_accept(smpl, id, true);
-
-    const llama_model * model = llama_get_model(ctx_llama);
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
-    static std::string ret;
-    if (llama_vocab_is_eog(vocab, id)) {
-        ret = "</s>";
-    } else {
-        ret = common_token_to_piece(ctx_llama, id);
-    }
-    eval_id(ctx_llama, id, n_past);
-    return ret.c_str();
-}
-
-static struct llava_context * minicpmv_init(common_params * params, const std::string & fname, int &n_past){
-    auto * ctx_clip = clip_init_context(params);
-    auto * embeds = llava_image_embed_make_with_filename(ctx_clip, params->cpuparams.n_threads, fname.c_str());
-    if (!embeds) {
-        LOG_ERR("failed to load image %s. Terminating\n\n", fname.c_str());
-        return NULL;
-    }
-
-    // process the prompt
-    if (params->prompt.empty() && params->interactive == false) {
-        LOG_ERR("prompt should be given or interactive mode should be on");
-        return NULL;
-    }
-
-    auto * model = llava_init(params);
-    if (model == NULL) {
-        fprintf(stderr, "%s: error: failed to init minicpmv model\n", __func__);
-        return NULL;
-    }
-    const int64_t t_llava_init_start_us = ggml_time_us();
-    auto * ctx_llava = llava_init_context(params, model);
-    ctx_llava->ctx_clip = ctx_clip;
-    const int64_t t_llava_init_end_us = ggml_time_us();
-    float t_llava_init_ms = (t_llava_init_end_us - t_llava_init_start_us) / 1000.0;
-    LOG_INF("%s: llava init in %8.2f ms.\n", __func__, t_llava_init_ms);
-
-    const int64_t t_process_image_start_us = ggml_time_us();
-    process_image(ctx_llava, embeds, params, n_past);
-    const int64_t t_process_image_end_us = ggml_time_us();
-    float t_process_image_ms = (t_process_image_end_us - t_process_image_start_us) / 1000.0;
-    LOG_INF("%s: llama process image in %8.2f ms.\n", __func__, t_process_image_ms);
-
-    llava_image_embed_free(embeds);
-    return ctx_llava;
-}
-
-static struct common_sampler * llama_init(struct llava_context * ctx_llava, common_params * params, const std::string & prompt, int & n_past, bool is_first = false){
-    std::string user_prompt = prompt;
-    int has_minicpmv_projector = clip_is_minicpmv(ctx_llava->ctx_clip);
-    if (!is_first) {
-        if (has_minicpmv_projector == 2) {
-            user_prompt = "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n" + prompt;
-        }
-        else if (has_minicpmv_projector == 3) {
-            user_prompt = "<|im_start|>user\n" + prompt;
-        }
-        else if (has_minicpmv_projector == 4) {
-            user_prompt = "<|im_start|>user\n" + prompt;
-        }
-    }
-
-    eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false);
-    if (has_minicpmv_projector == 2) {
-        eval_string(ctx_llava->ctx_llama, "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", params->n_batch, &n_past, false);
-    }
-    else if (has_minicpmv_projector == 3) {
-        eval_string(ctx_llava->ctx_llama, "<|im_end|><|im_start|>assistant\n", params->n_batch, &n_past, false);
-    }
-    else if (has_minicpmv_projector == 4) {
-        eval_string(ctx_llava->ctx_llama, "<|im_end|><|im_start|>assistant\n", params->n_batch, &n_past, false);
-    }
-
-    // generate the response
-
-    LOG_INF("\n");
-
-    struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling);
-    return smpl;
-}
-
-static const char * llama_loop(struct llava_context * ctx_llava,struct common_sampler * smpl, int &n_past){
-
-    const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past);
-    return tmp;
-}
-
-int main(int argc, char ** argv) {
-    ggml_time_init();
-
-    common_params params;
-
-    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, show_additional_info)) {
-        return 1;
-    }
-
-    common_init();
-
-    if (params.mmproj.path.empty() || (params.image.empty())) {
-        show_additional_info(argc, argv);
-        return 1;
-    }
-
-    for (auto & image : params.image) {
-        int n_past = 0;
-        auto * ctx_llava = minicpmv_init(&params, image, n_past);
-
-        if (!params.prompt.empty()) {
-            LOG("<user>%s\n", params.prompt.c_str());
-            LOG("<assistant>");
-            auto * smpl = llama_init(ctx_llava, &params, params.prompt, n_past, true);
-            const int max_tgt_len = params.n_predict < 0 ? 256 : params.n_predict;
-            std::string response;
-            bool have_tmp = false;
-            for (int i = 0; i < max_tgt_len; i++) {
-                const auto * tmp = llama_loop(ctx_llava, smpl, n_past);
-                response += tmp;
-                if (strcmp(tmp, "</s>") == 0){
-                    if (!have_tmp) {
-                        continue;
-                    }
-                    break;
-                }
-                if (strstr(tmp, "###")) break; // Yi-VL behavior
-                have_tmp = true;
-                printf("%s", tmp);
-                if (strstr(response.c_str(), "<user>")) break; // minicpm-v
-
-                fflush(stdout);
-            }
-            common_sampler_free(smpl);
-        }else {
-            while (true) {
-                LOG("<user>");
-                std::string prompt;
-                std::getline(std::cin, prompt);
-                LOG("<assistant>");
-                auto * smpl = llama_init(ctx_llava, &params, prompt, n_past, true);
-                const int max_tgt_len = params.n_predict < 0 ? 256 : params.n_predict;
-                std::string response;
-                for (int i = 0; i < max_tgt_len; i++) {
-                    const auto * tmp = llama_loop(ctx_llava, smpl, n_past);
-                    response += tmp;
-                    if (strcmp(tmp, "</s>") == 0) break;
-                    printf("%s", tmp);// mistral llava-1.6
-                    if (strstr(response.c_str(), "<user>")) break; // minicpm-v
-                    fflush(stdout);
-                }
-                common_sampler_free(smpl);
-            }
-        }
-        printf("\n");
-        llama_perf_context_print(ctx_llava->ctx_llama);
-
-        ctx_llava->model = NULL;
-        llava_free(ctx_llava);
-    }
-
-    return 0;
-}

