sync : ggml

ggml-ci

.devops/intel.Dockerfile

@@ -1,4 +1,4 @@
-ARG ONEAPI_VERSION=2025.1.1-0-devel-ubuntu24.04
+ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
 
 ## Build Image
 

.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=rc4.0.1
+ARG MUSA_VERSION=rc3.1.1
 # Target the MUSA build image
-ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-mudnn-devel-ubuntu${UBUNTU_VERSION}
+ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
 
-ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-mudnn-runtime-ubuntu${UBUNTU_VERSION}
+ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
 
 FROM ${BASE_MUSA_DEV_CONTAINER} AS build
 
@@ -21,14 +21,21 @@ 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 -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
+    cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_BUILD_TESTS=OFF -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \

.editorconfig

@@ -21,15 +21,15 @@ indent_style = tab
 [prompts/*.txt]
 insert_final_newline = unset
 
-[tools/server/public/*]
+[examples/server/public/*]
 indent_size = 2
 
-[tools/server/public/deps_*]
+[examples/server/public/deps_*]
 trim_trailing_whitespace = unset
 indent_style = unset
 indent_size = unset
 
-[tools/server/deps_*]
+[examples/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
 
-[tools/cvector-generator/*.txt]
+[examples/cvector-generator/*.txt]
 trim_trailing_whitespace = unset
 insert_final_newline = unset
 
@@ -48,7 +48,3 @@ end_of_line = unset
 charset = unset
 trim_trailing_whitespace = unset
 insert_final_newline = unset
-
-[vendor/miniaudio/miniaudio.h]
-trim_trailing_whitespace = unset
-insert_final_newline = unset

.flake8

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

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

@@ -1,22 +0,0 @@
-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

@@ -1,67 +0,0 @@
-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

@@ -5,10 +5,6 @@ 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"
@@ -22,9 +18,8 @@ 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-${env:CURL_VERSION}-${env:ARCHITECTURE}-mingw.zip"
+        curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-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

@@ -45,9 +45,7 @@ build:
             - CMakePresets.json
 examples:
     - changed-files:
-        - any-glob-to-any-file:
-            - examples/**
-            - tools/**
+        - any-glob-to-any-file: examples/**
 devops:
     - changed-files:
         - any-glob-to-any-file:
@@ -72,7 +70,7 @@ android:
 server:
     - changed-files:
         - any-glob-to-any-file:
-            - tools/server/**
+            - examples/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', 'tools/server/*.h*', 'tools/server/*.cpp']
+    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
   pull_request_target:
     types: [opened, synchronize, reopened]
-    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'tools/server/*.h*', 'tools/server/*.cpp']
+    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.cpp', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
   schedule:
     -  cron: '04 2 * * *'
 
@@ -69,7 +69,7 @@ jobs:
       - name: Install python env
         id: pipenv
         run: |
-          cd tools/server/bench
+          cd examples/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=tools/server/bench/prometheus.yml &
+          ./prometheus --config.file=examples/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 tools/server/bench
+          cd examples/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 tools/server/bench
+          cd examples/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 tools/server/bench
+          cd examples/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: |
-            tools/server/bench/*.jpg
-            tools/server/bench/*.json
-            tools/server/bench/*.log
+            examples/server/bench/*.jpg
+            examples/server/bench/*.json
+            examples/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: |
-            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
+            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
 
       - name: Extract mermaid
         id: set_mermaid
         run: |
           set -eux
 
-          cd tools/server/bench
+          cd examples/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

@@ -4,37 +4,29 @@ on:
   workflow_call:
 
 jobs:
-  ubuntu-24-riscv64-cpu-cross:
-    runs-on: ubuntu-24.04
+  ubuntu-latest-riscv64-cpu-cross:
+    runs-on: ubuntu-latest
 
     steps:
       - uses: actions/checkout@v4
       - name: Setup Riscv
         run: |
           sudo dpkg --add-architecture riscv64
-
-          # 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 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
           sudo apt-get install -y --no-install-recommends \
                   build-essential \
                   gcc-14-riscv64-linux-gnu \
-                  g++-14-riscv64-linux-gnu
+                  g++-14-riscv64-linux-gnu \
+                  libcurl4-openssl-dev:riscv64
 
       - name: Build
         run: |
-          cmake -B build -DLLAMA_CURL=OFF \
-                         -DCMAKE_BUILD_TYPE=Release \
+          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 \
@@ -48,40 +40,35 @@ jobs:
 
           cmake --build build --config Release -j $(nproc)
 
-  ubuntu-24-riscv64-vulkan-cross:
-    runs-on: ubuntu-24.04
+  ubuntu-latest-riscv64-vulkan-cross:
+    runs-on: ubuntu-latest
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
       - name: Setup Riscv
         run: |
           sudo dpkg --add-architecture riscv64
-
-          # 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 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
           sudo apt-get install -y --no-install-recommends \
                   build-essential \
                   glslc \
                   gcc-14-riscv64-linux-gnu \
                   g++-14-riscv64-linux-gnu \
-                  libvulkan-dev:riscv64
+                  libvulkan-dev:riscv64 \
+                  libcurl4-openssl-dev:riscv64
 
       - name: Build
         run: |
-          cmake -B build -DLLAMA_CURL=OFF \
-                         -DCMAKE_BUILD_TYPE=Release \
+          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=riscv64 \
@@ -95,39 +82,34 @@ jobs:
 
           cmake --build build --config Release -j $(nproc)
 
-  ubuntu-24-arm64-vulkan-cross:
-    runs-on: ubuntu-24.04
+  ubuntu-latest-arm64-vulkan-cross:
+    runs-on: ubuntu-latest
 
     steps:
       - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
       - name: Setup Arm64
         run: |
           sudo dpkg --add-architecture arm64
-
-          # 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 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
           sudo apt-get install -y --no-install-recommends \
                   build-essential \
                   glslc \
                   crossbuild-essential-arm64 \
-                  libvulkan-dev:arm64
+                  libvulkan-dev:arm64 \
+                  libcurl4-openssl-dev:arm64
 
       - name: Build
         run: |
-          cmake -B build -DLLAMA_CURL=OFF \
-                         -DCMAKE_BUILD_TYPE=Release \
+          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=aarch64 \
@@ -140,94 +122,3 @@ 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
-
-      - name: Build
-        run: |
-          cmake -B build -DLLAMA_CURL=OFF \
-                         -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
-
-      - name: Build
-        run: |
-          cmake -B build -DLLAMA_CURL=OFF \
-                         -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

@@ -36,13 +36,10 @@ jobs:
       matrix:
         config:
           # Multi-stage build
-          # 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: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", 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: true }
+          - { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false }
           - { 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

@@ -1,749 +0,0 @@
-name: 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
-          # GGML_BACKEND_DL and GGML_CPU_ALL_VARIANTS are not currently supported on arm
-          # - 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 \
-            -DGGML_BACKEND_DL=ON \
-            -DGGML_NATIVE=OFF \
-            -DGGML_CPU_ALL_VARIANTS=ON \
-            -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_BACKEND_DL=ON \
-            -DGGML_NATIVE=OFF \
-            -DGGML_CPU_ALL_VARIANTS=ON \
-            -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-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip
-          name: llama-bin-ubuntu-vulkan-x64.zip
-
-  windows-cpu:
-    runs-on: windows-latest
-
-    strategy:
-      matrix:
-        include:
-          - arch: 'x64'
-          - arch: 'arm64'
-
-    steps:
-      - name: Clone
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
-        with:
-          key: windows-latest-cmake-cpu-${{ matrix.arch }}
-          variant: ccache
-          evict-old-files: 1d
-
-      - name: Install Ninja
-        run: |
-          choco install ninja
-
-      - name: libCURL
-        id: get_libcurl
-        uses: ./.github/actions/windows-setup-curl
-        with:
-          architecture: ${{ matrix.arch == 'x64' && 'win64' || 'win64a' }}
-
-      - name: Build
-        shell: cmd
-        env:
-          CURL_PATH: ${{ steps.get_libcurl.outputs.curl_path }}
-        run: |
-          call "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" ${{ matrix.arch }}
-          cmake -S . -B build -G "Ninja Multi-Config" ^
-            -D CMAKE_TOOLCHAIN_FILE=cmake/${{ matrix.arch }}-windows-llvm.cmake ^
-            -DGGML_NATIVE=OFF ^
-            -DGGML_BACKEND_DL=ON ^
-            -DGGML_CPU_ALL_VARIANTS=${{ matrix.arch == 'x64' && 'ON' || 'OFF' }} ^
-            -DGGML_OPENMP=ON ^
-            -DCURL_LIBRARY="%CURL_PATH%/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="%CURL_PATH%/include" ^
-            ${{ env.CMAKE_ARGS }}
-          cmake --build build --config Release
-
-      - 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\
-          Copy-Item "C:\Program Files\Microsoft Visual Studio\2022\Enterprise\VC\Redist\MSVC\14.42.34433\debug_nonredist\${{ matrix.arch }}\Microsoft.VC143.OpenMP.LLVM\libomp140.${{ matrix.arch == 'x64' && 'x86_64' || 'aarch64' }}.dll" .\build\bin\Release\
-          7z a llama-bin-win-cpu-${{ matrix.arch }}.zip .\build\bin\Release\*
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          path: llama-bin-win-cpu-${{ matrix.arch }}.zip
-          name: llama-bin-win-cpu-${{ matrix.arch }}.zip
-
-  windows:
-    runs-on: windows-latest
-
-    env:
-      OPENBLAS_VERSION: 0.3.23
-      VULKAN_VERSION: 1.4.309.0
-
-    strategy:
-      matrix:
-        include:
-          - backend: 'vulkan'
-            arch: 'x64'
-            defines: '-DGGML_VULKAN=ON'
-            target: 'ggml-vulkan'
-          - backend: 'opencl-adreno'
-            arch: 'arm64'
-            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON'
-            target: 'ggml-opencl'
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-
-      - name: ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
-        with:
-          key: windows-latest-cmake-${{ matrix.backend }}-${{ matrix.arch }}
-          variant: ccache
-          evict-old-files: 1d
-
-      - name: Install Vulkan SDK
-        id: get_vulkan
-        if: ${{ matrix.backend == 'vulkan' }}
-        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: ${{ matrix.backend == 'opencl-adreno' && matrix.arch == 'arm64' }}
-        run: |
-          git clone https://github.com/KhronosGroup/OpenCL-Headers
-          cd OpenCL-Headers
-          cmake -B build `
-            -DBUILD_TESTING=OFF `
-            -DOPENCL_HEADERS_BUILD_TESTING=OFF `
-            -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF `
-            -DCMAKE_INSTALL_PREFIX="$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: Build
-        id: cmake_build
-        run: |
-          cmake -S . -B build ${{ matrix.defines }} -DGGML_NATIVE=OFF -DGGML_CPU=OFF -DGGML_BACKEND_DL=ON -DLLAMA_CURL=OFF
-          cmake --build build --config Release --target ${{ matrix.target }}
-
-      - name: Pack artifacts
-        id: pack_artifacts
-        run: |
-          7z a llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip .\build\bin\Release\${{ matrix.target }}.dll
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          path: llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip
-          name: llama-bin-win-${{ matrix.backend }}-${{ matrix.arch }}.zip
-
-  windows-cuda:
-    runs-on: windows-2019
-
-    strategy:
-      matrix:
-        cuda: ['12.4', '11.7']
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-
-      - name: Install ccache
-        uses: hendrikmuhs/ccache-action@v1.2.16
-        with:
-          key: windows-cuda-${{ matrix.cuda }}
-          variant: ccache
-          evict-old-files: 1d
-
-      - name: Install Cuda Toolkit
-        uses: ./.github/actions/windows-setup-cuda
-        with:
-          cuda_version: ${{ matrix.cuda }}
-
-      - name: Install Ninja
-        id: install_ninja
-        run: |
-          choco install ninja
-
-      - name: Build
-        id: cmake_build
-        shell: cmd
-        run: |
-          call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\VC\Auxiliary\Build\vcvars64.bat"
-          cmake -S . -B build -G "Ninja Multi-Config" ^
-            -DGGML_BACKEND_DL=ON ^
-            -DGGML_NATIVE=OFF ^
-            -DGGML_CPU=OFF ^
-            -DGGML_CUDA=ON ^
-            -DLLAMA_CURL=OFF
-          set /A NINJA_JOBS=%NUMBER_OF_PROCESSORS%-1
-          cmake --build build --config Release -j %NINJA_JOBS% --target ggml-cuda
-
-      - name: Pack artifacts
-        id: pack_artifacts
-        run: |
-          7z a llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip .\build\bin\Release\ggml-cuda.dll
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          path: llama-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
-
-      - 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
-
-      - name: Build
-        id: cmake_build
-        shell: cmd
-        run: |
-          call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
-          cmake -G "Ninja" -B build ^
-            -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx ^
-            -DCMAKE_BUILD_TYPE=Release ^
-            -DGGML_BACKEND_DL=ON -DBUILD_SHARED_LIBS=ON ^
-            -DGGML_CPU=OFF -DGGML_SYCL=ON ^
-            -DLLAMA_CURL=OFF
-          cmake --build build --target ggml-sycl -j
-
-      - 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-bin-win-sycl-x64.zip ./build/bin/*
-
-      - name: Upload the release package
-        uses: actions/upload-artifact@v4
-        with:
-          path: llama-bin-win-sycl-x64.zip
-          name: llama-bin-win-sycl-x64.zip
-
-  windows-hip:
-    runs-on: windows-latest
-
-    strategy:
-      matrix:
-        include:
-          - name: "radeon"
-            gpu_targets: "gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032"
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-
-      - 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-${{ matrix.name }}-x64
-          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: Build
-        id: cmake_build
-        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/ -Wno-ignored-attributes -Wno-nested-anon-types" `
-            -DCMAKE_BUILD_TYPE=Release `
-            -DGGML_BACKEND_DL=ON `
-            -DGGML_NATIVE=OFF `
-            -DGGML_CPU=OFF `
-            -DAMDGPU_TARGETS="${{ matrix.gpu_targets }}" `
-            -DGGML_HIP_ROCWMMA_FATTN=ON `
-            -DGGML_HIP=ON `
-            -DLLAMA_CURL=OFF
-          cmake --build build --target ggml-hip -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: Pack artifacts
-        id: pack_artifacts
-        run: |
-          7z a llama-bin-win-hip-${{ matrix.name }}-x64.zip .\build\bin\*
-
-      - name: Upload artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          path: llama-bin-win-hip-${{ matrix.name }}-x64.zip
-          name: llama-bin-win-hip-${{ matrix.name }}-x64.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: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-
-    # Fine-grant permission
-    # https://docs.github.com/en/actions/security-for-github-actions/security-guides/automatic-token-authentication#modifying-the-permissions-for-the-github_token
-    permissions:
-        contents: write # for creating release
-
-    runs-on: ubuntu-latest
-
-    needs:
-      - windows
-      - windows-cpu
-      - windows-cuda
-      - windows-sycl
-      - windows-hip
-      - ubuntu-22-cpu
-      - ubuntu-22-vulkan
-      - macOS-arm64
-      - macOS-x64
-      - ios-xcode-build
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Determine tag name
-        id: tag
-        uses: ./.github/actions/get-tag-name
-
-      - name: Download artifacts
-        id: download-artifact
-        uses: actions/download-artifact@v4
-        with:
-          path: ./artifact
-          merge-multiple: true
-
-      - name: Move artifacts
-        id: move_artifacts
-        run: |
-          mkdir -p release
-
-          echo "Adding CPU backend files to existing zips..."
-          for arch in x64 arm64; do
-            cpu_zip="artifact/llama-bin-win-cpu-${arch}.zip"
-            temp_dir=$(mktemp -d)
-            echo "Extracting CPU backend for $arch..."
-            unzip "$cpu_zip" -d "$temp_dir"
-
-            echo "Adding CPU files to $arch zips..."
-            for target_zip in artifact/llama-bin-win-*-${arch}.zip; do
-              if [[ "$target_zip" == "$cpu_zip" ]]; then
-                continue
-              fi
-              echo "Adding CPU backend to $(basename "$target_zip")"
-              realpath_target_zip=$(realpath "$target_zip")
-              (cd "$temp_dir" && zip -r "$realpath_target_zip" .)
-            done
-
-            rm -rf "$temp_dir"
-          done
-
-          echo "Renaming and moving zips to release..."
-          for zip_file in artifact/llama-bin-win-*.zip; do
-            base_name=$(basename "$zip_file" .zip)
-            zip_name="llama-${{ steps.tag.outputs.name }}-${base_name#llama-}.zip"
-            echo "Moving $zip_file to release/$zip_name"
-            mv "$zip_file" "release/$zip_name"
-          done
-
-          echo "Moving other artifacts..."
-          mv -v artifact/*.zip 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('./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(`./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', 'tools/server/**.*']
+    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'tools/server/**.*']
+    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
 