examples/llava/mtmd.cpp

@@ -1,341 +0,0 @@
-#include "clip.h"
-#include "clip-impl.h"
-#include "mtmd.h"
-
-#include "llama.h"
-
-#include <algorithm>
-#include <cerrno>
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <limits>
-#include <vector>
-
-struct mtmd_context {
-    struct clip_ctx * ctx_clip;
-    const struct llama_model * text_model;
-    std::vector<float> image_embd_v; // image embedding vector
-    bool print_timings;
-    int n_threads;
-    std::string image_marker;
-
-    // TODO @ngxson : add timings
-
-    mtmd_context(const char * mmproj_fname,
-                   const llama_model * text_model,
-                   const mtmd_context_params & ctx_params) : print_timings(ctx_params.print_timings), n_threads(ctx_params.n_threads), image_marker(ctx_params.image_marker) {
-        clip_context_params ctx_clip_params;
-        ctx_clip_params.use_gpu   = ctx_params.use_gpu;
-        ctx_clip_params.verbosity = ctx_params.verbosity;
-        ctx_clip = clip_init(mmproj_fname, ctx_clip_params);
-        if (!ctx_clip) {
-            throw std::runtime_error(string_format("Failed to load CLIP model from %s\n", mmproj_fname));
-        }
-        this->text_model = text_model;
-    }
-
-    ~mtmd_context() {
-        clip_free(ctx_clip);
-    }
-};
-
-struct mtmd_image_tokens_data {
-    clip_image_f32_batch batch_f32; // preprocessed image patches
-};
-
-struct mtmd_image_tokens {
-    uint32_t nx; // number of tokens in x direction
-    uint32_t ny; // number of tokens in y direction
-    uint32_t n_tokens() const { return nx * ny; }
-    clip_image_f32_batch batch_f32; // preprocessed image patches
-};
-
-mtmd_context * mtmd_init_from_file(const char * mmproj_fname,
-        const struct llama_model * text_model,
-        const struct mtmd_context_params ctx_params) {
-    try {
-        return new mtmd_context(mmproj_fname, text_model, ctx_params);
-    } catch (const std::exception & e) {
-        LOG_ERR("%s: error: %s\n", __func__, e.what());
-        return nullptr;
-    }
-}
-
-void mtmd_free(mtmd_context * ctx) {
-    if (ctx) {
-        delete ctx;
-    }
-}
-
-// copied from common_tokenize
-static std::vector<llama_token> mtmd_tokenize_text_internal(
-    const struct llama_vocab * vocab,
-           const std::string & text,
-                        bool   add_special,
-                        bool   parse_special) {
-    // upper limit for the number of tokens
-    int n_tokens = text.length() + 2 * add_special;
-    std::vector<llama_token> result(n_tokens);
-    n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
-    if (n_tokens < 0) {
-        result.resize(-n_tokens);
-        int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
-        GGML_ASSERT(check == -n_tokens);
-    } else {
-        result.resize(n_tokens);
-    }
-    return result;
-}
-
-mtmd_input_chunks * mtmd_tokenize(mtmd_context * ctx,
-                                const mtmd_input_text & text,
-                                const std::vector<mtmd_bitmap> & bitmaps) {
-    mtmd_input_chunks * output = new mtmd_input_chunks;
-    auto vocab = llama_model_get_vocab(ctx->text_model);
-
-    std::string prompt_modified(text.text);
-    std::string marker_modified(ctx->image_marker);
-    projector_type proj_type = clip_get_projector_type(ctx->ctx_clip);
-    // a bit hacky here, but works for now
-    // for some models, we need to add prefix and suffix to the image embeddings
-    if (proj_type == PROJECTOR_TYPE_GEMMA3) {
-        // <start_of_image> ... (image embeddings) ... <end_of_image>
-        marker_modified = "<start_of_image>" + ctx->image_marker + "<end_of_image>";
-        string_replace_all(prompt_modified, ctx->image_marker, marker_modified);
-    }
-
-    std::vector<std::string> parts = string_split_str(text.text, ctx->image_marker);
-    output->clear();
-    output->reserve(parts.size());
-
-    size_t i_img = 0;
-
-    for (const auto & part : parts) {
-        //printf("tokenizing part: %s\n", part.c_str());
-        bool add_bos = &parts.front() == &part;
-        auto tokens = mtmd_tokenize_text_internal(vocab, part, text.add_special && add_bos, text.parse_special);
-        if (tokens.empty()) {
-            continue;
-        }
-        mtmd_input_chunk chunk{
-            MTMD_INPUT_CHUNK_TYPE_TEXT,
-            std::move(tokens),
-            {},
-        };
-        output->emplace_back(std::move(chunk));
-
-        if (&parts.back() != &part) {
-            // add image token to middle of 2 parts
-
-            if (i_img >= bitmaps.size()) {
-                LOG_ERR("%s: error: not enough images for %d parts\n", __func__, (int)parts.size());
-                return nullptr;
-            }
-
-            // shim layer
-            clip_image_u8_ptr img_u8(clip_image_u8_init());
-            img_u8->nx = bitmaps[i_img].nx;
-            img_u8->ny = bitmaps[i_img].ny;
-            img_u8->buf.resize(bitmaps[i_img].data.size());
-            std::memcpy(img_u8->buf.data(), bitmaps[i_img].data.data(), img_u8->nx * img_u8->ny * 3);
-
-            // preprocess image
-            clip_image_f32_batch batch_f32;
-            bool ok = clip_image_preprocess(ctx->ctx_clip, img_u8.get(), &batch_f32);
-            if (!ok) {
-                LOG_ERR("Unable to preprocess image\n");
-                return nullptr;
-            }
-
-            mtmd_image_tokens * image_tokens = new mtmd_image_tokens;
-            image_tokens->nx = clip_n_patches(ctx->ctx_clip); // TODO @ngxson : use clip_n_patches_by_image
-            image_tokens->ny = 1; // TODO
-            image_tokens->batch_f32 = std::move(batch_f32);
-
-            mtmd_input_chunk chunk{
-                MTMD_INPUT_CHUNK_TYPE_IMAGE,
-                {},
-                image_tokens,
-            };
-            output->emplace_back(std::move(chunk));
-            i_img++;
-        }
-    }
-
-    return output;
-}
-
-void mtmd_input_chunks_free(mtmd_input_chunks * chunks) {
-    for (auto & chunk : *chunks) {
-        if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE && chunk.tokens_image) {
-            delete chunk.tokens_image;
-        }
-    }
-    delete chunks;
-}
-
-int32_t mtmd_encode(mtmd_context * ctx, const mtmd_image_tokens * image_tokens) {
-    int n_mmproj_embd = clip_n_mmproj_embd(ctx->ctx_clip);
-    ctx->image_embd_v.resize(image_tokens->n_tokens() * n_mmproj_embd);
-    bool ok = clip_image_batch_encode(
-        ctx->ctx_clip,
-        ctx->n_threads,
-        &image_tokens->batch_f32,
-        ctx->image_embd_v.data());
-    return ok ? 0 : 1;
-}
-
-float * mtmd_get_output_embd(mtmd_context * ctx) {
-    return ctx->image_embd_v.data();
-}
-
-size_t mtmd_helper_get_n_tokens(mtmd_input_chunks * chunks) {
-    size_t n_tokens = 0;
-    for (auto & chunk : *chunks) {
-        if (chunk.type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
-            n_tokens += chunk.tokens_text.size();
-        } else if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
-            n_tokens += chunk.tokens_image->n_tokens();
-        } else {
-            GGML_ASSERT(false && "chunk type not supported");
-        }
-    }
-    return n_tokens;
-}
-
-// helper struct to make working with embd batch easier
-// note: this will be removed after llama_batch_ext refactoring
-struct decode_embd_batch {
-    std::vector<llama_pos>      pos;
-    std::vector<int32_t>        n_seq_id;
-    std::vector<llama_seq_id>   seq_id_0;
-    std::vector<llama_seq_id *> seq_ids;
-    std::vector<int8_t>         logits;
-    llama_batch batch;
-    decode_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
-        pos     .resize(n_tokens);
-        n_seq_id.resize(n_tokens);
-        seq_ids .resize(n_tokens + 1);
-        logits  .resize(n_tokens);
-        seq_id_0.resize(1);
-        seq_id_0[0] = seq_id;
-        seq_ids [n_tokens] = nullptr;
-        batch = {
-            /*n_tokens       =*/ n_tokens,
-            /*tokens         =*/ nullptr,
-            /*embd           =*/ embd,
-            /*pos            =*/ pos.data(),
-            /*n_seq_id       =*/ n_seq_id.data(),
-            /*seq_id         =*/ seq_ids.data(),
-            /*logits         =*/ logits.data(),
-        };
-        for (int i = 0; i < n_tokens; i++) {
-            batch.pos     [i] = pos_0 + i;
-            batch.n_seq_id[i] = 1;
-            batch.seq_id  [i] = seq_id_0.data();
-            batch.logits  [i] = false;
-        }
-    }
-};
-
-int32_t mtmd_helper_eval(mtmd_context * ctx,
-        llama_context * lctx,
-        mtmd_input_chunks * chunks,
-        llama_pos pos0,
-        llama_seq_id seq_id,
-        int32_t n_batch) {
-    int32_t ret;
-    llama_pos n_past = pos0;
-    llama_batch text_batch = llama_batch_init(n_batch, 0, 1);
-
-    for (auto & chunk : *chunks) {
-        bool is_last = &chunk == &chunks->back();
-        if (chunk.type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
-            // TODO @ngxson : may need to split into smaller batches
-            text_batch.n_tokens = chunk.tokens_text.size();
-            for (size_t i = 0; i < chunk.tokens_text.size(); i++) {
-                text_batch.token   [i]    = chunk.tokens_text[i];
-                text_batch.pos     [i]    = n_past++;
-                text_batch.n_seq_id[i]    = 1;
-                text_batch.seq_id  [i][0] = seq_id;
-                text_batch.logits  [i]    = false;
-            }
-            if (is_last) {
-                // always get logits for last input chunk
-                text_batch.logits[text_batch.n_tokens - 1] = true;
-            }
-            ret = llama_decode(lctx, text_batch);
-            if (ret != 0) {
-                LOG_ERR("failed to decode text\n");
-                llama_batch_free(text_batch);
-                return ret;
-            }
-
-        } else if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
-            GGML_ASSERT(!is_last && "logits for last image chunk is not yet support");
-            GGML_ASSERT(chunk.tokens_image != nullptr);
-            int64_t t0 = ggml_time_ms();
-            if (ctx->print_timings) {
-                LOG_INF("encoding image...\n");
-            }
-            ret = mtmd_encode(ctx, chunk.tokens_image);
-            if (ret != 0) {
-                LOG_ERR("failed to encode image\n");
-                llama_batch_free(text_batch);
-                return ret;
-            }
-            if (ctx->print_timings) {
-                LOG_INF("image encoded in %" PRId64 " ms\n", ggml_time_ms() - t0);
-            }
-
-            int32_t n_tokens = chunk.tokens_image->n_tokens();
-            float * embd = mtmd_get_output_embd(ctx);
-            decode_embd_batch batch_img(embd, n_tokens, n_past, 0);
-            int64_t t1 = ggml_time_ms();
-            ret = llama_decode(lctx, batch_img.batch);
-            if (ret != 0) {
-                LOG_ERR("failed to decode image\n");
-                llama_batch_free(text_batch);
-                return ret;
-            }
-            if (ctx->print_timings) {
-                LOG_INF("image decoded in %" PRId64 " ms\n", ggml_time_ms() - t1);
-            }
-
-            n_past += n_tokens;
-
-        } else {
-            GGML_ASSERT(false && "chunk type not supported");
-        }
-    }
-
-    llama_batch_free(text_batch);
-    return 0;
-}
-
-int32_t mtmd_helper_bitmap_init_from_buf(const unsigned char * buf, size_t len, mtmd_bitmap & output) {
-    clip_image_u8_ptr img_u8(clip_image_u8_init());
-    bool ok = clip_image_load_from_bytes(buf, len, img_u8.get());
-    if (!ok) {
-        LOG_ERR("Unable to load image from buffer\n");
-        return 1;
-    }
-    unsigned char * data = clip_image_u8_get_data(img_u8.get(), &output.nx, &output.ny);
-    output.data.resize(output.nx * output.ny * 3);
-    std::memcpy(output.data.data(), data, output.nx * output.ny * 3);
-    return 0;
-}
-
-int32_t mtmd_helper_bitmap_init_from_file(const char * fname, mtmd_bitmap & output) {
-    clip_image_u8_ptr img_u8(clip_image_u8_init());
-    bool ok = clip_image_load_from_file(fname, img_u8.get());
-    if (!ok) {
-        LOG_ERR("Unable to load image %s\n", fname);
-        return 1;
-    }
-    unsigned char * data = clip_image_u8_get_data(img_u8.get(), &output.nx, &output.ny);
-    output.data.resize(output.nx * output.ny * 3);
-    std::memcpy(output.data.data(), data, output.nx * output.ny * 3);
-    return 0;
-}

examples/llava/mtmd.h

@@ -1,146 +0,0 @@
-#ifndef MTMD_H
-#define MTMD_H
-
-#include "ggml.h"
-#include "llama.h"
-#include "clip.h"
-
-#include <vector>
-#include <cinttypes>
-#include <memory>
-
-#ifdef LLAMA_SHARED
-#    if defined(_WIN32) && !defined(__MINGW32__)
-#        ifdef LLAMA_BUILD
-#            define MTMD_API __declspec(dllexport)
-#        else
-#            define MTMD_API __declspec(dllimport)
-#        endif
-#    else
-#        define MTMD_API __attribute__ ((visibility ("default")))
-#    endif
-#else
-#    define MTMD_API
-#endif
-
-#ifdef __cplusplus
-
-enum mtmd_input_chunk_type {
-    MTMD_INPUT_CHUNK_TYPE_TEXT,
-    MTMD_INPUT_CHUNK_TYPE_IMAGE,
-};
-
-struct mtmd_context;
-struct mtmd_image_tokens;
-
-// represents raw image data, layout is RGBRGBRGB...
-// length of data must be nx * ny * 3
-struct mtmd_bitmap {
-    uint32_t nx;
-    uint32_t ny;
-    std::vector<unsigned char> data;
-};
-
-struct mtmd_input_chunk {
-    mtmd_input_chunk_type type;
-    std::vector<llama_token> tokens_text;
-    mtmd_image_tokens * tokens_image = nullptr;
-};
-
-using mtmd_input_chunks = std::vector<mtmd_input_chunk>;
-
-struct mtmd_context_params {
-    bool use_gpu = true;
-    bool print_timings = true;
-    int n_threads = 4;
-    enum ggml_log_level verbosity = GGML_LOG_LEVEL_INFO;
-    const char * image_marker = "<__image__>";
-};
-
-struct mtmd_input_text {
-    std::string text;
-    bool add_special;
-    bool parse_special;
-};
-
-// initialize the mtmd context
-// return nullptr on failure
-MTMD_API mtmd_context * mtmd_init_from_file(const char * mmproj_fname,
-                                                const llama_model * text_model,
-                                                const mtmd_context_params ctx_params);
-
-MTMD_API void mtmd_free(mtmd_context * ctx);
-
-// tokenize an input text prompt and an image
-// the prompt must have the input image marker (default: "<__image__>") in it
-// the marker will be replaced with the image tokens
-// for example:
-//   "here is an image: <__image__>\ndescribe it in detail."
-//   this will gives 3 chunks:
-//   1. "here is an image: <start_of_image>"
-//   2. (image tokens)
-//   3. "<end_of_image>\ndescribe it in detail."
-// number of bitmaps must be equal to the number of image markers in the prompt
-// this function is thread-safe (shared ctx)
-MTMD_API mtmd_input_chunks * mtmd_tokenize(mtmd_context * ctx,
-                                const mtmd_input_text & text,
-                                const std::vector<mtmd_bitmap> & bitmaps);
-
-// free image chunk data
-MTMD_API void mtmd_input_chunks_free(mtmd_input_chunks * chunks);
-
-// returns 0 on success
-MTMD_API int32_t mtmd_encode(mtmd_context * ctx,
-                            const mtmd_image_tokens * image_tokens);
-
-// get output embeddings from the last encode pass
-MTMD_API float * mtmd_get_output_embd(mtmd_context * ctx);
-
-//
-// helper functions (can be implemented based on other functions)
-//
-
-// helper to count the total number of tokens from a list of chunks, useful to keep track of n_past
-MTMD_API size_t mtmd_helper_get_n_tokens(mtmd_input_chunks * chunks);
-
-// helper function that automatically:
-// 1. run llama_decode() on text chunks
-// 2. run mtmd_encode() on image chunks, then mtmd_get_output_embd() and then llama_decode()
-// if any of the mtmd_encode() or llama_decode() calls return non-zero, stop and forward the error
-// otherwise, returns 0 on success
-MTMD_API int32_t mtmd_helper_eval(mtmd_context * ctx,
-                                llama_context * lctx,
-                                mtmd_input_chunks * chunks,
-                                llama_pos pos0,
-                                llama_seq_id seq_id,
-                                int32_t n_batch);
-
-// helper function to construct a mtmd_bitmap from a file
-// returns 0 on success
-// this function is thread-safe
-MTMD_API int32_t mtmd_helper_bitmap_init_from_file(const char * fname, mtmd_bitmap & output);
-
-// helper function to construct a mtmd_bitmap from a buffer
-// the buffer must be an image in format supported by stb_image (jpg, png, bmp, gif, etc.)
-// returns 0 on success
-// this function is thread-safe
-MTMD_API int32_t mtmd_helper_bitmap_init_from_buf(const unsigned char * buf, size_t len, mtmd_bitmap & output);
-
-// convenient unique_ptr wrappers
-struct mtmd_context_deleter {
-    void operator()(mtmd_context * val) { mtmd_free(val); }
-};
-using mtmd_context_ptr = std::unique_ptr<mtmd_context, mtmd_context_deleter>;
-
-struct mtmd_input_chunks_deleter {
-    void operator()(mtmd_input_chunks * val) { mtmd_input_chunks_free(val); }
-};
-using mtmd_input_chunks_ptr = std::unique_ptr<mtmd_input_chunks, mtmd_input_chunks_deleter>;
-
-#else
-
-static_assert(false && "C header is not yet supported by this library");
-
-#endif
-
-#endif

examples/llava/qwen2_vl_surgery.py

@@ -1,165 +0,0 @@
-import argparse
-from typing import Dict
-
-import torch
-import numpy as np
-from gguf import *
-from transformers import (
-    Qwen2VLForConditionalGeneration,
-    Qwen2VLProcessor,
-    AutoProcessor,
-    Qwen2VLConfig
-)
-
-
-VISION = "clip.vision"
-
-
-def k(raw_key: str, arch: str) -> str:
-    return raw_key.format(arch=arch)
-
-
-def to_gguf_name(name: str) -> str:
-    og = name
-    name = name.replace("text_model", "t").replace("vision_model", "v")
-    name = name.replace("blocks", "blk").replace("embeddings.", "")
-    name = name.replace("attn.", "attn_")
-    name = name.replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("proj.", "out.")
-    # name = name.replace("layrnorm", "ln").replace("layer_norm", "ln").replace("layernorm", "ln")
-    name = name.replace("norm1", "ln1").replace("norm2", "ln2")
-    name = name.replace("merger.mlp", 'mm')
-    print(f"[to_gguf_name] {og} --> {name}")
-    return name
-
-
-def find_vision_tensors(qwen2vl, dtype) -> Dict[str, np.ndarray]:
-    vision_model = qwen2vl.visual
-    tensor_map = {}
-    for name, ten in vision_model.state_dict().items():
-        ten = ten.numpy()
-        if 'qkv' in name:
-            if ten.ndim == 2: # weight
-                c3, _ = ten.shape
-            else:             # bias
-                c3 = ten.shape[0]
-            assert c3 % 3 == 0
-            c = c3 // 3
-            wq = ten[:c]
-            wk = ten[c: c * 2]
-            wv = ten[c * 2:]
-            tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "q")] = wq
-            tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "k")] = wk
-            tensor_map[to_gguf_name(f"vision_model.{name}").replace("qkv", "v")] = wv
-        elif 'merger' in name:
-            if name.endswith("ln_q.weight"):
-                tensor_map['v.post_ln.weight'] = ten
-            elif name.endswith("ln_q.bias"):
-                tensor_map['v.post_ln.bias'] = ten
-            else:
-                # "merger.mlp.%d.weight/bias" --> "mm.%d.weight/bias"
-                tensor_map[to_gguf_name(name)] = ten
-        elif 'patch_embed.proj.weight' in name:
-            # NOTE: split Conv3D into Conv2Ds
-            c1, c2, kt, kh, kw = ten.shape
-            assert kt == 2, "Current implmentation only support temporal_patch_size of 2"
-            tensor_map["v.patch_embd.weight"] = ten[:, :, 0, ...]
-            tensor_map["v.patch_embd.weight.1"] = ten[:, :, 1, ...]
-        else:
-            tensor_map[to_gguf_name(f"vision_model.{name}")] = ten
-
-    for new_name, ten in tensor_map.items():
-        if ten.ndim <= 1 or new_name.endswith("_norm.weight"):
-            tensor_map[new_name] = ten.astype(np.float32)
-        else:
-            tensor_map[new_name] = ten.astype(dtype)
-    tensor_map["v.position_embd.weight"] = np.zeros([10, 10], dtype=np.float32)  # dummy tensor, just here as a placeholder
-    return tensor_map
-
-
-def main(args):
-    if args.data_type == 'fp32':
-        dtype = torch.float32
-        np_dtype = np.float32
-        ftype = 0
-    elif args.data_type == 'fp16':
-        dtype = torch.float32
-        np_dtype = np.float16
-        ftype = 1
-    else:
-        raise ValueError()
-
-    local_model = False
-    model_path = ""
-    model_name = args.model_name
-    print("model_name: ", model_name)
-    qwen2vl = Qwen2VLForConditionalGeneration.from_pretrained(
-        model_name, torch_dtype=dtype, device_map="cpu"
-    )
-    cfg: Qwen2VLConfig = qwen2vl.config  # type: ignore[reportAssignmentType]
-    vcfg = cfg.vision_config
-
-    if os.path.isdir(model_name):
-        local_model = True
-        if model_name.endswith(os.sep):
-            model_name = model_name[:-1]
-        model_path = model_name
-        model_name = os.path.basename(model_name)
-    fname_out = f"{model_name.replace('/', '-').lower()}-vision.gguf"
-
-    fout = GGUFWriter(path=fname_out, arch="clip")
-    fout.add_description("image encoder for Qwen2VL")
-
-    fout.add_file_type(ftype)
-    fout.add_bool("clip.has_text_encoder", False)
-    fout.add_bool("clip.has_vision_encoder", True)
-    fout.add_bool("clip.has_qwen2vl_merger", True)
-    fout.add_string("clip.projector_type", "qwen2vl_merger")
-
-    print(cfg.vision_config)
-    if 'silu' in cfg.vision_config.hidden_act.lower():
-        fout.add_bool("clip.use_silu", True)
-        fout.add_bool("clip.use_gelu", False)
-    elif 'gelu' in cfg.vision_config.hidden_act.lower():
-        fout.add_bool("clip.use_silu", False)
-        fout.add_bool("clip.use_gelu", 'quick' not in cfg.vision_config.hidden_act.lower())
-    else:
-        raise ValueError()
-
-    tensor_map = find_vision_tensors(qwen2vl, np_dtype)
-    for name, data in tensor_map.items():
-        fout.add_tensor(name, data)
-
-    fout.add_uint32("clip.vision.patch_size", vcfg.patch_size)
-    fout.add_uint32("clip.vision.image_size", 14 * 40)  # some reasonable size that is divable by (14*2)
-    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), vcfg.embed_dim)
-    fout.add_uint32("clip.vision.projection_dim", vcfg.hidden_size)
-    fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), vcfg.num_heads)
-    fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
-    fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), vcfg.depth)
-    fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), 0)  # not sure what this does, put 0 here as a placeholder
-    fout.add_name(model_name)
-    """
-    HACK: Since vision rope related parameter aren't stored in the `Qwen2VLConfig,
-            it will be hardcoded in the `clip_image_build_graph` from `clip.cpp`.
-    """
-
-    if local_model:
-        processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_path)
-    else:
-        processor: Qwen2VLProcessor = AutoProcessor.from_pretrained(model_name)
-    fout.add_array("clip.vision.image_mean", processor.image_processor.image_mean) # type: ignore[reportAttributeAccessIssue]
-    fout.add_array("clip.vision.image_std", processor.image_processor.image_std) # type: ignore[reportAttributeAccessIssue]
-
-    fout.write_header_to_file()
-    fout.write_kv_data_to_file()
-    fout.write_tensors_to_file()
-    fout.close()
-    print("save model as: ", fname_out)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("model_name", nargs='?', default="Qwen/Qwen2-VL-2B-Instruct")
-    parser.add_argument("--data_type", nargs='?', choices=['fp32', 'fp16'], default="fp32")
-    args = parser.parse_args()
-    main(args)