 env:
   LLAMA_LOG_COLORS: 1
@@ -74,7 +74,7 @@ jobs:
       - name: Tests dependencies
         id: test_dependencies
         run: |
-          pip install -r tools/server/tests/requirements.txt
+          pip install -r examples/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 tools/server/webui
+          cd examples/server/webui
           npm ci
 
       - name: WebUI - Check code format
         id: webui_format
         run: |
           git config --global --add safe.directory $(realpath .)
-          cd tools/server/webui
+          cd examples/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 tools/server/webui
+          cd examples/server/webui
           git status
 
           npm run build
@@ -161,21 +161,21 @@ jobs:
         env:
           GITHUB_ACTIONS: "true"
         run: |
-          cd tools/server/tests
+          cd examples/server/tests
           ./tests.sh
 
       - name: Tests (sanitizers)
         id: server_integration_tests_sanitizers
         if: ${{ matrix.sanitizer != '' }}
         run: |
-          cd tools/server/tests
+          cd examples/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 tools/server/tests
+          cd examples/server/tests
           SLOW_TESTS=1 ./tests.sh
 
 
@@ -211,7 +211,7 @@ jobs:
       - name: Tests dependencies
         id: test_dependencies
         run: |
-          pip install -r tools/server/tests/requirements.txt
+          pip install -r examples/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 tools/server/tests
+          cd examples/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 tools/server/tests
+          cd examples/server/tests
           $env:SLOW_TESTS = "1"
           pytest -v -x

.github/workflows/winget.yml

@@ -1,42 +0,0 @@
-name: Update Winget Package
-
-on:
-  workflow_dispatch: # allows manual triggering
-  schedule:
-    - cron: '28 5 * * *' # Update every day at 5:28 UTC
-
-jobs:
-  update:
-    name: Update Winget Package
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Install cargo binstall
-        uses: cargo-bins/cargo-binstall@268643a6b5ea099f5718ee5cd3ff7dc89a5eb49b
-
-      - name: Install komac
-        run: |
-          cargo binstall komac@2.11.2 -y
-
-      - name: Find latest release
-        id: find_latest_release
-        uses: actions/github-script@v6
-        with:
-          script: |
-            const { data: releases } = await github.rest.repos.listReleases({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-            });
-            console.log("Latest release:", releases[0].tag_name);
-            return releases[0].tag_name;
-
-      - name: Update manifest
-        env:
-          VERSION: ${{ steps.find_latest_release.outputs.result }}
-        run: |
-          echo "Updating manifest..."
-          komac update --version ${{ env.VERSION }} \
-            --urls "https://github.com/ggml-org/llama.cpp/releases/download/${{ env.VERSION }}/llama-${{ env.VERSION }}-bin-win-vulkan-x64.zip" \
-            --token ${{ secrets.WINGET_GITHUB_TOKEN }} \
-            --submit \
-            ggml.llamacpp

.gitignore

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

CMakeLists.txt

@@ -77,7 +77,6 @@ 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})
 
@@ -188,10 +187,6 @@ 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
 #
@@ -252,3 +247,20 @@ 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

@@ -38,6 +38,15 @@
         }
     },
 
+    {
+        "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" },
@@ -64,6 +73,10 @@
     { "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
-/tools/server/ @ngxson
+/examples/server/ @ngxson
 /ggml/src/ggml-cuda/fattn* @JohannesGaessler
 /ggml/src/ggml-cuda/mmq.* @JohannesGaessler
 /ggml/src/ggml-cuda/mmv.* @JohannesGaessler

Makefile

@@ -1156,10 +1156,10 @@ $(LIB_COMMON_S): $(OBJ_COMMON)
 
 # Clean generated server assets
 clean-server-assets:
-	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
+	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
 
 # Clean rule
 clean: clean-server-assets
@@ -1179,7 +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: tools/main/main.cpp \
+llama-cli: examples/main/main.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -1187,7 +1187,12 @@ llama-cli: tools/main/main.cpp \
 	@echo '====  Run ./llama-cli -h for help.  ===='
 	@echo
 
-llama-run: tools/run/run.cpp \
+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 \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -1202,7 +1207,7 @@ 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: tools/tokenize/tokenize.cpp \
+llama-tokenize: examples/tokenize/tokenize.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -1212,27 +1217,27 @@ 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: tools/batched-bench/batched-bench.cpp \
+llama-batched-bench: examples/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: tools/quantize/quantize.cpp \
+llama-quantize: examples/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: tools/quantize-stats/quantize-stats.cpp \
+llama-quantize-stats: examples/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: tools/perplexity/perplexity.cpp \
+llama-perplexity: examples/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: tools/imatrix/imatrix.cpp \
+llama-imatrix: examples/imatrix/imatrix.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -1274,7 +1279,7 @@ 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: tools/gguf-split/gguf-split.cpp \
+llama-gguf-split: examples/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)
@@ -1284,7 +1289,7 @@ 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: tools/cvector-generator/cvector-generator.cpp \
+llama-cvector-generator: examples/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)
@@ -1294,12 +1299,12 @@ 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: tools/llama-bench/llama-bench.cpp \
+llama-bench: examples/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: tools/export-lora/export-lora.cpp \
+llama-export-lora: examples/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)
@@ -1355,17 +1360,17 @@ 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: tools/rpc/rpc-server.cpp \
+rpc-server: examples/rpc/rpc-server.cpp \
 	$(OBJ_GGML)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
 endif # GGML_RPC
 
 llama-server: \
-	tools/server/server.cpp \
-	tools/server/utils.hpp \
-	tools/server/httplib.h \
-	tools/server/index.html.hpp \
-	tools/server/loading.html.hpp \
+	examples/server/server.cpp \
+	examples/server/utils.hpp \
+	examples/server/httplib.h \
+	examples/server/index.html.hpp \
+	examples/server/loading.html.hpp \
 	common/chat.cpp \
 	common/chat.h \
 	common/chat-template.hpp \
@@ -1373,10 +1378,10 @@ llama-server: \
 	common/minja.hpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Itools/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
 
-# Portable equivalent of `cd tools/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`:
-tools/server/%.hpp: tools/server/public/% FORCE Makefile
+# Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`:
+examples/server/%.hpp: examples/server/public/% FORCE Makefile
 	@( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \
 		echo "unsigned char $${NAME}[] = {" && \
 		cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \
@@ -1389,36 +1394,36 @@ 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: tools/mtmd/llava.cpp \
-	tools/mtmd/llava.h \
-	tools/mtmd/clip.cpp \
-	tools/mtmd/clip.h \
+libllava.a: examples/llava/llava.cpp \
+	examples/llava/llava.h \
+	examples/llava/clip.cpp \
+	examples/llava/clip.h \
 	common/stb_image.h \
 	common/base64.hpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual
 
-llama-llava-cli: tools/mtmd/llava-cli.cpp \
-	tools/mtmd/llava.cpp \
-	tools/mtmd/llava.h \
-	tools/mtmd/clip.cpp \
-	tools/mtmd/clip.h \
+llama-llava-cli: examples/llava/llava-cli.cpp \
+	examples/llava/llava.cpp \
+	examples/llava/llava.h \
+	examples/llava/clip.cpp \
+	examples/llava/clip.h \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
 
-llama-minicpmv-cli: tools/mtmd/minicpmv-cli.cpp \
-	tools/mtmd/llava.cpp \
-	tools/mtmd/llava.h \
-	tools/mtmd/clip.cpp \
-	tools/mtmd/clip.h \
+llama-minicpmv-cli: examples/llava/minicpmv-cli.cpp \
+	examples/llava/llava.cpp \
+	examples/llava/llava.h \
+	examples/llava/clip.cpp \
+	examples/llava/clip.h \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
 
-llama-qwen2vl-cli: tools/mtmd/qwen2vl-cli.cpp \
-	tools/mtmd/llava.cpp \
-	tools/mtmd/llava.h \
-	tools/mtmd/clip.cpp \
-	tools/mtmd/clip.h \
+llama-qwen2vl-cli: examples/llava/qwen2vl-cli.cpp \
+	examples/llava/llava.cpp \
+	examples/llava/llava.h \
+	examples/llava/clip.cpp \
+	examples/llava/clip.h \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
 
@@ -1475,12 +1480,12 @@ 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) -Itools/server -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) -Iexamples/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) -Itools/server -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) -Iexamples/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

@@ -16,9 +16,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 
 ## Hot topics
 
-- 🔥 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
+- 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
@@ -28,30 +27,6 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 
 ----
 
-## Quick start
-
-Getting started with llama.cpp is straightforward. Here are several ways to install it on your machine:
-
-- Install `llama.cpp` using [brew, nix or winget](docs/install.md)
-- Run with Docker - see our [Docker documentation](docs/docker.md)
-- Download pre-built binaries from the [releases page](https://github.com/ggml-org/llama.cpp/releases)
-- Build from source by cloning this repository - check out [our build guide](docs/build.md)
-
-Once installed, you'll need a model to work with. Head to the [Obtaining and quantizing models](#obtaining-and-quantizing-models) section to learn more.
-
-Example command:
-
-```sh
-# Use a local model file
-llama-cli -m my_model.gguf
-
-# Or download and run a model directly from Hugging Face
-llama-cli -hf ggml-org/gemma-3-1b-it-GGUF
-
-# Launch OpenAI-compatible API server
-llama-server -hf ggml-org/gemma-3-1b-it-GGUF
-```
-
 ## Description
 
 The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide
@@ -61,7 +36,7 @@ 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 GPUs via MUSA)
+- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads MTT GPUs via MUSA)
 - Vulkan and SYCL backend support
 - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
 
@@ -154,7 +129,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 <details>
 <summary>Bindings</summary>
 
-- Python: [ddh0/easy-llama](https://github.com/ddh0/easy-llama)
 - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
 - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
 - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
@@ -254,7 +228,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 
 </details>
 
-
 ## Supported backends
 
 | Backend | Target devices |
@@ -263,13 +236,23 @@ 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 GPU |
+| [MUSA](docs/build.md#musa) | Moore Threads MTT 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/tools/rpc) | All |
+| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/examples/rpc) | All |
+
+## Building the project
+
+The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](include/llama.h).
+The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server. Possible methods for obtaining the binaries:
+
+- Clone this repository and build locally, see [how to build](docs/build.md)
+- On MacOS or Linux, install `llama.cpp` via [brew, flox or nix](docs/install.md)
+- Use a Docker image, see [documentation for Docker](docs/docker.md)
+- Download pre-built binaries from [releases](https://github.com/ggml-org/llama.cpp/releases)
 
 ## Obtaining and quantizing models
 
@@ -278,11 +261,7 @@ The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](htt
 - [Trending](https://huggingface.co/models?library=gguf&sort=trending)
 - [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf)
 
-You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from [Hugging Face](https://huggingface.co/) or other model hosting sites, such as [ModelScope](https://modelscope.cn/), by using this CLI argument: `-hf <user>/<model>[:quant]`. For example:
-
-```sh
-llama-cli -hf ggml-org/gemma-3-1b-it-GGUF
-```
+You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from [Hugging Face](https://huggingface.co/) or other model hosting sites, such as [ModelScope](https://modelscope.cn/), by using this CLI argument: `-hf <user>/<model>[:quant]`.
 
 By default, the CLI would download from Hugging Face, you can switch to other options with the environment variable `MODEL_ENDPOINT`. For example, you may opt to downloading model checkpoints from ModelScope or other model sharing communities by setting the environment variable, e.g. `MODEL_ENDPOINT=https://www.modelscope.cn/`.
 
@@ -297,9 +276,9 @@ 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](tools/quantize/README.md)
+To learn more about model quantization, [read this documentation](examples/quantize/README.md)
 
-## [`llama-cli`](tools/main)
+## [`llama-cli`](examples/main)
 
 #### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality.
 
@@ -362,7 +341,7 @@ To learn more about model quantization, [read this documentation](tools/quantize
     </details>
 
 
-## [`llama-server`](tools/server)
+## [`llama-server`](examples/server)
 
 #### A lightweight, [OpenAI API](https://github.com/openai/openai-openapi) compatible, HTTP server for serving LLMs.
 
@@ -432,7 +411,7 @@ To learn more about model quantization, [read this documentation](tools/quantize
     </details>
 
 
-## [`llama-perplexity`](tools/perplexity)
+## [`llama-perplexity`](examples/perplexity)
 
 #### A tool for measuring the perplexity [^1][^2] (and other quality metrics) of a model over a given text.
 
@@ -457,10 +436,10 @@ To learn more about model quantization, [read this documentation](tools/quantize
 
     </details>
 
-[^1]: [tools/perplexity/README.md](./tools/perplexity/README.md)
+[^1]: [examples/perplexity/README.md](./examples/perplexity/README.md)
 [^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
 
-## [`llama-bench`](tools/llama-bench)
+## [`llama-bench`](examples/llama-bench)
 
 #### Benchmark the performance of the inference for various parameters.
 
@@ -481,7 +460,7 @@ To learn more about model quantization, [read this documentation](tools/quantize
 
     </details>
 
-## [`llama-run`](tools/run)
+## [`llama-run`](examples/run)
 
 #### A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama [^3].
 
@@ -525,8 +504,8 @@ To learn more about model quantization, [read this documentation](tools/quantize
 
 ## Other documentation
 
-- [main (cli)](tools/main/README.md)
-- [server](tools/server/README.md)
+- [main (cli)](examples/main/README.md)
+- [server](examples/server/README.md)
 - [GBNF grammars](grammars/README.md)
 