examples/llava/qwen2vl-cli.cpp

@@ -1,584 +0,0 @@
-#include "arg.h"
-#include "base64.hpp"
-#include "log.h"
-#include "common.h"
-#include "sampling.h"
-#include "clip.h"
-#include "llava.h"
-#include "llama.h"
-#include "ggml.h"
-
-#ifdef GGML_USE_CUDA
-#include "ggml-cuda.h"
-#endif
-#ifdef NDEBUG
-#include "ggml-alloc.h"
-#include "ggml-backend.h"
-#endif
-
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-#include <vector>
-#include <algorithm>
-#include <iostream>
-#include <fstream>
-
-
-static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed,
-                                     int n_batch, int * n_past, int * st_pos_id, struct clip_image_size * image_size) {
-    int n_embd  = llama_model_n_embd(llama_get_model(ctx_llama));
-    const int patch_size = 14 * 2;
-    const int ph = image_size->height / patch_size + (image_size->height % patch_size > 0);
-    const int pw = image_size->width / patch_size + (image_size->width % patch_size > 0);
-    auto img_tokens = image_embed->n_image_pos;
-    // llama_pos mrope_pos[img_tokens * 4];
-    std::vector<llama_pos> mrope_pos;
-    mrope_pos.resize(img_tokens * 4);
-
-    for (int y = 0; y < ph; y++)
-    {
-        for (int x = 0; x < pw; x++)
-        {
-            int i = y * pw + x;
-            mrope_pos[i] = *st_pos_id;
-            mrope_pos[i + img_tokens] = *st_pos_id + y;
-            mrope_pos[i + img_tokens * 2] = *st_pos_id + x;
-            mrope_pos[i + img_tokens * 3] = 0;
-        }
-    }
-    *st_pos_id += std::max(pw, ph);
-
-    int processed = 0;
-    std::vector<llama_pos> batch_mrope_pos;
-    batch_mrope_pos.resize(img_tokens * 4);
-
-    for (int i = 0; i < img_tokens; i += n_batch) {
-        int n_eval = img_tokens - i;
-        if (n_eval > n_batch) {
-            n_eval = n_batch;
-        }
-
-        // llama_pos batch_mrope_pos[n_eval * 4];
-        std::fill(batch_mrope_pos.begin(), batch_mrope_pos.end(), 0);
-        memcpy(batch_mrope_pos.data(), &mrope_pos[processed], n_eval * sizeof(llama_pos));
-        memcpy(&batch_mrope_pos[n_eval * 1], &mrope_pos[img_tokens * 1 + processed], n_eval * sizeof(llama_pos));
-        memcpy(&batch_mrope_pos[n_eval * 2], &mrope_pos[img_tokens * 2 + processed], n_eval * sizeof(llama_pos));
-        memcpy(&batch_mrope_pos[n_eval * 3], &mrope_pos[img_tokens * 3 + processed], n_eval * sizeof(llama_pos));
-
-        llama_batch batch = {
-            int32_t(n_eval),                // n_tokens
-            nullptr,                        // token
-            (image_embed->embed+i*n_embd),  // embed
-            batch_mrope_pos.data(),         // pos
-            nullptr,  // n_seq_id
-            nullptr,  // seq_id
-            nullptr,  // logits
-        };
-
-        if (llama_decode(ctx_llama, batch)) {
-            LOG_ERR("%s : failed to eval\n", __func__);
-            return false;
-        }
-        *n_past += n_eval;
-        processed += n_eval;
-    }
-    return true;
-}
-
-
-static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past, int * st_pos_id) {
-    int N = (int) tokens.size();
-    std::vector<llama_pos> pos;
-    for (int i = 0; i < N; i += n_batch) {
-        int n_eval = (int) tokens.size() - i;
-        if (n_eval > n_batch) {
-            n_eval = n_batch;
-        }
-        auto batch = llama_batch_get_one(&tokens[i], n_eval);
-        // TODO: add mrope pos ids somewhere else
-        pos.resize(batch.n_tokens * 4);
-        std::fill(pos.begin(), pos.end(), 0);
-        for (int j = 0; j < batch.n_tokens * 3; j ++) {
-            pos[j] = *st_pos_id + (j % batch.n_tokens);
-        }
-        batch.pos = pos.data();
-
-        if (llama_decode(ctx_llama, batch)) {
-            LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
-            return false;
-        }
-        *n_past += n_eval;
-        *st_pos_id += n_eval;
-    }
-    return true;
-}
-
-static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past, int * st_pos_id) {
-    std::vector<llama_token> tokens;
-    tokens.push_back(id);
-    return eval_tokens(ctx_llama, tokens, 1, n_past, st_pos_id);
-}
-
-static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, int * st_pos_id, bool add_bos){
-    std::string              str2     = str;
-    std::vector<llama_token> embd_inp = common_tokenize(ctx_llama, str2, add_bos, true);
-    eval_tokens(ctx_llama, embd_inp, n_batch, n_past, st_pos_id);
-    return true;
-}
-
-static const char * sample(struct common_sampler * smpl,
-                           struct llama_context * ctx_llama,
-                           int * n_past, int * st_pos_id) {
-    const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
-    common_sampler_accept(smpl, id, true);
-
-    const llama_model * model = llama_get_model(ctx_llama);
-    const llama_vocab * vocab = llama_model_get_vocab(model);
-
-    static std::string ret;
-    if (llama_vocab_is_eog(vocab, id)) {
-        ret = "</s>";
-    } else {
-        ret = common_token_to_piece(ctx_llama, id);
-    }
-    eval_id(ctx_llama, id, n_past, st_pos_id);
-    return ret.c_str();
-}
-
-static const char* IMG_BASE64_TAG_BEGIN = "<img src=\"data:image/jpeg;base64,";
-static const char* IMG_BASE64_TAG_END = "\">";
-
-static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) {
-    begin_out = prompt.find(IMG_BASE64_TAG_BEGIN);
-    end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out);
-}
-
-static bool prompt_contains_image(const std::string& prompt) {
-    size_t begin, end;
-    find_image_tag_in_prompt(prompt, begin, end);
-    return (begin != std::string::npos);
-}
-
-// replaces the base64 image tag in the prompt with `replacement`
-static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) {
-    size_t img_base64_str_start, img_base64_str_end;
-    find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
-    if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
-        LOG_ERR("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
-        return NULL;
-    }
-
-    auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN);
-    auto base64_bytes_count = img_base64_str_end - base64_bytes_start;
-    auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count );
-
-    auto required_bytes = base64::required_encode_size(base64_str.size());
-    auto img_bytes = std::vector<unsigned char>(required_bytes);
-    base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin());
-
-    auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
-    if (!embed) {
-        LOG_ERR("%s: could not load image from base64 string.\n", __func__);
-        return NULL;
-    }
-
-    return embed;
-}
-
-static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") {
-    size_t begin, end;
-    find_image_tag_in_prompt(prompt, begin, end);
-    if (begin == std::string::npos || end == std::string::npos) {
-        return prompt;
-    }
-    auto pre = prompt.substr(0, begin);
-    auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END));
-    return pre + replacement + post;
-}
-
-struct llava_context {
-    struct clip_ctx * ctx_clip = NULL;
-    struct llama_context * ctx_llama = NULL;
-    struct llama_model * model = NULL;
-};
-
-static void print_usage(int, char ** argv) {
-    LOG("\n example usage:\n");
-    LOG("\n     %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
-    LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
-}
-
-static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) {
-
-    // load and preprocess the image
-    llava_image_embed * embed = NULL;
-    auto prompt = params->prompt;
-    if (prompt_contains_image(prompt)) {
-        if (!params->image.empty()) {
-            LOG_INF("using base64 encoded image instead of command line image path\n");
-        }
-        embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt);
-        if (!embed) {
-            LOG_ERR("%s: can't load image from prompt\n", __func__);
-            return NULL;
-        }
-        params->prompt = remove_image_from_prompt(prompt);
-    } else {
-        embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str());
-        if (!embed) {
-            fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str());
-            return NULL;
-        }
-    }
-
-    return embed;
-}
-
-static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) {
-    int n_past = 0;
-    int cur_pos_id = 0;
-
-    const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
-
-    std::string system_prompt, user_prompt;
-    size_t image_pos = prompt.find("<|vision_start|>");
-    if (image_pos != std::string::npos) {
-        // new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
-        system_prompt = prompt.substr(0, image_pos);
-        user_prompt = prompt.substr(image_pos + std::string("<|vision_pad|>").length());
-        LOG_INF("system_prompt: %s\n", system_prompt.c_str());
-        if (params->verbose_prompt) {
-            auto tmp = common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
-            for (int i = 0; i < (int) tmp.size(); i++) {
-                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
-            }
-        }
-        LOG_INF("user_prompt: %s\n", user_prompt.c_str());
-        if (params->verbose_prompt) {
-            auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
-            for (int i = 0; i < (int) tmp.size(); i++) {
-                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
-            }
-        }
-    } else {
-        // llava-1.5 native mode
-        system_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|>";
-        user_prompt = "<|vision_end|>" + prompt + "<|im_end|>\n<|im_start|>assistant\n";
-        if (params->verbose_prompt) {
-            auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
-            for (int i = 0; i < (int) tmp.size(); i++) {
-                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
-            }
-        }
-    }
-
-    eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, true);
-    if (image_embed != nullptr) {
-        auto image_size = clip_get_load_image_size(ctx_llava->ctx_clip);
-        qwen2vl_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past, &cur_pos_id, image_size);
-    }
-    eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, false);
-
-    // generate the response
-
-    LOG("\n");
-
-    struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling);
-    if (!smpl) {
-        LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
-        exit(1);
-    }
-
-    std::string response = "";
-    for (int i = 0; i < max_tgt_len; i++) {
-        const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past, &cur_pos_id);
-        response += tmp;
-        if (strcmp(tmp, "</s>") == 0) break;
-        if (strstr(tmp, "###")) break; // Yi-VL behavior
-        LOG("%s", tmp);
-        if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works)
-        if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6
-        if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6
-
-        fflush(stdout);
-    }
-
-    common_sampler_free(smpl);
-    LOG("\n");
-}
-
-static struct llama_model * llava_init(common_params * params) {
-    llama_backend_init();
-    llama_numa_init(params->numa);
-
-    llama_model_params model_params = common_model_params_to_llama(*params);
-
-    llama_model * model = llama_model_load_from_file(params->model.path.c_str(), model_params);
-    if (model == NULL) {
-        LOG_ERR("%s: unable to load model\n" , __func__);
-        return NULL;
-    }
-    return model;
-}
-
-static struct llava_context * llava_init_context(common_params * params, llama_model * model) {
-    const char * clip_path = params->mmproj.path.c_str();
-
-    auto prompt = params->prompt;
-    if (prompt.empty()) {
-        prompt = "describe the image in detail.";
-    }
-
-    auto ctx_clip = clip_model_load(clip_path, GGML_LOG_LEVEL_INFO);
-
-    llama_context_params ctx_params = common_context_params_to_llama(*params);
-    ctx_params.n_ctx           = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
-
-    llama_context * ctx_llama = llama_init_from_model(model, ctx_params);
-
-    if (ctx_llama == NULL) {
-        LOG_ERR("%s: failed to create the llama_context\n" , __func__);
-        return NULL;
-    }
-
-    auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context));
-
-    ctx_llava->ctx_llama = ctx_llama;
-    ctx_llava->ctx_clip = ctx_clip;
-    ctx_llava->model = model;
-    return ctx_llava;
-}
-
-static void llava_free(struct llava_context * ctx_llava) {
-    if (ctx_llava->ctx_clip) {
-        clip_free(ctx_llava->ctx_clip);
-        ctx_llava->ctx_clip = NULL;
-    }
-
-    llama_free(ctx_llava->ctx_llama);
-    llama_model_free(ctx_llava->model);
-    llama_backend_free();
-}
-
-#ifndef NDEBUG
-
-static void debug_test_mrope_2d() {
-    // 1. Initialize backend
-    ggml_backend_t backend = NULL;
-    std::string backend_name = "";
-#ifdef GGML_USE_CUDA
-    fprintf(stderr, "%s: using CUDA backend\n", __func__);
-    backend = ggml_backend_cuda_init(0); // init device 0
-    backend_name = "cuda";
-    if (!backend) {
-        fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
-    }
-#endif
-    // if there aren't GPU Backends fallback to CPU backend
-    if (!backend) {
-        backend = ggml_backend_cpu_init();
-        backend_name = "cpu";
-    }
-
-    // Calculate the size needed to allocate
-    size_t ctx_size = 0;
-    ctx_size += 2 * ggml_tensor_overhead(); // tensors
-    // no need to allocate anything else!
-
-    // 2. Allocate `ggml_context` to store tensor data
-    struct ggml_init_params params = {
-        /*.mem_size   =*/ ctx_size,
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true, // the tensors will be allocated later by ggml_backend_alloc_ctx_tensors()
-    };
-    struct ggml_context * ctx = ggml_init(params);
-
-    struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 128, 12, 30);
-    ggml_set_name(inp_raw, "inp_raw");
-    ggml_set_input(inp_raw);
-
-    struct ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 30 * 4);
-    ggml_set_name(pos, "pos");
-    ggml_set_input(pos);
-
-    std::vector<float> dummy_q;
-    dummy_q.resize(128 * 12 * 30);
-    std::fill(dummy_q.begin(), dummy_q.end(), 0.1);
-    // memcpy(inp_raw->data, dummy_q.data(), 128 * 12 * 30 * ggml_element_size(inp_raw));
-
-    std::vector<int> pos_id;
-    pos_id.resize(30 * 4);
-    for (int i = 0; i < 30; i ++) {
-        pos_id[i] = i;
-        pos_id[i + 30] = i + 10;
-        pos_id[i + 60] = i + 20;
-        pos_id[i + 90] = i + 30;
-    }
-    int sections[4] = {32, 32, 0, 0};
-
-    // 4. Allocate a `ggml_backend_buffer` to store all tensors
-    ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
-
-    // 5. Copy tensor data from main memory (RAM) to backend buffer
-    ggml_backend_tensor_set(inp_raw, dummy_q.data(), 0, ggml_nbytes(inp_raw));
-    ggml_backend_tensor_set(pos, pos_id.data(), 0, ggml_nbytes(pos));
-
-    // 6. Create a `ggml_cgraph` for mul_mat operation
-    struct ggml_cgraph * gf = NULL;
-    struct ggml_context * ctx_cgraph = NULL;
-
-    // create a temporally context to build the graph
-    struct ggml_init_params params0 = {
-        /*.mem_size   =*/ ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(),
-        /*.mem_buffer =*/ NULL,
-        /*.no_alloc   =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
-    };
-    ctx_cgraph = ggml_init(params0);
-    gf = ggml_new_graph(ctx_cgraph);
-
-    struct ggml_tensor * result0 = ggml_rope_multi(
-        ctx_cgraph, inp_raw, pos, nullptr,
-        128/2, sections, LLAMA_ROPE_TYPE_VISION, 32768, 1000000, 1,
-        0, 1, 32, 1);
-
-    // Add "result" tensor and all of its dependencies to the cgraph
-    ggml_build_forward_expand(gf, result0);
-
-    // 7. Create a `ggml_gallocr` for cgraph computation
-    ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
-    ggml_gallocr_alloc_graph(allocr, gf);
-
-    // 9. Run the computation
-    int n_threads = 1; // Optional: number of threads to perform some operations with multi-threading
-    if (ggml_backend_is_cpu(backend)) {
-        ggml_backend_cpu_set_n_threads(backend, n_threads);
-    }
-    ggml_backend_graph_compute(backend, gf);
-
-    // 10. Retrieve results (output tensors)
-    // in this example, output tensor is always the last tensor in the graph
-    struct ggml_tensor * result = result0;
-    // struct ggml_tensor * result = gf->nodes[gf->n_nodes - 1];
-    float * result_data = (float *)malloc(ggml_nbytes(result));
-    // because the tensor data is stored in device buffer, we need to copy it back to RAM
-    ggml_backend_tensor_get(result, result_data, 0, ggml_nbytes(result));
-    const std::string bin_file = "mrope_2d_" + backend_name +".bin";
-    std::ofstream outFile(bin_file, std::ios::binary);
-
-    if (outFile.is_open()) {
-        outFile.write(reinterpret_cast<const char*>(result_data), ggml_nbytes(result));
-        outFile.close();
-        std::cout << "Data successfully written to " + bin_file << std::endl;
-    } else {
-        std::cerr << "Error opening file!" << std::endl;
-    }
-
-    free(result_data);
-    // 11. Free memory and exit
-    ggml_free(ctx_cgraph);
-    ggml_gallocr_free(allocr);
-    ggml_free(ctx);
-    ggml_backend_buffer_free(buffer);
-    ggml_backend_free(backend);
-}
-
-static void debug_dump_img_embed(struct llava_context * ctx_llava) {
-    int n_embd  = llama_model_n_embd(llama_get_model(ctx_llava->ctx_llama));
-    int ne = n_embd * 4;
-    float vals[56 * 56 * 3];
-    // float embd[ne];
-    std::vector<float> embd;
-    embd.resize(ne);
-
-    for (int i = 0; i < 56*56; i++)
-    {
-        for (int c = 0; c < 3; c++)
-            vals[i * 3 + c] = (float)(i % (56 * 56)) / (56*56);
-    }
-
-    clip_encode_float_image(ctx_llava->ctx_clip, 16, vals, 56, 56, embd.data());
-
-    std::ofstream outFile("img_embed.bin", std::ios::binary);
-    if (outFile.is_open()) {
-        outFile.write(reinterpret_cast<const char*>(embd.data()), ne * sizeof(float));
-
-        outFile.close();
-        std::cout << "Data successfully written to mrope.bin" << std::endl;
-    } else {
-        std::cerr << "Error opening file!" << std::endl;
-    }
-}
-
-#endif
-
-
-int main(int argc, char ** argv) {
-    ggml_time_init();
-
-    common_params params;
-
-    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
-        return 1;
-    }
-
-    common_init();
-
-    if (params.mmproj.path.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
-        print_usage(argc, argv);
-        return 1;
-    }
-
-    auto * model = llava_init(&params);
-    if (model == NULL) {
-        fprintf(stderr, "%s: error: failed to init llava model\n", __func__);
-        return 1;
-    }
-
-    if (prompt_contains_image(params.prompt)) {
-        auto * ctx_llava = llava_init_context(&params, model);
-
-        auto * image_embed = load_image(ctx_llava, &params, "");
-
-        // process the prompt
-        process_prompt(ctx_llava, image_embed, &params, params.prompt);
-
-        llama_perf_context_print(ctx_llava->ctx_llama);
-        llava_image_embed_free(image_embed);
-        ctx_llava->model = NULL;
-        llava_free(ctx_llava);
-#ifndef NDEBUG
-    } else if (params.image[0].empty()) {
-        auto ctx_llava = llava_init_context(&params, model);
-
-        debug_test_mrope_2d();
-        debug_dump_img_embed(ctx_llava);
-
-        llama_perf_context_print(ctx_llava->ctx_llama);
-        ctx_llava->model = NULL;
-        llava_free(ctx_llava);
-#endif
-    } else {
-        for (auto & image : params.image) {
-            auto * ctx_llava = llava_init_context(&params, model);
-
-            auto * image_embed = load_image(ctx_llava, &params, image);
-            if (!image_embed) {
-                LOG_ERR("%s: failed to load image %s. Terminating\n\n", __func__, image.c_str());
-                return 1;
-            }
-
-            // process the prompt
-            process_prompt(ctx_llava, image_embed, &params, params.prompt);
-
-            llama_perf_context_print(ctx_llava->ctx_llama);
-            llava_image_embed_free(image_embed);
-            ctx_llava->model = NULL;
-            llava_free(ctx_llava);
-        }
-    }
-
-    llama_model_free(model);
-
-    return 0;
-}