 #### Development documentation
@@ -592,12 +571,4 @@ automatically. For example:
 $ echo "source ~/.llama-completion.bash" >> ~/.bashrc
 ```
 
-## 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)
-- [miniaudio.h](https://github.com/mackron/miniaudio) - Single-header audio format decoder, used by multimodal subsystem - Public domain
+## References

SECURITY.md

@@ -40,7 +40,7 @@ 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:
-* 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).
+* Do not use the RPC backend, [rpc-server](https://github.com/ggml-org/llama.cpp/tree/master/examples/rpc) and [llama-server](https://github.com/ggml-org/llama.cpp/tree/master/examples/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.
 

build-xcframework.sh

@@ -8,7 +8,6 @@ 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
@@ -32,7 +31,6 @@ 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}
@@ -117,7 +115,6 @@ 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:rc4.0.1-mudnn-devel-ubuntu22.04
+    mthreads/musa:rc3.1.1-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 ./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
+    (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
 
     set +e
 }
@@ -211,8 +211,8 @@ function gg_run_test_scripts_release {
 
     set -e
 
-    (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
+    (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
 
     set +e
 }

cmake/arm64-windows-msvc.cmake

@@ -0,0 +1,6 @@
+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

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

cmake/x64-windows-llvm.cmake

@@ -3,3 +3,9 @@ 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

@@ -39,9 +39,7 @@ 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}
-            -DCMAKE_SYSTEM_NAME=${CMAKE_SYSTEM_NAME} -DCMAKE_SYSTEM_PROCESSOR=${CMAKE_SYSTEM_PROCESSOR}
-            -P "${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info-gen-cpp.cmake"
+            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -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" ${GIT_INDEX}
     VERBATIM
@@ -58,24 +56,21 @@ add_library(${TARGET} STATIC
     arg.cpp
     arg.h
     base64.hpp
-    chat-parser.cpp
-    chat-parser.h
     chat.cpp
     chat.h
     common.cpp
     common.h
     console.cpp
     console.h
-    json-partial.cpp
-    json-partial.h
     json-schema-to-grammar.cpp
+    json.hpp
     llguidance.cpp
     log.cpp
     log.h
+    minja/chat-template.hpp
+    minja/minja.hpp
     ngram-cache.cpp
     ngram-cache.h
-    regex-partial.cpp
-    regex-partial.h
     sampling.cpp
     sampling.h
     speculative.cpp
@@ -122,8 +117,8 @@ if (LLAMA_LLGUIDANCE)
 
     ExternalProject_Add(llguidance_ext
         GIT_REPOSITORY https://github.com/guidance-ai/llguidance
-        # v0.7.20 (+ fix to build on GCC 15):
-        GIT_TAG b5b8b64dba11c4e4ee6b1d1450d3a3ae279891e8
+        # v0.7.10:
+        GIT_TAG 0309d2a6bf40abda35344a362edc71e06d5009f8
         PREFIX ${CMAKE_BINARY_DIR}/llguidance
         SOURCE_DIR ${LLGUIDANCE_SRC}
         BUILD_IN_SOURCE TRUE
@@ -144,30 +139,6 @@ if (LLAMA_LLGUIDANCE)
     set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} llguidance ${LLGUIDANCE_PLATFORM_LIBS})
 endif ()
 
-target_include_directories(${TARGET} PUBLIC . ../vendor)
+target_include_directories(${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

@@ -1,11 +1,10 @@
+#include "gguf.h" // for reading GGUF splits
 #include "arg.h"
 
-#include "chat.h"
 #include "common.h"
-#include "gguf.h" // for reading GGUF splits
-#include "json-schema-to-grammar.h"
 #include "log.h"
 #include "sampling.h"
+#include "chat.h"
 
 // fix problem with std::min and std::max
 #if defined(_WIN32)
@@ -16,9 +15,6 @@
 #include <windows.h>
 #endif
 
-#define JSON_ASSERT GGML_ASSERT
-#include <nlohmann/json.hpp>
-
 #include <algorithm>
 #include <climits>
 #include <cstdarg>
@@ -38,11 +34,13 @@
 #include <future>
 #endif
 
+#include "json-schema-to-grammar.h"
+
 using json = nlohmann::ordered_json;
 
 std::initializer_list<enum llama_example> mmproj_examples = {
-    LLAMA_EXAMPLE_MTMD,
-    LLAMA_EXAMPLE_SERVER,
+    LLAMA_EXAMPLE_LLAVA,
+    // TODO: add LLAMA_EXAMPLE_SERVER when it's ready
 };
 
 static std::string read_file(const std::string & fname) {
@@ -219,11 +217,13 @@ 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, const char * method_name) {
+static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds) {
     int remaining_attempts = max_attempts;
+    char * method = nullptr;
+    curl_easy_getinfo(curl, CURLINFO_EFFECTIVE_METHOD, &method);
 
     while (remaining_attempts > 0) {
-        LOG_INF("%s: %s %s (attempt %d of %d)...\n", __func__ , method_name, url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
+        LOG_INF("%s: %s %s (attempt %d of %d)...\n", __func__ , method, url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
 
         CURLcode res = curl_easy_perform(curl);
         if (res == CURLE_OK) {
@@ -244,56 +244,7 @@ static bool curl_perform_with_retry(const std::string & url, CURL * curl, int ma
 }
 
 // download one single file from remote URL to local path
-static bool common_download_file_single(const std::string & url, const std::string & path, const std::string & bearer_token, bool offline) {
-    // Check if the file already exists locally
-    auto file_exists = std::filesystem::exists(path);
-
-    // If the file exists, check its JSON metadata companion file.
-    std::string metadata_path = path + ".json";
-    nlohmann::json metadata; // TODO @ngxson : get rid of this json, use regex instead
-    std::string etag;
-    std::string last_modified;
-
-    if (file_exists) {
-        if (offline) {
-            LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
-            return true; // skip verification/downloading
-        }
-        // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
-        std::ifstream metadata_in(metadata_path);
-        if (metadata_in.good()) {
-            try {
-                metadata_in >> metadata;
-                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");
-                }
-                if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
-                    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());
-            }
-        }
-        // 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 {
-        if (offline) {
-            LOG_ERR("%s: required file is not available in cache (offline mode): %s\n", __func__, path.c_str());
-            return false;
-        }
-        LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
-    }
-
-    // Send a HEAD request to retrieve the etag and last-modified headers
-    struct common_load_model_from_url_headers {
-        std::string etag;
-        std::string last_modified;
-    };
-
-    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
-
+static bool common_download_file_single(const std::string & url, const std::string & path, const std::string & bearer_token) {
     // Initialize libcurl
     curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
     curl_slist_ptr http_headers;
@@ -320,47 +271,91 @@ static bool common_download_file_single(const std::string & url, const std::stri
     curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
 #endif
 
-    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 {
-        common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata;
+    // Check if the file already exists locally
+    auto file_exists = std::filesystem::exists(path);
 
-        static std::regex header_regex("([^:]+): (.*)\r\n");
-        static std::regex etag_regex("ETag", std::regex_constants::icase);
-        static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);
+    // If the file exists, check its JSON metadata companion file.
+    std::string metadata_path = path + ".json";
+    nlohmann::json metadata; // TODO @ngxson : get rid of this json, use regex instead
+    std::string etag;
+    std::string last_modified;
 
-        std::string header(buffer, n_items);
-        std::smatch match;
-        if (std::regex_match(header, match, header_regex)) {
-            const std::string & key = match[1];
-            const std::string & value = match[2];
-            if (std::regex_match(key, match, etag_regex)) {
-                headers->etag = value;
-            } else if (std::regex_match(key, match, last_modified_regex)) {
-                headers->last_modified = value;
+    if (file_exists) {
+        // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
+        std::ifstream metadata_in(metadata_path);
+        if (metadata_in.good()) {
+            try {
+                metadata_in >> metadata;
+                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");
+                }
+                if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
+                    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 n_items;
-    };
-
-    curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
-    curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
-    curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
-    curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
-
-    // 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) {
-        head_request_ok = false;
+        // 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());
     }
 
-    long http_code = 0;
-    curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &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;
+    // Send a HEAD request to retrieve the etag and last-modified headers
+    struct common_load_model_from_url_headers {
+        std::string etag;
+        std::string last_modified;
+    };
+
+    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 {
+            common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata;
+
+            static std::regex header_regex("([^:]+): (.*)\r\n");
+            static std::regex etag_regex("ETag", std::regex_constants::icase);
+            static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);
+
+            std::string header(buffer, n_items);
+            std::smatch match;
+            if (std::regex_match(header, match, header_regex)) {
+                const std::string & key = match[1];
+                const std::string & value = match[2];
+                if (std::regex_match(key, match, etag_regex)) {
+                    headers->etag = value;
+                } else if (std::regex_match(key, match, last_modified_regex)) {
+                    headers->last_modified = value;
+                }
+            }
+            return n_items;
+        };
+
+        curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
+        curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
+        curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
+        curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
+
+        // 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);
+        if (!was_perform_successful) {
+            head_request_ok = false;
+        }
+
+        long http_code = 0;
+        curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &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;
+        }
     }
 
     // if head_request_ok is false, we don't have the etag or last-modified headers
@@ -430,7 +425,7 @@ 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, "GET");
+        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
         if (!was_perform_successful) {
             return false;
         }
@@ -467,12 +462,12 @@ static bool common_download_file_single(const std::string & url, const std::stri
 
 // download multiple files from remote URLs to local paths
 // the input is a vector of pairs <url, path>
-static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> & urls, const std::string & bearer_token, bool offline) {
+static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> & urls, const std::string & bearer_token) {
     // Prepare download in parallel
     std::vector<std::future<bool>> futures_download;
     for (auto const & item : urls) {
-        futures_download.push_back(std::async(std::launch::async, [bearer_token, offline](const std::pair<std::string, std::string> & it) -> bool {
-            return common_download_file_single(it.first, it.second, bearer_token, offline);
+        futures_download.push_back(std::async(std::launch::async, [bearer_token](const std::pair<std::string, std::string> & it) -> bool {
+            return common_download_file_single(it.first, it.second, bearer_token);
         }, item));
     }
 
@@ -488,15 +483,14 @@ static bool common_download_file_multiple(const std::vector<std::pair<std::strin
 
 static bool common_download_model(
         const common_params_model & model,
-        const std::string & bearer_token,
-        bool offline) {
+        const std::string & bearer_token) {
     // Basic validation of the model.url
     if (model.url.empty()) {
         LOG_ERR("%s: invalid model url\n", __func__);
         return false;
     }
 
-    if (!common_download_file_single(model.url, model.path, bearer_token, offline)) {
+    if (!common_download_file_single(model.url, model.path, bearer_token)) {
         return false;
     }
 
@@ -555,7 +549,7 @@ static bool common_download_model(
         }
 
         // Download in parallel
-        common_download_file_multiple(urls, bearer_token, offline);
+        common_download_file_multiple(urls, bearer_token);
     }
 
     return true;
@@ -616,7 +610,7 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string &
  *
  * Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files.
  */
-static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & bearer_token, bool offline) {
+static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & bearer_token) {
     auto parts = string_split<std::string>(hf_repo_with_tag, ':');
     std::string tag = parts.size() > 1 ? parts.back() : "latest";
     std::string hf_repo = parts[0];
@@ -646,25 +640,20 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_
     long res_code = 0;
     std::string res_str;
     bool use_cache = false;
-    if (!offline) {
+    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 {
-            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 at %s: %s\n", url.c_str(), e.what());
-        }
-    }
-    if (res_code == 0) {
-        if (std::filesystem::exists(cached_response_path)) {
-            LOG_WRN("trying to read manifest from cache: %s\n", cached_response_path.c_str());
             res_str = read_file(cached_response_path);
             res_code = 200;
             use_cache = true;
-        } else {
-            throw std::runtime_error(
-                offline ? "error: failed to get manifest (offline mode)"
-                : "error: failed to get manifest (check your internet connection)");
+        } catch (const std::exception & e) {
+            throw std::runtime_error("error: failed to get manifest (check your internet connection)");
         }
     }
     std::string ggufFile;
@@ -711,25 +700,24 @@ bool common_has_curl() {
     return false;
 }
 
-static bool common_download_file_single(const std::string &, const std::string &, const std::string &, bool) {
+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;
 }
 
-static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> &, const std::string &, bool) {
+static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> &, const std::string &) {
     LOG_ERR("error: built without CURL, cannot download model from the internet\n");
     return false;
 }
 
 static bool common_download_model(
         const common_params_model &,
-        const std::string &,
-        bool) {
+        const std::string &) {
     LOG_ERR("error: built without CURL, cannot download model from the internet\n");
     return false;
 }
 
-static struct common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool) {
+static struct common_hf_file_res common_get_hf_file(const std::string &, const std::string &) {
     LOG_ERR("error: built without CURL, cannot download model from the internet\n");
     return {};
 }
@@ -756,8 +744,7 @@ struct handle_model_result {
 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 offline) {
+        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
     {
@@ -765,7 +752,7 @@ static handle_model_result common_params_handle_model(
             // short-hand to avoid specifying --hf-file -> default it to --model
             if (model.hf_file.empty()) {
                 if (model.path.empty()) {
-                    auto auto_detected = common_get_hf_file(model.hf_repo, bearer_token, offline);
+                    auto auto_detected = common_get_hf_file(model.hf_repo, bearer_token);
                     if (auto_detected.repo.empty() || auto_detected.ggufFile.empty()) {
                         exit(1); // built without CURL, error message already printed
                     }
@@ -806,7 +793,7 @@ static handle_model_result common_params_handle_model(
 
     // then, download it if needed
     if (!model.url.empty()) {
-        bool ok = common_download_model(model, bearer_token, offline);
+        bool ok = common_download_model(model, bearer_token);
         if (!ok) {
             LOG_ERR("error: failed to download model from %s\n", model.url.c_str());
             exit(1);
@@ -949,7 +936,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
 
     // handle model and download
     {
-        auto res = common_params_handle_model(params.model, params.hf_token, DEFAULT_MODEL_PATH, params.offline);
+        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()) {
@@ -959,12 +946,12 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         // 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, "", params.offline);
+                common_params_handle_model(params.mmproj,    params.hf_token, "");
                 break;
             }
         }
-        common_params_handle_model(params.speculative.model, params.hf_token, "", params.offline);
-        common_params_handle_model(params.vocoder.model,     params.hf_token, "", params.offline);
+        common_params_handle_model(params.speculative.model, params.hf_token, "");
+        common_params_handle_model(params.vocoder.model,     params.hf_token, "");
     }
 
     if (params.escape) {
@@ -1298,7 +1285,7 @@ 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_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL, 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),
@@ -1348,9 +1335,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ));
     add_opt(common_arg(
         {"--prio"}, "N",
-        string_format("set process/thread priority : low(-1), normal(0), medium(1), high(2), realtime(3) (default: %d)\n", params.cpuparams.priority),
+        string_format("set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: %d)\n", params.cpuparams.priority),
         [](common_params & params, int prio) {
-            if (prio < GGML_SCHED_PRIO_LOW || prio > GGML_SCHED_PRIO_REALTIME) {
+            if (prio < 0 || prio > 3) {
                 throw std::invalid_argument("invalid value");
             }
             params.cpuparams.priority = (enum ggml_sched_priority) prio;
@@ -1431,7 +1418,7 @@ 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_MAIN || ex == LLAMA_EXAMPLE_INFILL
                 ? "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),
@@ -1460,14 +1447,6 @@ 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"),
@@ -1678,7 +1657,7 @@ 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}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
     add_opt(common_arg(
         {"--in-suffix"}, "STRING",
         "string to suffix after user inputs with (default: empty)",
@@ -1686,14 +1665,14 @@ 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}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
     add_opt(common_arg(
         {"--no-warmup"},
         "skip warming up the model with an empty run",
         [](common_params & params) {
             params.warmup = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_EMBEDDING}));
     add_opt(common_arg(
         {"--spm-infill"},
         string_format(
@@ -1703,7 +1682,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.spm_infill = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_INFILL}));
     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()),
@@ -2080,6 +2059,13 @@ 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",
@@ -2113,6 +2099,13 @@ 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",
@@ -2220,40 +2213,39 @@ 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. see tools/mtmd/README.md\n"
-        "note: if -hf is used, this argument can be omitted",
+        "path to a multimodal projector file. see examples/llava/README.md",
         [](common_params & params, const std::string & value) {
             params.mmproj.path = value;
         }
-    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ"));
+    ).set_examples(mmproj_examples));
     add_opt(common_arg(
         {"--mmproj-url"}, "URL",
-        "URL to a multimodal projector file. see tools/mtmd/README.md",
+        "URL to a multimodal projector file. see examples/llava/README.md",
         [](common_params & params, const std::string & value) {
             params.mmproj.url = value;
         }
-    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_URL"));
+    ).set_examples(mmproj_examples));
     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"));
+    ).set_examples(mmproj_examples));
     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"));
+    ).set_examples(mmproj_examples));
     add_opt(common_arg(
-        {"--image", "--audio"}, "FILE",
-        "path to an image or audio file. use with multimodal models, can be repeated if you have multiple files\n",
+        {"--image"}, "FILE",
+        "path to an image file. use with multimodal models. Specify multiple times for batching",
         [](common_params & params, const std::string & value) {
             params.image.emplace_back(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_MTMD}));
+    ).set_examples({LLAMA_EXAMPLE_LLAVA}));
     if (llama_supports_rpc()) {
         add_opt(common_arg(
             {"--rpc"}, "SERVERS",
@@ -2453,13 +2445,6 @@ 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)",
@@ -2601,7 +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, LLAMA_EXAMPLE_PARALLEL}));
+    ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
     add_opt(common_arg(
         {"--pos"}, "N",
         string_format("position of the passkey in the junk text (default: %d)", params.i_pos),
@@ -2651,20 +2636,13 @@ 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, LLAMA_EXAMPLE_PARALLEL}));
+    ).set_examples({LLAMA_EXAMPLE_BENCH}));
     add_opt(common_arg(
         {"-npp"}, "n0,n1,...",
         "number of prompt tokens",
@@ -2807,10 +2785,7 @@ 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)\n"
-            "[(card)](https://ggml.ai/f0.png)", params.n_cache_reuse
-        ),
+        string_format("min chunk size to attempt reusing from the cache via KV shifting (default: %d)", params.n_cache_reuse),
         [](common_params & params, int value) {
             params.n_cache_reuse = value;
         }
@@ -2863,25 +2838,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_JINJA"));
     add_opt(common_arg(
         {"--reasoning-format"}, "FORMAT",
-        "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n"
-        "- none: leaves thoughts unparsed in `message.content`\n"
-        "- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)\n"
-        "(default: deepseek)",
+        "reasoning format (default: deepseek; allowed values: deepseek, none)\n"
+        "controls whether thought tags are extracted from the response, and in which format they're returned. 'none' leaves thoughts unparsed in `message.content`, 'deepseek' puts them in `message.reasoning_content` (for DeepSeek R1 & Command R7B only).\n"