examples/llava/tests.sh

@@ -1,81 +0,0 @@
-#!/bin/bash
-
-# make sure we are in the right directory
-SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
-cd $SCRIPT_DIR
-
-#export LLAMA_CACHE="$SCRIPT_DIR/tmp"
-
-set -eux
-
-mkdir -p $SCRIPT_DIR/output
-
-PROJ_ROOT="$SCRIPT_DIR/../.."
-cd $PROJ_ROOT
-
-###############
-
-arr_bin=()
-arr_hf=()
-
-add_test() {
-    local bin=$1
-    local hf=$2
-    arr_bin+=("$bin")
-    arr_hf+=("$hf")
-}
-
-add_test "llama-gemma3-cli"   "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M"
-add_test "llama-llava-cli"    "cmp-nct/Yi-VL-6B-GGUF:Q5_K"
-add_test "llama-llava-cli"    "guinmoon/MobileVLM-3B-GGUF:Q4_K_M"
-add_test "llama-llava-cli"    "THUDM/glm-edge-v-5b-gguf:Q4_K_M"
-add_test "llama-llava-cli"    "second-state/Llava-v1.5-7B-GGUF:Q2_K"
-add_test "llama-llava-cli"    "cjpais/llava-1.6-mistral-7b-gguf:Q3_K"
-add_test "llama-llava-cli"    "ibm-research/granite-vision-3.2-2b-GGUF:Q4_K_M"
-add_test "llama-minicpmv-cli" "second-state/MiniCPM-Llama3-V-2_5-GGUF:Q2_K" # model from openbmb is corrupted
-add_test "llama-minicpmv-cli" "openbmb/MiniCPM-V-2_6-gguf:Q2_K"
-add_test "llama-minicpmv-cli" "openbmb/MiniCPM-o-2_6-gguf:Q4_0"
-add_test "llama-qwen2vl-cli"  "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M"
-
-###############
-
-cmake --build build -j --target "${arr_bin[@]}"
-
-arr_res=()
-
-for i in "${!arr_bin[@]}"; do
-    bin="${arr_bin[$i]}"
-    hf="${arr_hf[$i]}"
-
-    echo "Running test with binary: $bin and HF model: $hf"
-    echo ""
-    echo ""
-
-    output=$("$PROJ_ROOT/build/bin/$bin" -hf "$hf" --image $SCRIPT_DIR/test-1.jpeg -p "what is the publisher name of the newspaper?" --temp 0 2>&1 | tee /dev/tty)
-
-    echo "$output" > $SCRIPT_DIR/output/$bin-$(echo "$hf" | tr '/' '-').log
-
-    if echo "$output" | grep -iq "new york"; then
-        result="\033[32mOK\033[0m:   $bin $hf"
-    else
-        result="\033[31mFAIL\033[0m: $bin $hf"
-    fi
-    echo -e "$result"
-    arr_res+=("$result")
-
-    echo ""
-    echo ""
-    echo ""
-    echo "#################################################"
-    echo "#################################################"
-    echo ""
-    echo ""
-done
-
-set +x
-
-for i in "${!arr_res[@]}"; do
-    echo -e "${arr_res[$i]}"
-done
-echo ""
-echo "Output logs are saved in $SCRIPT_DIR/output"

examples/lookahead/lookahead.cpp

@@ -50,8 +50,6 @@ int main(int argc, char ** argv) {
     const int N = 5;  // n-gram size
     const int G = 15; // max verification n-grams
 
-    const bool dump_kv_cache = params.dump_kv_cache;
-
     // init llama.cpp
     llama_backend_init();
     llama_numa_init(params.numa);
@@ -152,9 +150,6 @@ int main(int argc, char ** argv) {
     // here we keep adding new n-grams as we go
     ngram_container ngrams_observed(llama_vocab_n_tokens(vocab), N, G);
 
-    // debug
-    struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, W + G + 1);
-
     const auto t_dec_start = ggml_time_us();
 
     // sample first token
@@ -172,12 +167,6 @@ int main(int argc, char ** argv) {
     }
 
     while (true) {
-        // debug
-        if (dump_kv_cache) {
-            llama_kv_cache_view_update(ctx, &kvc_view);
-            common_kv_cache_dump_view_seqs(kvc_view, 40);
-        }
-
         // build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/
         //
         // Example for W = 5, N = 4, G = 2:
@@ -473,8 +462,6 @@ int main(int argc, char ** argv) {
 
     common_sampler_free(smpl);
 
-    llama_kv_cache_view_free(&kvc_view);
-
     llama_batch_free(batch);
 
     llama_backend_free();

examples/lookup/lookup.cpp

@@ -24,8 +24,6 @@ int main(int argc, char ** argv){
     // max. number of additional tokens to draft if match is found
     const int n_draft = params.speculative.n_max;
 
-    const bool dump_kv_cache = params.dump_kv_cache;
-
     // init llama.cpp
     llama_backend_init();
     llama_numa_init(params.numa);
@@ -110,18 +108,9 @@ int main(int argc, char ** argv){
 
     llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, 1);
 
-    // debug
-    struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, 1);
-
     const auto t_dec_start = ggml_time_us();
 
     while (true) {
-        // debug
-        if (dump_kv_cache) {
-            llama_kv_cache_view_update(ctx, &kvc_view);
-            common_kv_cache_dump_view_seqs(kvc_view, 40);
-        }
-
         // print current draft sequence
         LOG_DBG("drafted %s\n", string_from(ctx, draft).c_str());
 

examples/parallel/README.md

@@ -1,3 +1,14 @@
 # llama.cpp/example/parallel
 
 Simplified simulation of serving incoming requests in parallel
+
+## Example
+
+Generate 128 client requests (`-ns 128`), simulating 8 concurrent clients (`-np 8`). The system prompt is shared (`-pps`), meaning that it is computed once at the start. The client requests consist of 10 junk questions (`-j 10`) followed by the actual question.
+
+```bash
+llama-parallel -m model.gguf -np 8 -ns 128 --top-k 1 -pps --junk 10 -c 16384
+```
+
+> [!NOTE]
+> It's recommended to use base models with this example. Instruction tuned models might not be able to properly follow the custom chat template specified here, so the results might not be as expected.

examples/parallel/parallel.cpp

@@ -34,11 +34,61 @@ static std::string k_system =
 R"(Transcript of a never ending dialog, where the User interacts with an Assistant.
 The Assistant is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.
 
-User: Recommend a nice restaurant in the area.
-Assistant: I recommend the restaurant "The Golden Duck". It is a 5 star restaurant with a great view of the city. The food is delicious and the service is excellent. The prices are reasonable and the portions are generous. The restaurant is located at 123 Main Street, New York, NY 10001. The phone number is (212) 555-1234. The hours are Monday through Friday from 11:00 am to 10:00 pm. The restaurant is closed on Saturdays and Sundays.
-User: Who is Richard Feynman?
-Assistant: Richard Feynman was an American physicist who is best known for his work in quantum mechanics and particle physics. He was awarded the Nobel Prize in Physics in 1965 for his contributions to the development of quantum electrodynamics. He was a popular lecturer and author, and he wrote several books, including "Surely You're Joking, Mr. Feynman!" and "What Do You Care What Other People Think?".
-User:)";
+User:
+Recommend a nice restaurant in the area.
+Assistant:
+I recommend the restaurant "The Golden Duck". It is a 5 star restaurant with a great view of the city. The food is delicious and the service is excellent. The prices are reasonable and the portions are generous. The restaurant is located at 123 Main Street, New York, NY 10001. The phone number is (212) 555-1234. The hours are Monday through Friday from 11:00 am to 10:00 pm. The restaurant is closed on Saturdays and Sundays.
+User:
+Who is Richard Feynman?
+Assistant:
+Richard Feynman was an American physicist who is best known for his work in quantum mechanics and particle physics. He was awarded the Nobel Prize in Physics in 1965 for his contributions to the development of quantum electrodynamics. He was a popular lecturer and author, and he wrote several books, including "Surely You're Joking, Mr. Feynman!" and "What Do You Care What Other People Think?".
+)";
+
+static std::vector<std::string> k_questions = {
+    "What is the tallest mountain in the world?",
+    "Who was the first person to win two Nobel Prizes?",
+    "Which country invented paper?",
+    "What organ is primarily responsible for pumping blood throughout the body?",
+    "Which planet is known for its prominent ring system?",
+    "Who directed the movie 'Inception'?",
+    "What is the freezing point of water in Fahrenheit?",
+    "Which animal is known to have the longest lifespan?",
+    "What language has the most native speakers worldwide?",
+    "What is the capital city of Canada?",
+    "Who is credited with inventing the World Wide Web?",
+    "Which metal is liquid at room temperature?",
+    "What is the term for an animal that eats both plants and meat?",
+    "Who painted 'The Starry Night'?",
+    "What gas do humans exhale that plants use for photosynthesis?",
+    "What year did World War II end?",
+    "Which continent has the most countries?",
+    "Who wrote the novel 'Frankenstein'?",
+    "What does DNA stand for?",
+    "What is the main ingredient in traditional Japanese miso soup?"
+};
+
+static std::vector<std::string> k_answers = {
+    "The tallest mountain in the world is Mount Everest.",
+    "Marie Curie was the first person to win two Nobel Prizes.",
+    "Paper was invented in China.",
+    "The heart is the organ responsible for pumping blood.",
+    "Saturn is known for its prominent ring system.",
+    "Christopher Nolan directed the movie 'Inception'.",
+    "The freezing point of water in Fahrenheit is 32°F.",
+    "The bowhead whale is known to have the longest lifespan among mammals.",
+    "Mandarin Chinese has the most native speakers in the world.",
+    "The capital city of Canada is Ottawa.",
+    "Tim Berners-Lee is credited with inventing the World Wide Web.",
+    "Mercury is the metal that is liquid at room temperature.",
+    "An animal that eats both plants and meat is called an omnivore.",
+    "'The Starry Night' was painted by Vincent van Gogh.",
+    "Humans exhale carbon dioxide, which plants use in photosynthesis.",
+    "World War II ended in 1945.",
+    "Africa is the continent with the most countries.",
+    "The novel 'Frankenstein' was written by Mary Shelley.",
+    "DNA stands for Deoxyribonucleic Acid.",
+    "The main ingredient in traditional Japanese miso soup is fermented soybean paste."
+};
 
 static std::vector<std::string> k_prompts = {
     "What is the meaning of life?",
@@ -49,7 +99,7 @@ static std::vector<std::string> k_prompts = {
     "What is the best way to learn a new language?",
     "How to get a job at Google?",
     "If you could have any superpower, what would it be?",
-    "I want to learn how to play the piano.",
+    "I want to learn how to play the piano. What would be the best way to do it?",
 };
 
 struct client {
@@ -68,6 +118,7 @@ struct client {
     int64_t t_start_prompt;
     int64_t t_start_gen;
 
+    int32_t n_past    = 0;
     int32_t n_prompt  = 0;
     int32_t n_decoded = 0;
     int32_t i_batch   = -1;
@@ -107,6 +158,7 @@ int main(int argc, char ** argv) {
     common_params params;
 
     params.n_predict = 128;
+    params.n_junk = 0;
 
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PARALLEL)) {
         return 1;
@@ -126,7 +178,11 @@ int main(int argc, char ** argv) {
     // insert new requests as soon as the previous one is done
     const bool cont_batching = params.cont_batching;
 
-    const bool dump_kv_cache = params.dump_kv_cache;
+    // is the system prompt shared in the cache
+    const bool is_sp_shared = params.is_pp_shared;
+
+    // extra text to insert in each client's prompt in order to make it larger
+    const int32_t n_junk = params.n_junk;
 
     // init llama.cpp
     llama_backend_init();
@@ -169,6 +225,7 @@ int main(int argc, char ** argv) {
     }
 
     std::vector<llama_token> tokens_system;
+
     tokens_system = common_tokenize(ctx, k_system, true);
     const int32_t n_tokens_system = tokens_system.size();
 
@@ -182,15 +239,13 @@ int main(int argc, char ** argv) {
     int32_t n_total_gen    = 0;
     int32_t n_cache_miss   = 0;
 
-    struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, n_clients);
-
     const auto t_main_start = ggml_time_us();
 
     LOG_INF("%s: Simulating parallel requests from clients:\n", __func__);
     LOG_INF("%s: n_parallel = %d, n_sequences = %d, cont_batching = %d, system tokens = %d\n", __func__, n_clients, n_seq, cont_batching, n_tokens_system);
     LOG_INF("\n");
 
-    {
+    if (is_sp_shared) {
         LOG_INF("%s: Evaluating the system prompt ...\n", __func__);
 
         for (int32_t i = 0; i < n_tokens_system; ++i) {
@@ -213,11 +268,6 @@ int main(int argc, char ** argv) {
     LOG_INF("Processing requests ...\n\n");
 
     while (true) {
-        if (dump_kv_cache) {
-            llama_kv_cache_view_update(ctx, &kvc_view);
-            common_kv_cache_dump_view_seqs(kvc_view, 40);
-        }
-
         common_batch_clear(batch);
 
         // decode any currently ongoing sequences
@@ -228,7 +278,7 @@ int main(int argc, char ** argv) {
 
             client.i_batch = batch.n_tokens;
 
-            common_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id + 1 }, true);
+            common_batch_add(batch, client.sampled, client.n_past++, { client.id + 1 }, true);
 
             client.n_decoded += 1;
         }
@@ -254,9 +304,23 @@ int main(int argc, char ** argv) {
                     client.t_start_gen    = 0;
 
                     client.input    = k_prompts[rand() % k_prompts.size()];
-                    client.prompt   = client.input + "\nAssistant:";
                     client.response = "";
 
+                    // construct the prompt:
+                    // [system prompt] + [junk] + [user prompt]
+                    client.n_past = 0;
+                    client.prompt = "";
+                    if (is_sp_shared) {
+                        client.n_past = n_tokens_system;
+                    } else {
+                        client.prompt += k_system;
+                    }
+                    for (int i = 0; i < n_junk; ++i) {
+                        const int r = rand() % k_questions.size();
+                        client.prompt += "User:\n" + k_questions[r] + "\nAssistant:\n " + k_answers[r] + "\n";
+                    }
+                    client.prompt += "User:\n" + client.input + "\nAssistant:\n";
+
                     common_sampler_reset(client.smpl);
 
                     // do not prepend BOS because we have a system prompt!
@@ -264,7 +328,7 @@ int main(int argc, char ** argv) {
                     tokens_prompt = common_tokenize(ctx, client.prompt, false);
 
                     for (size_t i = 0; i < tokens_prompt.size(); ++i) {
-                        common_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id + 1 }, false);
+                        common_batch_add(batch, tokens_prompt[i], client.n_past++, { client.id + 1 }, false);
                     }
 
                     // extract the logits only for the last token
@@ -363,10 +427,9 @@ int main(int argc, char ** argv) {
                 //        client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str());
 
                 if (client.n_decoded > 2 &&
-                        (llama_vocab_is_eog(vocab, id) ||
-                         (params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) ||
-                         client.response.find("User:") != std::string::npos ||
-                         client.response.find('\n') != std::string::npos)) {
+                    (llama_vocab_is_eog(vocab, id) ||
+                     (params.n_predict > 0 && client.n_decoded >= params.n_predict) ||
+                     client.response.find("User:") != std::string::npos)) {
                     // basic reverse prompt
                     const size_t pos = client.response.find("User:");
                     if (pos != std::string::npos) {

examples/pydantic_models_to_grammar_examples.py

@@ -23,7 +23,7 @@ def create_completion(host, prompt, gbnf_grammar):
     """Calls the /completion API on llama-server.
 
     See
-    https://github.com/ggml-org/llama.cpp/tree/HEAD/examples/server#api-endpoints
+    https://github.com/ggml-org/llama.cpp/tree/HEAD/tools/server#api-endpoints
     """
     print(f"  Request:\n    Grammar:\n{textwrap.indent(gbnf_grammar, '      ')}\n    Prompt:\n{textwrap.indent(prompt.rstrip(), '      ')}")
     headers = {"Content-Type": "application/json"}

examples/quantize-stats/CMakeLists.txt

@@ -1,6 +0,0 @@
-set(TARGET llama-quantize-stats)
-add_executable(${TARGET} quantize-stats.cpp)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})
-target_include_directories(${TARGET} PRIVATE ../../common)
-target_compile_features(${TARGET} PRIVATE cxx_std_17)