+        "only supported for non-streamed responses",
         [](common_params & params, const std::string & value) {
             /**/ if (value == "deepseek") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; }
-            else if (value == "deepseek-legacy") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY; }
             else if (value == "none") {     params.reasoning_format = COMMON_REASONING_FORMAT_NONE; }
-            else { throw std::invalid_argument("invalid value"); }
+            else { std::invalid_argument("invalid value"); }
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK"));
-    add_opt(common_arg(
-        {"--reasoning-budget"}, "N",
-        "controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)",
-        [](common_params & params, int value) {
-            if (value != 0 && value != -1) { throw std::invalid_argument("invalid value"); }
-            params.reasoning_budget = value;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK_BUDGET"));
     add_opt(common_arg(
         {"--chat-template"}, "JINJA_TEMPLATE",
         string_format(
@@ -2893,7 +2858,7 @@ 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, LLAMA_EXAMPLE_MTMD}).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(
@@ -2906,16 +2871,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             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),
@@ -2936,7 +2891,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.simple_io = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_INFILL}));
     add_opt(common_arg(
         {"--positive-file"}, "FNAME",
         string_format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()),
@@ -2980,7 +2935,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             /**/ if (value == "jsonl") { params.batched_bench_output_jsonl = true; }
             else if (value == "md") { params.batched_bench_output_jsonl = false; }
-            else { throw std::invalid_argument("invalid value"); }
+            else { std::invalid_argument("invalid value"); }
         }
     ).set_examples({LLAMA_EXAMPLE_BENCH}));
     add_opt(common_arg(
@@ -3012,13 +2967,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             common_log_set_verbosity_thold(INT_MAX);
         }
     ));
-    add_opt(common_arg(
-        {"--offline"},
-        "Offline mode: forces use of cache, prevents network access",
-        [](common_params & params) {
-            params.offline = true;
-        }
-    ).set_env("LLAMA_OFFLINE"));
     add_opt(common_arg(
         {"-lv", "--verbosity", "--log-verbosity"}, "N",
         "Set the verbosity threshold. Messages with a higher verbosity will be ignored.",

common/chat-parser.cpp

@@ -1,380 +0,0 @@
-#include "chat-parser.h"
-#include "common.h"
-#include "log.h"
-#include "regex-partial.h"
-
-#include <optional>
-#include <stdexcept>
-#include <string>
-#include <vector>
-
-using json = nlohmann::ordered_json;
-
-common_chat_msg_parser::common_chat_msg_parser(const std::string & input, bool is_partial, const common_chat_syntax & syntax)
-    : input_(input), is_partial_(is_partial), syntax_(syntax)
-{
-    result_.role = "assistant";
-
-    while (true) {
-        std::string id = std::to_string(std::rand());
-        if (input.find(id) == std::string::npos) {
-            healing_marker_ = id;
-            break;
-        }
-    }
-}
-
-std::string common_chat_msg_parser::str(const common_string_range & rng) const {
-    GGML_ASSERT(rng.begin <= rng.end);
-    return input_.substr(rng.begin, rng.end - rng.begin);
-}
-
-void common_chat_msg_parser::add_content(const std::string &content) {
-    result_.content += content;
-}
-
-void common_chat_msg_parser::add_reasoning_content(const std::string &reasoning_content) {
-    result_.reasoning_content += reasoning_content;
-}
-
-bool common_chat_msg_parser::add_tool_call(const std::string & name, const std::string & id, const std::string & arguments) {
-    if (name.empty()) {
-        return false;
-    }
-
-    common_chat_tool_call tool_call;
-    tool_call.name = name;
-    tool_call.arguments = arguments;
-    tool_call.id = id;
-
-    // LOG_DBG("Tool call arguments:\n\traw: %s\n\tresult: %s\n", arguments.c_str(), tool_call.arguments.c_str());
-    result_.tool_calls.emplace_back(tool_call);
-    return true;
-}
-bool common_chat_msg_parser::add_tool_call(const json & tool_call) {
-    std::string name = tool_call.contains("name") ? tool_call.at("name") : "";
-    std::string id = tool_call.contains("id") ? tool_call.at("id") : "";
-    std::string arguments = tool_call.contains("arguments") ? tool_call.at("arguments") : "";
-    return add_tool_call(name, id, arguments);
-}
-
-bool common_chat_msg_parser::add_tool_calls(const json & arr) {
-    for (const auto & item : arr) {
-        if (!add_tool_call(item)) {
-            return false;
-        }
-    }
-    return true;
-}
-void common_chat_msg_parser::finish() {
-    if (!is_partial_ && pos_ != input_.size()) {
-        throw std::runtime_error("Unexpected content at end of input");// + input_.substr(pos_));
-    }
-}
-
-bool common_chat_msg_parser::consume_spaces() {
-    const auto length = input_.size();
-    auto consumed = false;
-    while (pos_ < length && std::isspace(input_[pos_])) {
-        ++pos_;
-        consumed = true;
-    }
-    return consumed;
-}
-
-bool common_chat_msg_parser::try_consume_literal(const std::string & literal) {
-    auto pos = pos_;
-    for (auto i = 0u; i < literal.size(); ++i) {
-        if (pos >= input_.size()) {
-            return false;
-        }
-        if (input_[pos] != literal[i]) {
-            return false;
-        }
-        ++pos;
-    }
-    pos_ = pos;
-    return true;
-}
-
-std::optional<common_chat_msg_parser::find_regex_result>  common_chat_msg_parser::try_find_literal(const std::string & literal) {
-    auto idx = input_.find(literal, pos_);
-    if (idx != std::string::npos) {
-        find_regex_result res;
-        res.prelude = input_.substr(pos_, idx - pos_);
-        auto end = idx + literal.size();
-        res.groups.emplace_back(common_string_range{idx, end});
-        move_to(end);
-        return res;
-    }
-    if (is_partial_) {
-        idx = string_find_partial_stop(input_, literal);
-        if (idx != std::string::npos && idx >= pos_) {
-            find_regex_result res;
-            res.prelude = input_.substr(pos_, idx - pos_);
-            auto end = input_.size();
-            res.groups.emplace_back(common_string_range{idx, end});
-            move_to(end);
-            return res;
-        }
-    }
-    return std::nullopt;
-}
-
-void common_chat_msg_parser::consume_literal(const std::string & literal) {
-    if (!try_consume_literal(literal)) {
-        throw common_chat_msg_partial_exception(literal);
-    }
-}
-
-bool common_chat_msg_parser::try_parse_reasoning(const std::string & start_think, const std::string & end_think) {
-    auto handle_reasoning = [&](const std::string & reasoning, bool closed) {
-        auto stripped_reasoning = string_strip(reasoning);
-        if (stripped_reasoning.empty()) {
-            return;
-        }
-        if (syntax_.reasoning_in_content) {
-            add_content(syntax_.reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK ? "<think>" : start_think);
-            add_content(stripped_reasoning);
-            if (closed) {
-                add_content(syntax_.reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK ? "</think>" : end_think);
-            }
-        } else {
-            add_reasoning_content(stripped_reasoning);
-        }
-    };
-    if (syntax_.reasoning_format != COMMON_REASONING_FORMAT_NONE) {
-        if (syntax_.thinking_forced_open || try_consume_literal(start_think)) {
-            if (auto res = try_find_literal(end_think)) {
-                handle_reasoning(res->prelude, /* closed */ true);
-                consume_spaces();
-                return true;
-            }
-            auto rest = consume_rest();
-            if (!rest.empty()) {
-                handle_reasoning(rest, /* closed */ !is_partial());
-            }
-            // Allow unclosed thinking tags, for now (https://github.com/ggml-org/llama.cpp/issues/13812, https://github.com/ggml-org/llama.cpp/issues/13877)
-            // if (!syntax_.thinking_forced_open) {
-            //     throw common_chat_msg_partial_exception(end_think);
-            // }
-            return true;
-        }
-    }
-    return false;
-}
-
-std::string common_chat_msg_parser::consume_rest() {
-    auto rest = input_.substr(pos_);
-    pos_ = input_.size();
-    return rest;
-}
-
-// Tries to find the regex, consumes it (pos right after it) and gives the prelude (right before it) and the groups to the callback.
-std::optional<common_chat_msg_parser::find_regex_result> common_chat_msg_parser::try_find_regex(const common_regex & regex, size_t from, bool add_prelude_to_content) {
-    auto m = regex.search(input_, from == std::string::npos ? pos_ : from);
-    if (m.type == COMMON_REGEX_MATCH_TYPE_NONE) {
-        return std::nullopt;
-    }
-    auto prelude = input_.substr(pos_, m.groups[0].begin - pos_);
-    pos_ = m.groups[0].end;
-
-    if (add_prelude_to_content) {
-        add_content(prelude);
-    }
-    if (m.type == COMMON_REGEX_MATCH_TYPE_PARTIAL) {
-        if (is_partial()) {
-            throw common_chat_msg_partial_exception(regex.str());
-        }
-        return std::nullopt;
-    }
-    return find_regex_result{prelude, m.groups};
-}
-
-common_chat_msg_parser::find_regex_result common_chat_msg_parser::consume_regex(const common_regex & regex) {
-    if (auto result = try_consume_regex(regex)) {
-        return *result;
-    }
-    throw common_chat_msg_partial_exception(regex.str());
-}
-
-std::optional<common_chat_msg_parser::find_regex_result> common_chat_msg_parser::try_consume_regex(const common_regex & regex) {
-    auto m = regex.search(input_, pos_);
-    if (m.type == COMMON_REGEX_MATCH_TYPE_NONE) {
-        return std::nullopt;
-    }
-    if (m.type == COMMON_REGEX_MATCH_TYPE_PARTIAL) {
-        if (is_partial()) {
-            throw common_chat_msg_partial_exception(regex.str());
-        }
-        return std::nullopt;
-    }
-    if (m.groups[0].begin != pos_) {
-        // Didn't match at the current position.
-        return std::nullopt;
-    }
-    pos_ = m.groups[0].end;
-
-    return find_regex_result {
-        /* .prelude = */ "",
-        m.groups,
-    };
-}
-
-std::optional<common_json> common_chat_msg_parser::try_consume_json() {
-    auto it = input_.cbegin() + pos_;
-    const auto end = input_.cend();
-    common_json result;
-    if (!common_json_parse(it, end, healing_marker_, result)) {
-        return std::nullopt;
-    }
-    pos_ = std::distance(input_.cbegin(), it);
-    if (result.healing_marker.marker.empty()) {
-        // No healing marker, just return the parsed json
-        return result;
-    }
-    if (!is_partial()) {
-        throw common_chat_msg_partial_exception("JSON");
-    }
-    return result;
-}
-
-common_json common_chat_msg_parser::consume_json() {
-    if (auto result = try_consume_json()) {
-        return *result;
-    }
-    throw common_chat_msg_partial_exception("JSON");
-}
-
-common_chat_msg_parser::consume_json_result common_chat_msg_parser::consume_json_with_dumped_args(
-    const std::vector<std::vector<std::string>> & args_paths,
-    const std::vector<std::vector<std::string>> & content_paths
-) {
-    if (auto result = try_consume_json_with_dumped_args(args_paths, content_paths)) {
-        return *result;
-    }
-    throw common_chat_msg_partial_exception("JSON");
-}
-
-std::optional<common_chat_msg_parser::consume_json_result> common_chat_msg_parser::try_consume_json_with_dumped_args(
-    const std::vector<std::vector<std::string>> & args_paths,
-    const std::vector<std::vector<std::string>> & content_paths
-) {
-    auto partial = try_consume_json();
-    if (!partial) {
-        return std::nullopt;
-    }
-    auto is_arguments_path = [&](const std::vector<std::string> & path) {
-        return std::find(args_paths.begin(), args_paths.end(), path) != args_paths.end();
-    };
-    auto is_content_path = [&](const std::vector<std::string> & path) {
-        return std::find(content_paths.begin(), content_paths.end(), path) != content_paths.end();
-    };
-
-    if (partial->healing_marker.marker.empty()) {
-        if (args_paths.empty()) {
-            // No arguments to dump, and JSON was parsed fully.
-            return consume_json_result {
-                partial->json,
-                /* .is_partial = */ false,
-            };
-        }
-        if (is_arguments_path({})) {
-            // Entire JSON is the arguments and was parsed fully.
-            return consume_json_result {
-                partial->json.dump(),
-                /* .is_partial = */ false,
-            };
-        }
-    }
-
-    LOG_DBG("Parsed partial JSON: %s (json_healing_marker: %s)\n", partial->json.dump().c_str(), partial->healing_marker.json_dump_marker.c_str());
-
-    auto found_healing_marker = false;
-    std::vector<std::string> path;
-    std::function<json(const json &)> remove_unsupported_healings_and_dump_args = [&](const json & j) -> json {
-        if (is_arguments_path(path)) {
-            auto arguments = j.dump();
-            if (is_partial() && !partial->healing_marker.marker.empty()) {
-                auto idx = arguments.find(partial->healing_marker.json_dump_marker);
-                if (idx != std::string::npos) {
-                    arguments.resize(idx);
-                    found_healing_marker = true;
-                }
-                if (arguments == "\"") {
-                    // This happens because of completing `:"$magic` after `"arguments"`
-                    arguments = "";
-                }
-            }
-            return arguments;
-        }
-        if (is_content_path(path)) {
-            if (!j.is_string()) {
-                throw std::runtime_error("Content path must be a string");
-            }
-            std::string str = j;
-            auto idx = str.find(partial->healing_marker.marker); // not using json_dump_marker as we're inside a string
-            if (idx != std::string::npos) {
-                str.resize(idx);
-                found_healing_marker = true;
-            }
-            return str;
-        }
-        if (j.is_object()) {
-            auto obj = json::object();
-            for (const auto & p : j.items()) {
-                const auto & key = p.key();
-                const auto & value = p.value();
-                const std::string key_str = key; // NOLINT
-                auto idx = key_str.find(healing_marker_);
-                if (idx != std::string::npos) {
-                    found_healing_marker = true;
-                    break;
-                }
-                path.push_back(key_str);
-                if (value.is_string()) {
-                    const std::string value_str = value;
-                    if (value_str.find(healing_marker_) != std::string::npos) {
-                        found_healing_marker = true;
-                        if (is_content_path(path)) {
-                            if (partial->healing_marker.marker == partial->healing_marker.json_dump_marker) {
-                                // The healing occurred inside the string: good. Otherwise we just ditch the entire key/value pair.
-                                obj[key] = remove_unsupported_healings_and_dump_args(value);
-                            }
-                        }
-                        break;
-                    }
-                    obj[key] = value;
-                } else {
-                    obj[key] = remove_unsupported_healings_and_dump_args(value);
-                }
-                path.pop_back();
-            }
-            return obj;
-        }
-        if (j.is_array()) {
-            auto arr = json::array();
-            for (const auto & value : j) {
-                if (value.is_string()) {
-                    std::string str = value;
-                    auto idx = str.find(healing_marker_);
-                    if (idx != std::string::npos) {
-                        // Don't heal array values that aren't in the arguments.
-                        found_healing_marker = true;
-                        break;
-                    }
-                }
-                arr.push_back(remove_unsupported_healings_and_dump_args(value));
-            }
-            return arr;
-        }
-        return j;
-    };
-
-    auto cleaned = remove_unsupported_healings_and_dump_args(partial->json);
-    LOG_DBG("Cleaned up JSON %s to %s (json_healing_marker : '%s')\n", partial->json.dump().c_str(), cleaned.dump().c_str(), partial->healing_marker.json_dump_marker.c_str());
-    return consume_json_result {
-        cleaned,
-        /* .is_partial = */ found_healing_marker,
-    };
-}

common/chat-parser.h

@@ -1,118 +0,0 @@
-#pragma once
-
-#include "chat.h"
-#include "json-partial.h"
-#include "regex-partial.h"
-
-#include <nlohmann/json.hpp>
-
-#include <optional>
-#include <string>
-#include <vector>
-
-class common_chat_msg_partial_exception : public std::runtime_error {
-  public:
-    common_chat_msg_partial_exception(const std::string & message) : std::runtime_error(message) {}
-};
-
-class common_chat_msg_parser {
-    std::string input_;
-    bool is_partial_;
-    common_chat_syntax syntax_;
-    std::string healing_marker_;
-
-    size_t pos_ = 0;
-    common_chat_msg result_;
-
-  public:
-    common_chat_msg_parser(const std::string & input, bool is_partial, const common_chat_syntax & syntax);
-    const std::string & input() const { return input_; }
-    size_t pos() const { return pos_; }
-    const std::string & healing_marker() const { return healing_marker_; }
-    const bool & is_partial() const { return is_partial_; }
-    const common_chat_msg & result() const { return result_; }
-    const common_chat_syntax & syntax() const { return syntax_; }
-
-    void move_to(size_t pos) {
-        if (pos > input_.size()) {
-            throw std::runtime_error("Invalid position!");
-        }
-        pos_ = pos;
-    }
-    void move_back(size_t n) {
-        if (pos_ < n) {
-            throw std::runtime_error("Can't move back that far!");
-        }
-        pos_ -= n;
-    }
-
-    // Get the substring of the input at the given range
-    std::string str(const common_string_range & rng) const;
-
-    // Appends to the result.content field
-    void add_content(const std::string & content);
-
-    // Appends to the result.reasoning_content field
-    void add_reasoning_content(const std::string & reasoning_content);
-
-    // Adds a tool call to the result. If the tool call is too incomplete (e.g. name empty), it won't add anything.
-    bool add_tool_call(const std::string & name, const std::string & id, const std::string & arguments);
-
-    // Adds a tool call using the "name", "id" and "arguments" fields of the json object
-    bool add_tool_call(const nlohmann::ordered_json & tool_call);
-
-    // Adds an array of tool calls using their "name", "id" and "arguments" fields.
-    bool add_tool_calls(const nlohmann::ordered_json & arr);
-
-    void finish();
-
-    bool consume_spaces();
-
-    void consume_literal(const std::string & literal);
-
-    bool try_parse_reasoning(const std::string & start_think, const std::string & end_think);
-
-    std::string consume_rest();
-
-    struct find_regex_result {
-        std::string prelude;
-        std::vector<common_string_range> groups;
-    };
-
-    std::optional<find_regex_result> try_find_regex(const common_regex & regex, size_t from = std::string::npos, bool add_prelude_to_content = true);
-
-    bool try_consume_literal(const std::string & literal);
-
-    std::optional<find_regex_result> try_find_literal(const std::string & literal);
-
-    find_regex_result consume_regex(const common_regex & regex);
-
-    std::optional<find_regex_result> try_consume_regex(const common_regex & regex);
-
-    std::optional<common_json> try_consume_json();
-    common_json consume_json();
-
-    struct consume_json_result {
-        nlohmann::ordered_json value;
-        bool is_partial;
-    };
-
-    /*
-        Consume (possibly partial) json and converts specific subtrees to (possibly truncated) JSON strings.
-
-        By default, object keys can't be truncated, nor can string values (their corresponding key is removed,
-        e.g. `{"foo": "bar", "baz": "b` -> `{"foo": "bar"}`
-
-        But one can allow subpaths to be kept truncated, and possibly json-dumped to truncated json strings
-        - with `content_paths={{"foo"}}` -> `{"foo": "b` -> {"foo": "b"}`
-        - with `args_paths={{"foo"}}` -> `{"foo": {"b` -> `{"foo": "{b"}`
-    */
-    consume_json_result consume_json_with_dumped_args(
-        const std::vector<std::vector<std::string>> & args_paths = {},
-        const std::vector<std::vector<std::string>> & content_paths = {}
-    );
-    std::optional<consume_json_result> try_consume_json_with_dumped_args(
-        const std::vector<std::vector<std::string>> & args_paths = {},
-        const std::vector<std::vector<std::string>> & content_paths = {}
-    );
-};

common/chat.h

@@ -3,8 +3,6 @@
 #pragma once
 
 #include "common.h"
-#include <functional>
-#include <chrono>
 #include <string>
 #include <vector>
 
@@ -14,19 +12,11 @@ struct common_chat_tool_call {
     std::string name;
     std::string arguments;
     std::string id;
-
-    bool operator==(const common_chat_tool_call & other) const {
-        return name == other.name && arguments == other.arguments && id == other.id;
-    }
 };
 
 struct common_chat_msg_content_part {
     std::string type;
     std::string text;
-
-    bool operator==(const common_chat_msg_content_part & other) const {
-        return type == other.type && text == other.text;
-    }
 };
 
 struct common_chat_msg {
@@ -37,51 +27,6 @@ struct common_chat_msg {
     std::string reasoning_content;
     std::string tool_name;
     std::string tool_call_id;
-
-    template <class T> T to_json_oaicompat() const;
-
-    bool empty() const {
-        return content.empty() && content_parts.empty() && tool_calls.empty() && reasoning_content.empty() && tool_name.empty() && tool_call_id.empty();
-    }
-    void ensure_tool_call_ids_set(std::vector<std::string> & ids_cache, const std::function<std::string()> & gen_tool_call_id) {
-        for (auto i = 0u; i < tool_calls.size(); i++) {
-            if (ids_cache.size() <= i) {
-                auto id = tool_calls[i].id;
-                if (id.empty()) {
-                    id = gen_tool_call_id();
-                }
-                ids_cache.push_back(id);
-            }
-            tool_calls[i].id = ids_cache[i];
-        }
-    }
-    bool operator==(const common_chat_msg & other) const {
-        return role == other.role
-            && content == other.content
-            && content_parts == other.content_parts
-            && tool_calls == other.tool_calls
-            && reasoning_content == other.reasoning_content
-            && tool_name == other.tool_name
-            && tool_call_id == other.tool_call_id;
-    }
-    bool operator!=(const common_chat_msg & other) const {
-        return !(*this == other);
-    }
-};
-
-struct common_chat_msg_diff {
-    std::string reasoning_content_delta;
-    std::string content_delta;
-    size_t tool_call_index = std::string::npos;
-    common_chat_tool_call tool_call_delta;
-
-    static std::vector<common_chat_msg_diff> compute_diffs(const common_chat_msg & previous_msg, const common_chat_msg & new_msg);
-
-    bool operator==(const common_chat_msg_diff & other) const {
-        return content_delta == other.content_delta
-        && tool_call_index == other.tool_call_index
-        && tool_call_delta == other.tool_call_delta;
-    }
 };
 
 struct common_chat_tool {
@@ -103,11 +48,14 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_LLAMA_3_X,
     COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS,
     COMMON_CHAT_FORMAT_DEEPSEEK_R1,
+    COMMON_CHAT_FORMAT_DEEPSEEK_R1_EXTRACT_REASONING,
     COMMON_CHAT_FORMAT_FIREFUNCTION_V2,
     COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2,
     COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
     COMMON_CHAT_FORMAT_HERMES_2_PRO,
+    COMMON_CHAT_FORMAT_HERMES_2_PRO_EXTRACT_REASONING,
     COMMON_CHAT_FORMAT_COMMAND_R7B,
+    COMMON_CHAT_FORMAT_COMMAND_R7B_EXTRACT_REASONING,
 
     COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
 };
@@ -122,9 +70,7 @@ struct common_chat_templates_inputs {
     std::vector<common_chat_tool> tools;
     common_chat_tool_choice tool_choice = COMMON_CHAT_TOOL_CHOICE_AUTO;
     bool parallel_tool_calls = false;
-    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE;
-    bool enable_thinking = true;
-    std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
+    bool extract_reasoning     = true;
 };
 
 struct common_chat_params {
@@ -132,21 +78,11 @@ struct common_chat_params {
     std::string                         prompt;
     std::string                         grammar;
     bool                                grammar_lazy = false;
-    bool                                thinking_forced_open = false;
     std::vector<common_grammar_trigger> grammar_triggers;
     std::vector<std::string>            preserved_tokens;
     std::vector<std::string>            additional_stops;
 };
 
-struct common_chat_syntax {
-    common_chat_format       format                = COMMON_CHAT_FORMAT_CONTENT_ONLY;
-    common_reasoning_format  reasoning_format      = COMMON_REASONING_FORMAT_NONE;
-    // Whether reasoning_content should be inlined in the content (e.g. for reasoning_format=deepseek in stream mode)
-    bool                     reasoning_in_content  = false;
-    bool                     thinking_forced_open  = false;
-    bool                     parse_tool_calls      = true;
-};
-
 // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
 bool common_chat_verify_template(const std::string & tmpl, bool use_jinja);
 
@@ -183,9 +119,8 @@ std::string common_chat_format_example(
     const struct common_chat_templates * tmpls,
     bool use_jinja);
 
-const char*               common_chat_format_name(common_chat_format format);
-const char*               common_reasoning_format_name(common_reasoning_format format);
-common_chat_msg           common_chat_parse(const std::string & input, bool is_partial, const common_chat_syntax & syntax);
+std::string               common_chat_format_name(common_chat_format format);
+common_chat_msg           common_chat_parse(      const std::string & input, common_chat_format format);
 
 common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice);
 
@@ -198,5 +133,3 @@ template <class T> T common_chat_msgs_to_json_oaicompat(const std::vector<common
 // T can be std::string containing JSON or nlohmann::ordered_json
 template <class T> std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const T & tools);
 template <class T> T common_chat_tools_to_json_oaicompat(const std::vector<common_chat_tool> & tools);
-
-template <class T> T common_chat_msg_diff_to_json_oaicompat(const common_chat_msg_diff & diff);

common/common.cpp

@@ -203,7 +203,6 @@ bool set_process_priority(enum ggml_sched_priority prio) {
 
     DWORD p = NORMAL_PRIORITY_CLASS;
     switch (prio) {
-        case GGML_SCHED_PRIO_LOW:      p = BELOW_NORMAL_PRIORITY_CLASS; break;
         case GGML_SCHED_PRIO_NORMAL:   p = NORMAL_PRIORITY_CLASS;       break;
         case GGML_SCHED_PRIO_MEDIUM:   p = ABOVE_NORMAL_PRIORITY_CLASS; break;
         case GGML_SCHED_PRIO_HIGH:     p = HIGH_PRIORITY_CLASS;         break;
@@ -229,7 +228,6 @@ bool set_process_priority(enum ggml_sched_priority prio) {
 
     int p = 0;
     switch (prio) {
-        case GGML_SCHED_PRIO_LOW:      p =  5;  break;
         case GGML_SCHED_PRIO_NORMAL:   p =  0;  break;
         case GGML_SCHED_PRIO_MEDIUM:   p = -5;  break;
         case GGML_SCHED_PRIO_HIGH:     p = -10; break;
@@ -445,25 +443,6 @@ 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");
@@ -851,7 +830,7 @@ std::string fs_get_cache_directory() {
     if (getenv("LLAMA_CACHE")) {
         cache_directory = std::getenv("LLAMA_CACHE");
     } else {
-#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || defined(__OpenBSD__)
+#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX)
         if (std::getenv("XDG_CACHE_HOME")) {
             cache_directory = std::getenv("XDG_CACHE_HOME");
         } else {
@@ -905,16 +884,13 @@ struct common_init_result common_init_from_params(common_params & params) {
             ok = false;
         }
 
-        bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
-        bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL;
-
-        if (!has_eos && !has_sep) {
-            LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__);
+        if (llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
+            LOG_WRN("%s: warning: vocab does not have an EOS token, reranking will not work\n", __func__);
             ok = false;
-        } else if (!has_eos) {
-            LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
-        } else if (!has_sep) {
-            LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
+        }
+
+        if (llama_vocab_sep(vocab) == LLAMA_TOKEN_NULL) {
+            LOG_WRN("%s: warning: vocab does not have a  SEP token, reranking will not work\n", __func__);
             ok = false;
         }
 
@@ -1107,9 +1083,6 @@ 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;
 }
 
@@ -1123,6 +1096,7 @@ 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;
@@ -1140,8 +1114,6 @@ 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;
@@ -1334,6 +1306,81 @@ 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
 //
@@ -1518,20 +1565,3 @@ 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

@@ -6,7 +6,6 @@
 
 #include <set>
 #include <string>
-#include <string_view>
 #include <vector>
 #include <sstream>
 
@@ -67,6 +66,7 @@ enum llama_example {
     LLAMA_EXAMPLE_COMMON,
     LLAMA_EXAMPLE_SPECULATIVE,
     LLAMA_EXAMPLE_MAIN,
+    LLAMA_EXAMPLE_INFILL,
     LLAMA_EXAMPLE_EMBEDDING,
     LLAMA_EXAMPLE_PERPLEXITY,
     LLAMA_EXAMPLE_RETRIEVAL,
@@ -76,7 +76,7 @@ enum llama_example {
     LLAMA_EXAMPLE_SERVER,
     LLAMA_EXAMPLE_CVECTOR_GENERATOR,
     LLAMA_EXAMPLE_EXPORT_LORA,
-    LLAMA_EXAMPLE_MTMD,
+    LLAMA_EXAMPLE_LLAVA,
     LLAMA_EXAMPLE_LOOKUP,
     LLAMA_EXAMPLE_PARALLEL,
     LLAMA_EXAMPLE_TTS,
@@ -96,7 +96,6 @@ 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
@@ -115,7 +114,7 @@ enum common_grammar_trigger_type {
     COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN,
     COMMON_GRAMMAR_TRIGGER_TYPE_WORD,
     COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
-    COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,
+    COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_START,
 };
 
 struct common_grammar_trigger {
@@ -162,7 +161,6 @@ 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,
@@ -215,8 +213,7 @@ struct common_params_vocoder {
 
 enum common_reasoning_format {
     COMMON_REASONING_FORMAT_NONE,
-    COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY, // Extract thinking tag contents and return as `message.reasoning_content`, or leave inline in <think> tags in stream mode
-    COMMON_REASONING_FORMAT_DEEPSEEK,        // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
+    COMMON_REASONING_FORMAT_DEEPSEEK, // Extract thinking tag contents and return as `message.reasoning_content`
 };
 
 struct common_params {
@@ -292,7 +289,6 @@ struct common_params {
     int32_t verbosity                  = 0;
     int32_t control_vector_layer_start = -1; // layer range for control vector
     int32_t control_vector_layer_end   = -1; // layer range for control vector
-    bool    offline                    = false;
 
     int32_t ppl_stride      = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
     int32_t ppl_output_type = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
@@ -325,17 +321,17 @@ 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
 
@@ -344,7 +340,7 @@ struct common_params {
 
     common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO;
 
-    // multimodal models (see tools/mtmd)
+    // multimodal models (see examples/llava)
     struct common_params_model mmproj;
     bool mmproj_use_gpu = true;     // use GPU for multimodal model
     bool no_mmproj = false;         // explicitly disable multimodal model
@@ -370,8 +366,6 @@ struct common_params {
     bool use_jinja = false;                                                                                 // NOLINT
     bool enable_chat_template = true;
     common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
-    int reasoning_budget = -1;
-    bool prefill_assistant = true;                                                                          // if true, any trailing assistant message will be prefilled into the response
 
     std::vector<std::string> api_keys;
 
@@ -415,14 +409,13 @@ 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 = "tools/cvector-generator/positive.txt";
-    std::string cvector_negative_file = "tools/cvector-generator/negative.txt";
+    std::string cvector_positive_file = "examples/cvector-generator/positive.txt";
+    std::string cvector_negative_file = "examples/cvector-generator/negative.txt";
 
     bool spm_infill = false; // suffix/prefix/middle pattern for infill
 
@@ -431,11 +424,6 @@ 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
@@ -513,9 +501,10 @@ static bool string_starts_with(const std::string & str,
     return str.rfind(prefix, 0) == 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);
+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;
+}
 
 bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
 void string_process_escapes(std::string & input);
@@ -624,6 +613,16 @@ 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
 //
@@ -665,9 +664,3 @@ 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-partial.cpp

@@ -1,256 +0,0 @@
-#include "json-partial.h"
-
-#include "log.h"
-
-#include <nlohmann/json.hpp>
-
-#include <string>
-
-using json = nlohmann::ordered_json;
-
-enum common_json_stack_element_type {
-    COMMON_JSON_STACK_ELEMENT_OBJECT,
-    COMMON_JSON_STACK_ELEMENT_KEY,
-    COMMON_JSON_STACK_ELEMENT_ARRAY,
-};
-
-struct common_json_stack_element {
-    common_json_stack_element_type type;
-    std::string key;
-};
-
-bool common_json_parse(
-    const std::string & input,
-    const std::string & healing_marker,
-    common_json & out)
-{
-    std::string::const_iterator it = input.begin();
-    const auto end = input.end();
-    return common_json_parse(it, end, healing_marker, out);
-}
-
-bool common_json_parse(
-    std::string::const_iterator & it,
-    const std::string::const_iterator & end,
-    const std::string & healing_marker,
-    common_json & out)
-{
-    // // https://json.nlohmann.me/features/parsing/sax_interface/
-    struct json_error_locator : public nlohmann::json_sax<json> {
-        std::size_t position;
-        bool found_error;
-        std::string last_token;
-        std::string exception_message;
-        std::vector<common_json_stack_element> stack;
-
-        json_error_locator() : position(0), found_error(false) {}
-
-        bool parse_error(std::size_t position, const std::string & last_token, const json::exception & ex) override { // NOLINT
-            this->position = position - 1;
-            this->found_error = true;
-            this->last_token = last_token;
-            this->exception_message = ex.what();
-            return false;
-        }
-        void close_value() {
-            if (!stack.empty() && (stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY)) {
-                stack.pop_back();
-            }
-        }
-        bool null() override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool boolean(bool) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool number_integer(number_integer_t) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool number_unsigned(number_unsigned_t) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool number_float(number_float_t, const string_t &) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool string(string_t &) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool binary(binary_t &) override { // NOLINT
-            close_value();
-            return true;
-        }
-        bool start_object(std::size_t) override { // NOLINT
-            stack.push_back({COMMON_JSON_STACK_ELEMENT_OBJECT, ""});
-            return true;
-        }
-        bool end_object() override {
-            GGML_ASSERT(!stack.empty() && stack.back().type == COMMON_JSON_STACK_ELEMENT_OBJECT);
-            stack.pop_back();
-            close_value();
-            return true;
-        }
-        bool key(string_t & key) override { // NOLINT
-            stack.push_back({COMMON_JSON_STACK_ELEMENT_KEY, key});
-            return true;
-        }
-        bool start_array(std::size_t) override { // NOLINT
-            stack.push_back({COMMON_JSON_STACK_ELEMENT_ARRAY, ""});
-            return true;
-        }
-        bool end_array() override {
-            GGML_ASSERT(!stack.empty() && stack.back().type == COMMON_JSON_STACK_ELEMENT_ARRAY);
-            stack.pop_back();
-            close_value();
-            return true;
-        }
-    };
-    json_error_locator err_loc;
-    auto start = it;
-    json::sax_parse(it, end, &err_loc);
-
-    if (err_loc.found_error) {
-        it = start;
-        auto temptative_end = it + err_loc.position;
-        // LOG_DBG("Error at position %zu (is_end = %s): %s\n", err_loc.position, temptative_end == end ? "true" : "false", err_loc.exception_message.c_str());
-
-        auto input = std::string(it, temptative_end);
-        try {
-            out.json = json::parse(input);
-            // out.json = json::parse(it, temptative_end);
-            it = temptative_end;
-            return true;
-        } catch (const std::exception & ex) {
-            // No, needs healing.
-            LOG_DBG("Failed to parse up to error: %s: <<<%s>>>\n", ex.what(), std::string(it, temptative_end).c_str());
-        }
-        auto can_parse = [](const std::string & str) {
-            try {
-                auto _ = json::parse(str); // NOLINT
-                return true;
-            } catch (const std::exception &) {
-                return false;
-            }
-        };
-        if (!healing_marker.empty() && !err_loc.stack.empty()) {
-            std::string str(it, temptative_end);
-            auto last_non_sp_pos = str.find_last_not_of(" \n\r\t");
-            if (last_non_sp_pos == std::string::npos) {
-                throw std::runtime_error("Cannot heal a truncated JSON that stopped in an unknown location");
-            }
-            auto last_non_sp_char = str[last_non_sp_pos];
-            // Used to detect stops on a number, which may not be complete.
-            auto was_maybe_number = [&]() {
-                if (!str.empty() && std::isspace(str.back())) {
-                    return false;
-                }
-                return std::isdigit(last_non_sp_char) ||
-                    last_non_sp_char == '.' ||
-                    last_non_sp_char == 'e' ||
-                    last_non_sp_char == 'E' ||
-                    last_non_sp_char == '-';
-            };
-
-            std::string closing;
-            for (size_t i = err_loc.stack.size(); i > 0; i--) {
-                auto & el = err_loc.stack[i - 1];
-                if (el.type == COMMON_JSON_STACK_ELEMENT_OBJECT) {
-                    closing += "}";
-                } else if (el.type == COMMON_JSON_STACK_ELEMENT_ARRAY) {
-                    closing += "]";
-                } else if (el.type != COMMON_JSON_STACK_ELEMENT_KEY) {
-                    throw std::runtime_error("Unexpected stack element type");
-                }
-            }
-
-            const auto & magic_seed = out.healing_marker.marker = healing_marker;//"$llama.cpp.json$";
-
-            if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY) {
-                // We're inside an object value
-                if (last_non_sp_char == ':' && can_parse(str + "1" + closing)) {
-                    // Was about to create an object value
-                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                } else if (can_parse(str + ": 1" + closing)) {
-                    str += (out.healing_marker.json_dump_marker = ":\"" + magic_seed) + "\"" + closing;
-                } else if (last_non_sp_char == '{' && can_parse(str + closing)) {
-                    // Was about to create an object
-                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\": 1" + closing;
-                } else if (can_parse(str + "\"" + closing)) {
-                    // Was inside an object value string
-                    str += (out.healing_marker.json_dump_marker = magic_seed) + "\"" + closing;
-                } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) {
-                    // Was inside an object value string after an escape
-                    str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing;
-                } else {
-                    // find last :
-                    auto last_pos = str.find_last_of(':');
-                    if (last_pos == std::string::npos) {
-                        throw std::runtime_error("Cannot heal a truncated JSON that stopped in an unknown location");
-                    }
-                    // Cutting back to opening : for object value
-                    str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                }
-            } else if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_ARRAY) {
-                if ((last_non_sp_char == ',' || last_non_sp_char == '[') && can_parse(str + "1" + closing)) {
-                    // Was about to create an array value
-                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                } else if (can_parse(str + "\"" + closing)) {
-                    // Was inside an array value string
-                    str += (out.healing_marker.json_dump_marker = magic_seed) + "\"" + closing;
-                } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) {
-                    // Was inside an array value string after an escape
-                    str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing;
-                } else if (!was_maybe_number() && can_parse(str + ", 1" + closing)) {
-                    // Had just finished a value
-                    str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\"" + closing;
-                } else {
-                    auto last_pos = str.find_last_of("[,");
-                    if (last_pos == std::string::npos) {
-                        throw std::runtime_error("Cannot heal a truncated JSON array stopped in an unknown location");
-                    }
-                    // Cutting back to last [ or , for array value
-                    str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                }
-            } else if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_OBJECT) {
-                if ((last_non_sp_char == '{' && can_parse(str + closing)) ||
-                        (last_non_sp_char == ',' && can_parse(str + "\"\": 1" + closing))) {
-                    // Was about to create an object key+value
-                    str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\": 1" + closing;
-                } else if (!was_maybe_number() && can_parse(str + ",\"\": 1" + closing)) {
-                    // Was about to create an object key+value
-                    str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\": 1" + closing;
-                } else if (can_parse(str + "\": 1" + closing)) {
-                    // Was inside an object key string
-                    str += (out.healing_marker.json_dump_marker = magic_seed) + "\": 1" + closing;
-                } else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\": 1" + closing)) {
-                    // Was inside an object key string after an escape
-                    str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\": 1" + closing;
-                } else {
-                    auto last_pos = str.find_last_of(':');
-                    if (last_pos == std::string::npos) {
-                        throw std::runtime_error("Cannot heal a truncated JSON object stopped in an unknown location");
-                    }
-                    // fprintf(stderr, "Cutting back to last : for object key+value\n");
-                    str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
-                }
-            } else {
-                throw std::runtime_error("Cannot heal a truncated JSON object stopped in an unknown location");
-            }
-            // fprintf(stderr, "HEALED:\nSTRING <<<\n%s\n>>>\n\nmagic_cut: <<<\n%s\n>>>\n\n", str.c_str(), out.healing_marker.json_dump_marker.c_str());
-            out.json = json::parse(str);
-            it = temptative_end;
-            return true;
-        }
-        // TODO: handle unclosed top-level primitive if the stack was empty but we got an error (e.g. "tru", "\"", etc...)
-        // fprintf(stderr, "Closing: TODO\n");
-        return false;
-    }
-    out.json = json::parse(it, end);
-    it = end;
-    return true;
-}

common/json-partial.h

@@ -1,38 +0,0 @@
-#pragma once
-
-#include <nlohmann/json.hpp>
-
-// Healing marker (empty if the JSON was fully parsed / wasn't healed).
-struct common_healing_marker {
-    // Raw marker.
-    std::string marker;
-
-    // Cutting the `common_json.json.dump()` string at the (only) occurrence of this marker should yield the original partial JSON string (modulo spaces / if it had the same dump format).
-    std::string json_dump_marker;
-};
-
-// Represents a parsed JSON object, with its optional healing marker (a JSON dump fragment that can be used to find the position of healing in the JSON dump string)
-struct common_json {
-    nlohmann::ordered_json json;
-
-    common_healing_marker healing_marker;
-};
-
-// Parse the JSON string, healing (closing) any partial JSON if `healing_marker` is not empty.
-//
-// Healing completes partial JSON strings by adding a (possibly modified) healing marker, then whatever is needed to close the JSON.
-// This allows to parse the resulting healed JSON string, yet be able to cut it again if needed at the healing marker.
-// (this is used when parsing JSON outputs from the models, then crafting partial JSONs for the partial tool calls in OAI format).
-//
-// For instance, parsing `{` with a healing marker `foo` will produce a healed JSON `{"foo":1}`, w/ json_dump_marker = `"foo"` (which can be used to break the JSON again).
-bool common_json_parse(
-    const std::string & input,
-    const std::string & healing_marker,
-    common_json & out);
-
-// Parse the JSON string (see overload above), but advancing an iterator to the end of the input when the (potentially partial) parsing succeeds.
-bool common_json_parse(
-    std::string::const_iterator & it,
-    const std::string::const_iterator & end,
-    const std::string & healing_marker,
-    common_json & out);

common/json-schema-to-grammar.cpp

@@ -1,9 +1,8 @@
 #include "json-schema-to-grammar.h"
 #include "common.h"
 
-#include <nlohmann/json.hpp>
-
 #include <algorithm>
+#include <fstream>
 #include <map>
 #include <regex>
 #include <sstream>

common/json-schema-to-grammar.h

@@ -1,9 +1,9 @@
 #pragma once
 
-#include <nlohmann/json_fwd.hpp>
-
-#include <functional>
-#include <string>
+#include "ggml.h"