examples/server/webui/src/components/ChatScreen.tsx

@@ -1,296 +0,0 @@
-import { useEffect, useMemo, useState } from 'react';
-import { CallbackGeneratedChunk, useAppContext } from '../utils/app.context';
-import ChatMessage from './ChatMessage';
-import { CanvasType, Message, PendingMessage } from '../utils/types';
-import { classNames, cleanCurrentUrl, throttle } from '../utils/misc';
-import CanvasPyInterpreter from './CanvasPyInterpreter';
-import StorageUtils from '../utils/storage';
-import { useVSCodeContext } from '../utils/llama-vscode';
-import { useChatTextarea, ChatTextareaApi } from './useChatTextarea.ts';
-
-/**
- * A message display is a message node with additional information for rendering.
- * For example, siblings of the message node are stored as their last node (aka leaf node).
- */
-export interface MessageDisplay {
-  msg: Message | PendingMessage;
-  siblingLeafNodeIds: Message['id'][];
-  siblingCurrIdx: number;
-  isPending?: boolean;
-}
-
-/**
- * If the current URL contains "?m=...", prefill the message input with the value.
- * If the current URL contains "?q=...", prefill and SEND the message.
- */
-const prefilledMsg = {
-  content() {
-    const url = new URL(window.location.href);
-    return url.searchParams.get('m') ?? url.searchParams.get('q') ?? '';
-  },
-  shouldSend() {
-    const url = new URL(window.location.href);
-    return url.searchParams.has('q');
-  },
-  clear() {
-    cleanCurrentUrl(['m', 'q']);
-  },
-};
-
-function getListMessageDisplay(
-  msgs: Readonly<Message[]>,
-  leafNodeId: Message['id']
-): MessageDisplay[] {
-  const currNodes = StorageUtils.filterByLeafNodeId(msgs, leafNodeId, true);
-  const res: MessageDisplay[] = [];
-  const nodeMap = new Map<Message['id'], Message>();
-  for (const msg of msgs) {
-    nodeMap.set(msg.id, msg);
-  }
-  // find leaf node from a message node
-  const findLeafNode = (msgId: Message['id']): Message['id'] => {
-    let currNode: Message | undefined = nodeMap.get(msgId);
-    while (currNode) {
-      if (currNode.children.length === 0) break;
-      currNode = nodeMap.get(currNode.children.at(-1) ?? -1);
-    }
-    return currNode?.id ?? -1;
-  };
-  // traverse the current nodes
-  for (const msg of currNodes) {
-    const parentNode = nodeMap.get(msg.parent ?? -1);
-    if (!parentNode) continue;
-    const siblings = parentNode.children;
-    if (msg.type !== 'root') {
-      res.push({
-        msg,
-        siblingLeafNodeIds: siblings.map(findLeafNode),
-        siblingCurrIdx: siblings.indexOf(msg.id),
-      });
-    }
-  }
-  return res;
-}
-
-const scrollToBottom = throttle(
-  (requiresNearBottom: boolean, delay: number = 80) => {
-    const mainScrollElem = document.getElementById('main-scroll');
-    if (!mainScrollElem) return;
-    const spaceToBottom =
-      mainScrollElem.scrollHeight -
-      mainScrollElem.scrollTop -
-      mainScrollElem.clientHeight;
-    if (!requiresNearBottom || spaceToBottom < 50) {
-      setTimeout(
-        () => mainScrollElem.scrollTo({ top: mainScrollElem.scrollHeight }),
-        delay
-      );
-    }
-  },
-  80
-);
-
-export default function ChatScreen() {
-  const {
-    viewingChat,
-    sendMessage,
-    isGenerating,
-    stopGenerating,
-    pendingMessages,
-    canvasData,
-    replaceMessageAndGenerate,
-  } = useAppContext();
-
-  const textarea: ChatTextareaApi = useChatTextarea(prefilledMsg.content());
-
-  const { extraContext, clearExtraContext } = useVSCodeContext(textarea);
-  // TODO: improve this when we have "upload file" feature
-  const currExtra: Message['extra'] = extraContext ? [extraContext] : undefined;
-
-  // keep track of leaf node for rendering
-  const [currNodeId, setCurrNodeId] = useState<number>(-1);
-  const messages: MessageDisplay[] = useMemo(() => {
-    if (!viewingChat) return [];
-    else return getListMessageDisplay(viewingChat.messages, currNodeId);
-  }, [currNodeId, viewingChat]);
-
-  const currConvId = viewingChat?.conv.id ?? null;
-  const pendingMsg: PendingMessage | undefined =
-    pendingMessages[currConvId ?? ''];
-
-  useEffect(() => {
-    // reset to latest node when conversation changes
-    setCurrNodeId(-1);
-    // scroll to bottom when conversation changes
-    scrollToBottom(false, 1);
-  }, [currConvId]);
-
-  const onChunk: CallbackGeneratedChunk = (currLeafNodeId?: Message['id']) => {
-    if (currLeafNodeId) {
-      setCurrNodeId(currLeafNodeId);
-    }
-    scrollToBottom(true);
-  };
-
-  const sendNewMessage = async () => {
-    const lastInpMsg = textarea.value();
-    if (lastInpMsg.trim().length === 0 || isGenerating(currConvId ?? ''))
-      return;
-    textarea.setValue('');
-    scrollToBottom(false);
-    setCurrNodeId(-1);
-    // get the last message node
-    const lastMsgNodeId = messages.at(-1)?.msg.id ?? null;
-    if (
-      !(await sendMessage(
-        currConvId,
-        lastMsgNodeId,
-        lastInpMsg,
-        currExtra,
-        onChunk
-      ))
-    ) {
-      // restore the input message if failed
-      textarea.setValue(lastInpMsg);
-    }
-    // OK
-    clearExtraContext();
-  };
-
-  const handleEditMessage = async (msg: Message, content: string) => {
-    if (!viewingChat) return;
-    setCurrNodeId(msg.id);
-    scrollToBottom(false);
-    await replaceMessageAndGenerate(
-      viewingChat.conv.id,
-      msg.parent,
-      content,
-      msg.extra,
-      onChunk
-    );
-    setCurrNodeId(-1);
-    scrollToBottom(false);
-  };
-
-  const handleRegenerateMessage = async (msg: Message) => {
-    if (!viewingChat) return;
-    setCurrNodeId(msg.parent);
-    scrollToBottom(false);
-    await replaceMessageAndGenerate(
-      viewingChat.conv.id,
-      msg.parent,
-      null,
-      msg.extra,
-      onChunk
-    );
-    setCurrNodeId(-1);
-    scrollToBottom(false);
-  };
-
-  const hasCanvas = !!canvasData;
-
-  useEffect(() => {
-    if (prefilledMsg.shouldSend()) {
-      // send the prefilled message if needed
-      sendNewMessage();
-    } else {
-      // otherwise, focus on the input
-      textarea.focus();
-    }
-    prefilledMsg.clear();
-    // no need to keep track of sendNewMessage
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [textarea.ref]);
-
-  // due to some timing issues of StorageUtils.appendMsg(), we need to make sure the pendingMsg is not duplicated upon rendering (i.e. appears once in the saved conversation and once in the pendingMsg)
-  const pendingMsgDisplay: MessageDisplay[] =
-    pendingMsg && messages.at(-1)?.msg.id !== pendingMsg.id
-      ? [
-          {
-            msg: pendingMsg,
-            siblingLeafNodeIds: [],
-            siblingCurrIdx: 0,
-            isPending: true,
-          },
-        ]
-      : [];
-
-  return (
-    <div
-      className={classNames({
-        'grid lg:gap-8 grow transition-[300ms]': true,
-        'grid-cols-[1fr_0fr] lg:grid-cols-[1fr_1fr]': hasCanvas, // adapted for mobile
-        'grid-cols-[1fr_0fr]': !hasCanvas,
-      })}
-    >
-      <div
-        className={classNames({
-          'flex flex-col w-full max-w-[900px] mx-auto': true,
-          'hidden lg:flex': hasCanvas, // adapted for mobile
-          flex: !hasCanvas,
-        })}
-      >
-        {/* chat messages */}
-        <div id="messages-list" className="grow">
-          <div className="mt-auto flex justify-center">
-            {/* placeholder to shift the message to the bottom */}
-            {viewingChat ? '' : 'Send a message to start'}
-          </div>
-          {[...messages, ...pendingMsgDisplay].map((msg) => (
-            <ChatMessage
-              key={msg.msg.id}
-              msg={msg.msg}
-              siblingLeafNodeIds={msg.siblingLeafNodeIds}
-              siblingCurrIdx={msg.siblingCurrIdx}
-              onRegenerateMessage={handleRegenerateMessage}
-              onEditMessage={handleEditMessage}
-              onChangeSibling={setCurrNodeId}
-            />
-          ))}
-        </div>
-
-        {/* chat input */}
-        <div className="flex flex-row items-end pt-8 pb-6 sticky bottom-0 bg-base-100">
-          <textarea
-            // Default (mobile): Enable vertical resize, overflow auto for scrolling if needed
-            // Large screens (lg:): Disable manual resize, apply max-height for autosize limit
-            className="textarea textarea-bordered w-full resize-vertical lg:resize-none lg:max-h-48 lg:overflow-y-auto" // Adjust lg:max-h-48 as needed (e.g., lg:max-h-60)
-            placeholder="Type a message (Shift+Enter to add a new line)"
-            ref={textarea.ref}
-            onInput={textarea.onInput} // Hook's input handler (will only resize height on lg+ screens)
-            onKeyDown={(e) => {
-              if (e.nativeEvent.isComposing || e.keyCode === 229) return;
-              if (e.key === 'Enter' && !e.shiftKey) {
-                e.preventDefault();
-                sendNewMessage();
-              }
-            }}
-            id="msg-input"
-            dir="auto"
-            // Set a base height of 2 rows for mobile views
-            // On lg+ screens, the hook will calculate and set the initial height anyway
-            rows={2}
-          ></textarea>
-
-          {isGenerating(currConvId ?? '') ? (
-            <button
-              className="btn btn-neutral ml-2"
-              onClick={() => stopGenerating(currConvId ?? '')}
-            >
-              Stop
-            </button>
-          ) : (
-            <button className="btn btn-primary ml-2" onClick={sendNewMessage}>
-              Send
-            </button>
-          )}
-        </div>
-      </div>
-      <div className="w-full sticky top-[7em] h-[calc(100vh-9em)]">
-        {canvasData?.type === CanvasType.PY_INTERPRETER && (
-          <CanvasPyInterpreter />
-        )}
-      </div>
-    </div>
-  );
-}