+// Change JSON_ASSERT from assert() to GGML_ASSERT:
+#define JSON_ASSERT GGML_ASSERT
+#include "json.hpp"
 
 std::string json_schema_to_grammar(const nlohmann::ordered_json & schema,
                                    bool force_gbnf = false);

common/llguidance.cpp

@@ -189,7 +189,6 @@ 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

@@ -13,16 +13,14 @@
 #include <chrono>
 #include <cstddef>
 #include <cstdio>
-#include <ctime>
 #include <exception>
 #include <iomanip>
 #include <memory>
 #include <sstream>
-#include <stdexcept>
 #include <string>
 #include <vector>
 
-#include <nlohmann/json.hpp>
+#include <json.hpp>
 
 using json = nlohmann::ordered_json;
 
@@ -395,8 +393,8 @@ class chat_template {
 
             for (const auto & message_ : adjusted_messages) {
                 auto message = message_;
-                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());
+                if (!message.contains("role") || !message.contains("content")) {
+                    throw std::runtime_error("message must have 'role' and 'content' fields: " + message.dump());
                 }
                 std::string role = message.at("role");
 
@@ -417,6 +415,7 @@ 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") {
@@ -435,11 +434,8 @@ class chat_template {
                         auto obj = json {
                             {"tool_calls", tool_calls},
                         };
-                        if (message.contains("content")) {
-                            auto content = message.at("content");
-                            if (!content.is_null() && !content.empty()) {
-                                obj["content"] = content;
-                            }
+                        if (!content.is_null() && !content.empty()) {
+                            obj["content"] = content;
                         }
                         message["content"] = obj.dump(2);
                         message.erase("tool_calls");

common/minja/minja.hpp

@@ -11,7 +11,6 @@
 #include <algorithm>
 #include <cctype>
 #include <cstddef>
-#include <cstdint>
 #include <cmath>
 #include <exception>
 #include <functional>
@@ -29,7 +28,7 @@
 #include <utility>
 #include <vector>
 
-#include <nlohmann/json.hpp>
+#include <json.hpp>
 
 using json = nlohmann::ordered_json;
 
@@ -234,7 +233,7 @@ public:
       }
     } else if (is_object()) {
       if (!index.is_hashable())
-        throw std::runtime_error("Unhashable type: " + index.dump());
+        throw std::runtime_error("Unashable type: " + index.dump());
       auto it = object_->find(index.primitive_);
       if (it == object_->end())
         throw std::runtime_error("Key not found: " + index.dump());
@@ -253,7 +252,7 @@ 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("Unhashable type: " + dump());
+      if (!key.is_hashable()) throw std::runtime_error("Unashable type: " + dump());
       auto it = object_->find(key.primitive_);
       if (it == object_->end()) return Value();
       return it->second;
@@ -262,7 +261,7 @@ 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("Unhashable type: " + dump());
+    if (!key.is_hashable()) throw std::runtime_error("Unashable type: " + dump());
     (*object_)[key.primitive_] = value;
   }
   Value call(const std::shared_ptr<Context> & context, ArgumentsValue & args) const {
@@ -399,7 +398,7 @@ public:
       }
       return false;
     } else if (object_) {
-      if (!value.is_hashable()) throw std::runtime_error("Unhashable type: " + value.dump());
+      if (!value.is_hashable()) throw std::runtime_error("Unashable 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());
@@ -417,7 +416,7 @@ public:
     return const_cast<Value*>(this)->at(index);
   }
   Value& at(const Value & index) {
-    if (!index.is_hashable()) throw std::runtime_error("Unhashable type: " + dump());
+    if (!index.is_hashable()) throw std::runtime_error("Unashable 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());
@@ -677,8 +676,8 @@ public:
 class VariableExpr : public Expression {
     std::string name;
 public:
-    VariableExpr(const Location & loc, const std::string& n)
-      : Expression(loc), name(n) {}
+    VariableExpr(const Location & location, const std::string& n)
+      : Expression(location), name(n) {}
     std::string get_name() const { return name; }
     Value do_evaluate(const std::shared_ptr<Context> & context) const override {
         if (!context->contains(name)) {
@@ -1201,9 +1200,9 @@ public:
 
 class SliceExpr : public Expression {
 public:
-    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)) {}
+    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)) {}
     Value do_evaluate(const std::shared_ptr<Context> &) const override {
         throw std::runtime_error("SliceExpr not implemented");
     }
@@ -1220,35 +1219,18 @@ 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 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);
+          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();
           if (target_value.is_string()) {
             std::string s = target_value.get<std::string>();
-
-            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;
-
+            if (start < 0) start = s.size() + start;
+            if (end < 0) end = s.size() + end;
+            return s.substr(start, end - start);
           } 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 (int64_t i = start; step > 0 ? i < end : i > end; i += step) {
+            for (auto i = start; i < end; ++i) {
               result.push_back(target_value.at(i));
             }
             return result;
@@ -1323,8 +1305,6 @@ 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();
@@ -1540,10 +1520,6 @@ 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;
@@ -2106,37 +2082,28 @@ private:
 
       while (it != end && consumeSpaces() && peekSymbols({ "[", "." })) {
         if (!consumeToken("[").empty()) {
-          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();
-            }
+            std::shared_ptr<Expression> index;
             if (!consumeToken(":").empty()) {
-              has_second_colon = true;
-              if (!peekSymbols({ "]" })) {
-                step = parseExpression();
+              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);
               }
             }
-          }
+            if (!index) throw std::runtime_error("Empty index in subscript");
+            if (consumeToken("]").empty()) throw std::runtime_error("Expected closing bracket in subscript");
 
-          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));
+            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

@@ -1,204 +0,0 @@
-#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

@@ -1,56 +0,0 @@
-#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

@@ -1,7 +1,6 @@
 #include "sampling.h"
 
 #include "common.h"
-#include "log.h"
 
 #include <cmath>
 #include <unordered_map>
@@ -161,7 +160,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
         GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
 #endif // LLAMA_USE_LLGUIDANCE
     } else {
-        std::vector<std::string> trigger_patterns;
+        std::vector<std::string> patterns_at_start;
         std::vector<std::string> patterns_anywhere;
         std::vector<llama_token> trigger_tokens;
         for (const auto & trigger : params.grammar_triggers) {
@@ -173,13 +172,10 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                     break;
                 }
                 case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN:
+                case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_START:
                 {
-                    patterns_anywhere.push_back(trigger.value);
-                    break;
-                }
-                case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL:
-                {
-                    trigger_patterns.push_back(trigger.value);
+                    const auto & pattern = trigger.value;
+                    (trigger.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_START ? patterns_at_start : patterns_anywhere).push_back(pattern);
                     break;
                 }
                 case COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN:
@@ -193,6 +189,10 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
             }
         }
 
+        std::vector<std::string> trigger_patterns;
+        if (!patterns_at_start.empty()) {
+            trigger_patterns.push_back("^(" + string_join(patterns_at_start, "|") + ")[\\s\\S]*");
+        }
         if (!patterns_anywhere.empty()) {
             trigger_patterns.push_back("^[\\s\\S]*?(" + string_join(patterns_anywhere, "|") + ")[\\s\\S]*");
         }
@@ -229,48 +229,51 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                 params.logit_bias.data()));
 
     if (params.mirostat == 0) {
-        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());
-                        }
+        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()));
-                    }
-                    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_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_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));
@@ -472,7 +475,6 @@ 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';
@@ -488,7 +490,6 @@ 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";
@@ -503,7 +504,6 @@ 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 },
@@ -517,7 +517,6 @@ 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 },
@@ -534,16 +533,14 @@ 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);
-            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;
+        } else {
+            if (allow_alt_names) {
+                sampler = sampler_alt_name_map.find(name);
+                if (sampler != sampler_alt_name_map.end()) {
+                    samplers.push_back(sampler->second);
+                }
             }
         }
-        LOG_WRN("%s: unable to match sampler by name '%s'\n", __func__, name.c_str());
     }
 
     return samplers;
@@ -555,7 +552,6 @@ 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 },
@@ -570,8 +566,6 @@ 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

@@ -1,6 +1,28 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
 
+# This script downloads the tokenizer models of the specified models from Huggingface and
+# generates the get_vocab_base_pre() function for convert_hf_to_gguf.py
+#
+# This is necessary in order to analyze the type of pre-tokenizer used by the model and
+# provide the necessary information to llama.cpp via the GGUF header in order to implement
+# the same pre-tokenizer.
+#
+# ref: https://github.com/ggml-org/llama.cpp/pull/6920
+#
+# Instructions:
+#
+# - Add a new model to the "models" list
+# - Run the script with your huggingface token:
+#
+#   python3 convert_hf_to_gguf_update.py <huggingface_token>
+#
+# - The convert_hf_to_gguf.py script will have had its get_vocab_base_pre() function updated
+# - Update llama.cpp with the new pre-tokenizer if necessary
+#
+# TODO: generate tokenizer tests for llama.cpp
+#
+
 import logging
 import os
 import pathlib
@@ -10,7 +32,6 @@ import requests
 import sys
 import json
 import shutil
-import argparse
 
 from hashlib import sha256
 from enum import IntEnum, auto
@@ -20,11 +41,6 @@ logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger("convert_hf_to_gguf_update")
 sess = requests.Session()
 
-convert_py_pth = pathlib.Path("convert_hf_to_gguf.py")
-convert_py = convert_py_pth.read_text(encoding="utf-8")
-hf_token_pth = pathlib.Path.home() / ".cache" / "huggingface" / "token"
-hf_token = hf_token_pth.read_text(encoding="utf-8").strip() if hf_token_pth.exists() else None
-
 
 class TOKENIZER_TYPE(IntEnum):
     SPM = auto()
@@ -33,49 +49,20 @@ class TOKENIZER_TYPE(IntEnum):
     UGM = auto()
 
 
-DOC_STRING = """
-This script downloads the tokenizer models of the specified models from Huggingface and
-generates the get_vocab_base_pre() function for convert_hf_to_gguf.py
-
-/!\\ It is intended to be used by contributors and is not meant to be run by end users
-
-This is necessary in order to analyze the type of pre-tokenizer used by the model and
-provide the necessary information to llama.cpp via the GGUF header in order to implement
-the same pre-tokenizer.
-
-ref: https://github.com/ggml-org/llama.cpp/pull/6920
-
-Instructions:
-
-- Add a new model to the "models" list
-- Run the script with your huggingface token
-    By default, token will be read from ~/.cache/huggingface/token
-- The convert_hf_to_gguf.py script will have had its get_vocab_base_pre() function updated
-- Update llama.cpp with the new pre-tokenizer if necessary
-"""
-# TODO: generate tokenizer tests for llama.cpp
-
-parser = argparse.ArgumentParser(description=DOC_STRING, formatter_class=argparse.RawTextHelpFormatter)
-parser.add_argument(
-    "--full", action="store_true",
-    help="download full list of models - make sure you have access to all of them",
-)
-parser.add_argument(
-    "hf_token",
-    help="optional HF token",
-    nargs="?",
-)
-args = parser.parse_args()
-hf_token = args.hf_token if args.hf_token is not None else hf_token
-
-if hf_token is None:
-    logger.error("HF token is required. Please provide it as an argument or set it in ~/.cache/huggingface/token")
-    sys.exit(1)
-
 # TODO: this string has to exercise as much pre-tokenizer functionality as possible
 #       will be updated with time - contributions welcome
 CHK_TXT = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
 
+if len(sys.argv) == 2:
+    token = sys.argv[1]
+    if not token.startswith("hf_"):
+        logger.info("Huggingface token seems invalid")
+        logger.info("Usage: python convert_hf_to_gguf_update.py <huggingface_token>")
+        sys.exit(1)
+else:
+    logger.info("Usage: python convert_hf_to_gguf_update.py <huggingface_token>")
+    sys.exit(1)
+
 # TODO: add models here, base models preferred
 models = [
     {"name": "llama-spm",        "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
@@ -116,6 +103,7 @@ models = [
     {"name": "exaone",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", },
     {"name": "phi-2",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", },
     {"name": "chameleon",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", },
+    {"name": "minerva-7b",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", },
     {"name": "roberta-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
     {"name": "gigachat",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
     {"name": "megrez",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
@@ -126,17 +114,8 @@ models = [
     {"name": "trillion",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/trillionlabs/Trillion-7B-preview", },
     {"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", },
-]
-
-# some models are known to be broken upstream, so we will skip them as exceptions
-pre_computed_hashes = [
-    # chatglm-bpe has 2 hashes, why?
-    {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b"},
-    {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516"},
-    {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2"},
-    {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
 ]
 
 
@@ -189,29 +168,9 @@ def download_model(model):
             if os.path.isfile(save_path):
                 logger.info(f"{name}: File {save_path} already exists - skipping")
                 continue
-            download_file_with_auth(f"{repo}/resolve/main/{file}", hf_token, save_path)
+            download_file_with_auth(f"{repo}/resolve/main/{file}", token, save_path)
 
 
-# get list of existing models and chkhsh from the convert_hf_to_gguf.py file
-# returns mapping res --> chkhsh
-def get_existing_models(convert_py):
-    pattern = r'if chkhsh == "([a-f0-9]{64})":\s*\n\s*.*\s*res = "([^"]+)"'
-    matches = re.findall(pattern, convert_py)
-    output = {}
-    for chkhsh, res in matches:
-        output[res] = chkhsh
-    return output
-
-
-existing_models = {}
-all_models = models.copy()
-if not args.full:
-    # Filter out models that already exist in convert_hf_to_gguf.py
-    existing_models = get_existing_models(convert_py)
-    all_models = models.copy()
-    models = [model for model in all_models if model["name"] not in existing_models]
-
-logging.info(f"Downloading {len(models)} models...")
 for model in models:
     try:
         download_model(model)
@@ -222,10 +181,9 @@ for model in models:
 # generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function:
 
 src_ifs = ""
-for model in [*all_models, *pre_computed_hashes]:
+for model in models:
     name = model["name"]
     tokt = model["tokt"]
-    chkhsh = model.get("chkhsh")
 
     if tokt == TOKENIZER_TYPE.SPM or tokt == TOKENIZER_TYPE.UGM:
         continue
@@ -236,44 +194,35 @@ for model in [*all_models, *pre_computed_hashes]:
         continue
 
     # create the tokenizer
-    if chkhsh is not None:
-        # if the model has a pre-computed hash, use it
-        logger.info(f"Using pre-computed hash for model {name}: {chkhsh}")
-    elif name in existing_models:
-        # if the model already exists in convert_hf_to_gguf.py, skip compute hash
-        chkhsh = existing_models[name]
-    else:
-        # otherwise, compute the hash of the tokenizer
-        try:
-            logger.info(f"Loading tokenizer from {f'models/tokenizers/{name}'}...")
-            if name == "t5":
-                tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
-            else:
-                tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
-        except OSError as e:
-            logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
-            continue  # Skip to the next model if the tokenizer can't be loaded
+    try:
+        if name == "t5":
+            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
+        else:
+            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
+    except OSError as e:
+        logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
+        continue  # Skip to the next model if the tokenizer can't be loaded
 
-        chktok = tokenizer.encode(CHK_TXT)
-        chkhsh = sha256(str(chktok).encode()).hexdigest()
+    chktok = tokenizer.encode(CHK_TXT)
+    chkhsh = sha256(str(chktok).encode()).hexdigest()
 
-        logger.info(f"model: {name}")
-        logger.info(f"tokt: {tokt}")
-        logger.info(f"repo: {model['repo']}")
-        logger.info(f"chktok: {chktok}")
-        logger.info(f"chkhsh: {chkhsh}")
+    logger.info(f"model: {name}")
+    logger.info(f"tokt: {tokt}")
+    logger.info(f"repo: {model['repo']}")
+    logger.info(f"chktok: {chktok}")
+    logger.info(f"chkhsh: {chkhsh}")
 
-        # print the "pre_tokenizer" content from the tokenizer.json
-        with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f:
-            cfg = json.load(f)
-            normalizer = cfg["normalizer"]
-            logger.info("normalizer: " + json.dumps(normalizer, indent=4))
-            pre_tokenizer = cfg["pre_tokenizer"]
-            logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4))
-            if "ignore_merges" in cfg["model"]:
-                logger.info("ignore_merges: " + json.dumps(cfg["model"]["ignore_merges"], indent=4))
+    # print the "pre_tokenizer" content from the tokenizer.json
+    with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f:
+        cfg = json.load(f)
+        normalizer = cfg["normalizer"]
+        logger.info("normalizer: " + json.dumps(normalizer, indent=4))
+        pre_tokenizer = cfg["pre_tokenizer"]
+        logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4))
+        if "ignore_merges" in cfg["model"]:
+            logger.info("ignore_merges: " + json.dumps(cfg["model"]["ignore_merges"], indent=4))
 
-        logger.info("")
+    logger.info("")
 
     src_ifs += f"        if chkhsh == \"{chkhsh}\":\n"
     src_ifs += f"            # ref: {model['repo']}\n"
@@ -321,6 +270,8 @@ src_func = f"""
         return res
 """
 
+convert_py_pth = pathlib.Path("convert_hf_to_gguf.py")
+convert_py = convert_py_pth.read_text(encoding="utf-8")
 convert_py = re.sub(
     r"(# Marker: Start get_vocab_base_pre)(.+?)( +# Marker: End get_vocab_base_pre)",
     lambda m: m.group(1) + src_func + m.group(3),
@@ -336,7 +287,7 @@ logger.info("+++ convert_hf_to_gguf.py was updated")
 
 tests = [
     "ied 4 ½ months",
-    "Äpfel",
+    "Führer",
     "",
     " ",
     "  ",
@@ -415,10 +366,6 @@ for model in models:
         logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
         continue  # Skip this model and continue with the next one in the loop
 
-    if not os.path.exists(f"models/ggml-vocab-{name}.gguf"):
-        logger.info(f"Skip vocab files for model {name}, no GGUF file found")
-        continue
-
     with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f:
         for text in tests:
             f.write(f"{text}")

docs/backend/CANN.md

@@ -56,82 +56,60 @@ The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the abi
 
 ## Model Supports
 
-| Model Name                  | FP16  | Q4_0 | Q8_0 |
+| Model Name                  | FP16  | Q8_0 | Q4_0 |
 |:----------------------------|:-----:|:----:|:----:|
-| 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                    |   √   |   √  |   √  |
+| 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    |   √   |   √  |   √  |
 | GritLM-7B                   |   √   |   √  |   √  |
-| 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                   |   √   |   √  |   √  |
+| 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                  |   √   |   √  |   √  |
 
 
 
@@ -280,15 +258,6 @@ cmake --build build --config release
 ### **GitHub contribution**:
 Please add the **[CANN]** prefix/tag in issues/PRs titles to help the CANN-team check/address them without delay.
 
-## Updates
-### Basic Flash Attention Support
-The basic FA kernel with aclnnops has been added in aclnn_ops.cpp.
-Currently, the FA only supports the cases with FP16 KV tensors and NO logit softcap.
-Since the aclnn interface for flash attention cannot support the logit softcap, we will only update the quantized version in the future.
-
-Authors from Peking University: Bizhao Shi (bshi@pku.edu.cn), Yuxin Yang (yxyang@pku.edu.cn), Ruiyang Ma (ruiyang@stu.pku.edu.cn), and Guojie Luo (gluo@pku.edu.cn).
-
-We would like to thank Tuo Dai, Shanni Li, and all of the project maintainers from Huawei Technologies Co., Ltd for their help during the code development and pull request.
 
 ## TODO
 - Support more models and data types.

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 primarily designed for **Intel GPUs**.
-SYCL cross-platform capabilities enable support for Nvidia GPUs as well, with limited support for AMD.
+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.
 
 ## Recommended Release
 
-The following releases are verified and recommended:
+The SYCL backend would be broken by some PRs due to no online CI.
+
+The following release is verified with good quality:
 
 |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||
 
@@ -106,14 +106,15 @@ 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, 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  |
+| 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 |
 
 *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)**
@@ -137,11 +138,9 @@ 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 .
@@ -149,10 +148,9 @@ 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)*, set `--build-arg="GGML_SYCL_F16=OFF"` in the previous command.
+To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="GGML_SYCL_F16=ON"` argument from 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
 
@@ -252,7 +250,7 @@ sycl-ls
 
 - **Intel GPU**
 
-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:
+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:
 