examples/server/webui/src/components/Header.tsx

@@ -1,178 +0,0 @@
-import { useEffect, useState } from 'react';
-import StorageUtils from '../utils/storage';
-import { useAppContext } from '../utils/app.context';
-import { classNames } from '../utils/misc';
-import daisyuiThemes from 'daisyui/theme/object';
-import { THEMES } from '../Config';
-import { useNavigate } from 'react-router';
-
-export default function Header() {
-  const navigate = useNavigate();
-  const [selectedTheme, setSelectedTheme] = useState(StorageUtils.getTheme());
-  const { setShowSettings } = useAppContext();
-
-  const setTheme = (theme: string) => {
-    StorageUtils.setTheme(theme);
-    setSelectedTheme(theme);
-  };
-
-  useEffect(() => {
-    document.body.setAttribute('data-theme', selectedTheme);
-    document.body.setAttribute(
-      'data-color-scheme',
-      daisyuiThemes[selectedTheme]?.['color-scheme'] ?? 'auto'
-    );
-  }, [selectedTheme]);
-
-  const { isGenerating, viewingChat } = useAppContext();
-  const isCurrConvGenerating = isGenerating(viewingChat?.conv.id ?? '');
-
-  const removeConversation = () => {
-    if (isCurrConvGenerating || !viewingChat) return;
-    const convId = viewingChat?.conv.id;
-    if (window.confirm('Are you sure to delete this conversation?')) {
-      StorageUtils.remove(convId);
-      navigate('/');
-    }
-  };
-
-  const downloadConversation = () => {
-    if (isCurrConvGenerating || !viewingChat) return;
-    const convId = viewingChat?.conv.id;
-    const conversationJson = JSON.stringify(viewingChat, null, 2);
-    const blob = new Blob([conversationJson], { type: 'application/json' });
-    const url = URL.createObjectURL(blob);
-    const a = document.createElement('a');
-    a.href = url;
-    a.download = `conversation_${convId}.json`;
-    document.body.appendChild(a);
-    a.click();
-    document.body.removeChild(a);
-    URL.revokeObjectURL(url);
-  };
-
-  return (
-    <div className="flex flex-row items-center pt-6 pb-6 sticky top-0 z-10 bg-base-100">
-      {/* open sidebar button */}
-      <label htmlFor="toggle-drawer" className="btn btn-ghost lg:hidden">
-        <svg
-          xmlns="http://www.w3.org/2000/svg"
-          width="16"
-          height="16"
-          fill="currentColor"
-          className="bi bi-list"
-          viewBox="0 0 16 16"
-        >
-          <path
-            fillRule="evenodd"
-            d="M2.5 12a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5"
-          />
-        </svg>
-      </label>
-
-      <div className="grow text-2xl font-bold ml-2">llama.cpp</div>
-
-      {/* action buttons (top right) */}
-      <div className="flex items-center">
-        {viewingChat && (
-          <div className="dropdown dropdown-end">
-            {/* "..." button */}
-            <button
-              tabIndex={0}
-              role="button"
-              className="btn m-1"
-              disabled={isCurrConvGenerating}
-            >
-              <svg
-                xmlns="http://www.w3.org/2000/svg"
-                width="16"
-                height="16"
-                fill="currentColor"
-                className="bi bi-three-dots-vertical"
-                viewBox="0 0 16 16"
-              >
-                <path d="M9.5 13a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0" />
-              </svg>
-            </button>
-            {/* dropdown menu */}
-            <ul
-              tabIndex={0}
-              className="dropdown-content menu bg-base-100 rounded-box z-[1] w-52 p-2 shadow"
-            >
-              <li onClick={downloadConversation}>
-                <a>Download</a>
-              </li>
-              <li className="text-error" onClick={removeConversation}>
-                <a>Delete</a>
-              </li>
-            </ul>
-          </div>
-        )}
-
-        <div className="tooltip tooltip-bottom" data-tip="Settings">
-          <button className="btn" onClick={() => setShowSettings(true)}>
-            {/* settings button */}
-            <svg
-              xmlns="http://www.w3.org/2000/svg"
-              width="16"
-              height="16"
-              fill="currentColor"
-              className="bi bi-gear"
-              viewBox="0 0 16 16"
-            >
-              <path d="M8 4.754a3.246 3.246 0 1 0 0 6.492 3.246 3.246 0 0 0 0-6.492M5.754 8a2.246 2.246 0 1 1 4.492 0 2.246 2.246 0 0 1-4.492 0" />
-              <path d="M9.796 1.343c-.527-1.79-3.065-1.79-3.592 0l-.094.319a.873.873 0 0 1-1.255.52l-.292-.16c-1.64-.892-3.433.902-2.54 2.541l.159.292a.873.873 0 0 1-.52 1.255l-.319.094c-1.79.527-1.79 3.065 0 3.592l.319.094a.873.873 0 0 1 .52 1.255l-.16.292c-.892 1.64.901 3.434 2.541 2.54l.292-.159a.873.873 0 0 1 1.255.52l.094.319c.527 1.79 3.065 1.79 3.592 0l.094-.319a.873.873 0 0 1 1.255-.52l.292.16c1.64.893 3.434-.902 2.54-2.541l-.159-.292a.873.873 0 0 1 .52-1.255l.319-.094c1.79-.527 1.79-3.065 0-3.592l-.319-.094a.873.873 0 0 1-.52-1.255l.16-.292c.893-1.64-.902-3.433-2.541-2.54l-.292.159a.873.873 0 0 1-1.255-.52zm-2.633.283c.246-.835 1.428-.835 1.674 0l.094.319a1.873 1.873 0 0 0 2.693 1.115l.291-.16c.764-.415 1.6.42 1.184 1.185l-.159.292a1.873 1.873 0 0 0 1.116 2.692l.318.094c.835.246.835 1.428 0 1.674l-.319.094a1.873 1.873 0 0 0-1.115 2.693l.16.291c.415.764-.42 1.6-1.185 1.184l-.291-.159a1.873 1.873 0 0 0-2.693 1.116l-.094.318c-.246.835-1.428.835-1.674 0l-.094-.319a1.873 1.873 0 0 0-2.692-1.115l-.292.16c-.764.415-1.6-.42-1.184-1.185l.159-.291A1.873 1.873 0 0 0 1.945 8.93l-.319-.094c-.835-.246-.835-1.428 0-1.674l.319-.094A1.873 1.873 0 0 0 3.06 4.377l-.16-.292c-.415-.764.42-1.6 1.185-1.184l.292.159a1.873 1.873 0 0 0 2.692-1.115z" />
-            </svg>
-          </button>
-        </div>
-
-        {/* theme controller is copied from https://daisyui.com/components/theme-controller/ */}
-        <div className="tooltip tooltip-bottom" data-tip="Themes">
-          <div className="dropdown dropdown-end dropdown-bottom">
-            <div tabIndex={0} role="button" className="btn m-1">
-              <svg
-                xmlns="http://www.w3.org/2000/svg"
-                width="16"
-                height="16"
-                fill="currentColor"
-                className="bi bi-palette2"
-                viewBox="0 0 16 16"
-              >
-                <path d="M0 .5A.5.5 0 0 1 .5 0h5a.5.5 0 0 1 .5.5v5.277l4.147-4.131a.5.5 0 0 1 .707 0l3.535 3.536a.5.5 0 0 1 0 .708L10.261 10H15.5a.5.5 0 0 1 .5.5v5a.5.5 0 0 1-.5.5H3a3 3 0 0 1-2.121-.879A3 3 0 0 1 0 13.044m6-.21 7.328-7.3-2.829-2.828L6 7.188zM4.5 13a1.5 1.5 0 1 0-3 0 1.5 1.5 0 0 0 3 0M15 15v-4H9.258l-4.015 4zM0 .5v12.495zm0 12.495V13z" />
-              </svg>
-            </div>
-            <ul
-              tabIndex={0}
-              className="dropdown-content bg-base-300 rounded-box z-[1] w-52 p-2 shadow-2xl h-80 overflow-y-auto"
-            >
-              <li>
-                <button
-                  className={classNames({
-                    'btn btn-sm btn-block btn-ghost justify-start': true,
-                    'btn-active': selectedTheme === 'auto',
-                  })}
-                  onClick={() => setTheme('auto')}
-                >
-                  auto
-                </button>
-              </li>
-              {THEMES.map((theme) => (
-                <li key={theme}>
-                  <input
-                    type="radio"
-                    name="theme-dropdown"
-                    className="theme-controller btn btn-sm btn-block btn-ghost justify-start"
-                    aria-label={theme}
-                    value={theme}
-                    checked={selectedTheme === theme}
-                    onChange={(e) => e.target.checked && setTheme(theme)}
-                  />
-                </li>
-              ))}
-            </ul>
-          </div>
-        </div>
-      </div>
-    </div>
-  );
-}

examples/server/webui/src/components/Sidebar.tsx

@@ -1,96 +0,0 @@
-import { useEffect, useState } from 'react';
-import { classNames } from '../utils/misc';
-import { Conversation } from '../utils/types';
-import StorageUtils from '../utils/storage';
-import { useNavigate, useParams } from 'react-router';
-
-export default function Sidebar() {
-  const params = useParams();
-  const navigate = useNavigate();
-
-  const [conversations, setConversations] = useState<Conversation[]>([]);
-  const [currConv, setCurrConv] = useState<Conversation | null>(null);
-
-  useEffect(() => {
-    StorageUtils.getOneConversation(params.convId ?? '').then(setCurrConv);
-  }, [params.convId]);
-
-  useEffect(() => {
-    const handleConversationChange = async () => {
-      setConversations(await StorageUtils.getAllConversations());
-    };
-    StorageUtils.onConversationChanged(handleConversationChange);
-    handleConversationChange();
-    return () => {
-      StorageUtils.offConversationChanged(handleConversationChange);
-    };
-  }, []);
-
-  return (
-    <>
-      <input
-        id="toggle-drawer"
-        type="checkbox"
-        className="drawer-toggle"
-        defaultChecked
-      />
-
-      <div className="drawer-side h-screen lg:h-screen z-50 lg:max-w-64">
-        <label
-          htmlFor="toggle-drawer"
-          aria-label="close sidebar"
-          className="drawer-overlay"
-        ></label>
-        <div className="flex flex-col bg-base-200 min-h-full max-w-64 py-4 px-4">
-          <div className="flex flex-row items-center justify-between mb-4 mt-4">
-            <h2 className="font-bold ml-4">Conversations</h2>
-
-            {/* close sidebar button */}
-            <label htmlFor="toggle-drawer" className="btn btn-ghost lg:hidden">
-              <svg
-                xmlns="http://www.w3.org/2000/svg"
-                width="16"
-                height="16"
-                fill="currentColor"
-                className="bi bi-arrow-bar-left"
-                viewBox="0 0 16 16"
-              >
-                <path
-                  fillRule="evenodd"
-                  d="M12.5 15a.5.5 0 0 1-.5-.5v-13a.5.5 0 0 1 1 0v13a.5.5 0 0 1-.5.5M10 8a.5.5 0 0 1-.5.5H3.707l2.147 2.146a.5.5 0 0 1-.708.708l-3-3a.5.5 0 0 1 0-.708l3-3a.5.5 0 1 1 .708.708L3.707 7.5H9.5a.5.5 0 0 1 .5.5"
-                />
-              </svg>
-            </label>
-          </div>
-
-          {/* list of conversations */}
-          <div
-            className={classNames({
-              'btn btn-ghost justify-start': true,
-              'btn-active': !currConv,
-            })}
-            onClick={() => navigate('/')}
-          >
-            + New conversation
-          </div>
-          {conversations.map((conv) => (
-            <div
-              key={conv.id}
-              className={classNames({
-                'btn btn-ghost justify-start font-normal': true,
-                'btn-active': conv.id === currConv?.id,
-              })}
-              onClick={() => navigate(`/chat/${conv.id}`)}
-              dir="auto"
-            >
-              <span className="truncate">{conv.name}</span>
-            </div>
-          ))}
-          <div className="text-center text-xs opacity-40 mt-auto mx-4">
-            Conversations are saved to browser's IndexedDB
-          </div>
-        </div>
-      </div>
-    </>
-  );
-}

examples/server/webui/src/utils/common.tsx

@@ -1,38 +0,0 @@
-export const XCloseButton: React.ElementType<
-  React.ClassAttributes<HTMLButtonElement> &
-    React.HTMLAttributes<HTMLButtonElement>
-> = ({ className, ...props }) => (
-  <button className={`btn btn-square btn-sm ${className ?? ''}`} {...props}>
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      className="h-6 w-6"
-      fill="none"
-      viewBox="0 0 24 24"
-      stroke="currentColor"
-    >
-      <path
-        strokeLinecap="round"
-        strokeLinejoin="round"
-        strokeWidth="2"
-        d="M6 18L18 6M6 6l12 12"
-      />
-    </svg>
-  </button>
-);
-
-export const OpenInNewTab = ({
-  href,
-  children,
-}: {
-  href: string;
-  children: string;
-}) => (
-  <a
-    className="underline"
-    href={href}
-    target="_blank"
-    rel="noopener noreferrer"
-  >
-    {children}
-  </a>
-);

examples/simple-chat/simple-chat.cpp

@@ -98,7 +98,7 @@ int main(int argc, char ** argv) {
     auto generate = [&](const std::string & prompt) {
         std::string response;
 
-        const bool is_first = llama_kv_self_used_cells(ctx) == 0;
+        const bool is_first = llama_kv_self_seq_pos_max(ctx, 0) == 0;
 
         // tokenize the prompt
         const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);
@@ -113,7 +113,7 @@ int main(int argc, char ** argv) {
         while (true) {
             // check if we have enough space in the context to evaluate this batch
             int n_ctx = llama_n_ctx(ctx);
-            int n_ctx_used = llama_kv_self_used_cells(ctx);
+            int n_ctx_used = llama_kv_self_seq_pos_max(ctx, 0);
             if (n_ctx_used + batch.n_tokens > n_ctx) {
                 printf("\033[0m\n");
                 fprintf(stderr, "context size exceeded\n");