 ```
 [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]
@@ -284,7 +282,7 @@ For AMD GPUs we should expect at least one SYCL-HIP device [`hip:gpu`]:
 
 #### Intel GPU
 
-```sh
+```
 ./examples/sycl/build.sh
 ```
 
@@ -353,7 +351,7 @@ 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 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).
+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.
 
 ##### Check device
 
@@ -400,15 +398,11 @@ 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
@@ -431,13 +425,13 @@ 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 99 -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 33 -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 99 -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 33 -sm layer
 ```
 
 *Notes:*
@@ -458,7 +452,7 @@ 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
 
@@ -635,7 +629,7 @@ 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 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).
+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.
 
 ##### Check device
 
@@ -654,7 +648,7 @@ 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|               |
@@ -664,14 +658,13 @@ 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|Default option. You may also want to `set ONEAPI_DEVICE_SELECTOR="level_zero:0"`|
+|0|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"` or no action|
 |1|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"`|
-|0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"` or `set ONEAPI_DEVICE_SELECTOR="level_zero:*"`|
+|0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`|
 
 #### Execute
 
@@ -680,13 +673,7 @@ Choose one of following methods to run.
 1. Script
 
 ```
-examples\sycl\win-run-llama-2.bat
-```
-
-or
-
-```
-examples\sycl\win-run-llama-3.bat
+examples\sycl\win-run-llama2.bat
 ```
 
 2. Command line
@@ -710,13 +697,13 @@ 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 99 -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 33 -s 0 -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 99 -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 33 -s 0 -sm layer
 ```
 
 
@@ -727,9 +714,7 @@ 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
 ```
@@ -741,17 +726,14 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 
 | Name               | Value                                 | Function                                    |
 |--------------------|---------------------------------------|---------------------------------------------|
-| GGML_SYCL          | ON (mandatory)                        | Enable build with SYCL code path.           |
+| GGML_SYCL          | ON (mandatory)                        | Enable build with SYCL code path.<br>FP32 path - recommended for better perforemance than FP16 on quantized model|
 | 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. (1.) |
+| GGML_SYCL_F16      | OFF *(default)* \|ON *(optional)*     | Enable FP16 build with SYCL code path.      |
 | 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                                                                                                                  |
@@ -759,7 +741,6 @@ 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 |
 
 
@@ -769,7 +750,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 
 ## Q&A
 
-- Error:  `error while loading shared libraries: libsycl.so: cannot open shared object file: No such file or directory`.
+- Error:  `error while loading shared libraries: libsycl.so.7: 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`.
@@ -798,18 +779,18 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 
   It's same for other projects including llama.cpp SYCL backend.
 
-- `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`
+- 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`
 
-  You are running out of Device Memory.
+  Device Memory is not enough.
 
   |Reason|Solution|
   |-|-|
-  | 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.|
+  |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.|
 
 ### **GitHub contribution**:
-Please add the `SYCL :` prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay.
+Please add the **[SYCL]** prefix/tag in issues/PRs titles to help the SYCL-team check/address them without delay.
 
 ## TODO
 
-- Review ZES_ENABLE_SYSMAN: https://github.com/intel/compute-runtime/blob/master/programmers-guide/SYSMAN.md#support-and-limitations
+- NA

docs/build.md

@@ -1,9 +1,5 @@
 # Build llama.cpp locally
 
-The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](include/llama.h).
-
-The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server.
-
 **To get the Code:**
 
 ```bash
@@ -67,7 +63,6 @@ cmake --build build --config Release
       cmake --preset x64-windows-llvm-release
       cmake --build build-x64-windows-llvm-release
       ```
-- Curl usage is enabled by default and can be turned off with `-DLLAMA_CURL=OFF`. Otherwise you need to install development libraries for libcurl.
 
 ## BLAS Build
 

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](/tools/main/)
-- [imatrix](/tools/imatrix/)
-- [quantize](/tools/quantize/)
-- [server](/tools/server/)
+- [main](/examples/main/)
+- [imatrix](/examples/imatrix/)
+- [quantize](/examples/quantize/)
+- [server](/examples/server/)
 
 ### 1. Convert the model to GGUF
 

docs/docker.md

@@ -22,9 +22,6 @@ 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).
 
@@ -107,7 +104,7 @@ You may want to pass in some different `ARGS`, depending on the MUSA environment
 
 The defaults are:
 
-- `MUSA_VERSION` set to `rc4.0.1`
+- `MUSA_VERSION` set to `rc3.1.1`
 
 The resulting images, are essentially the same as the non-MUSA images:
 

docs/function-calling.md

@@ -2,6 +2,7 @@
 
 [chat.h](../common/chat.h) (https://github.com/ggml-org/llama.cpp/pull/9639) adds support for [OpenAI-style function calling](https://platform.openai.com/docs/guides/function-calling) and is used in:
 - `llama-server` when started w/ `--jinja` flag
+- `llama-cli` (WIP: https://github.com/ggml-org/llama.cpp/pull/11556)
 
 ## Universal support w/ Native & Generic handlers
 
@@ -324,65 +325,36 @@ To get the official template from original HuggingFace repos, you can use [scrip
 > [!TIP]
 > If there is no official `tool_use` Jinja template, you may want to set `--chat-template chatml` to use a default that works with many models (YMMV!), or write your own (e.g. we provide a custom [llama-cpp-deepseek-r1.jinja](../models/templates/llama-cpp-deepseek-r1.jinja) for DeepSeek R1 distills)
 
-> [!CAUTION]
-> Beware of extreme KV quantizations (e.g. `-ctk q4_0`), they can substantially degrade the model's tool calling performance.
-
 Test in CLI (or with any library / software that can use OpenAI-compatible API backends):
 
 ```bash
 curl http://localhost:8080/v1/chat/completions -d '{
-    "model": "gpt-3.5-turbo",
-    "tools": [
-        {
-        "type":"function",
-        "function":{
-            "name":"python",
-            "description":"Runs code in an ipython interpreter and returns the result of the execution after 60 seconds.",
-            "parameters":{
-            "type":"object",
-            "properties":{
-                "code":{
-                "type":"string",
-                "description":"The code to run in the ipython interpreter."
-                }
-            },
-            "required":["code"]
+"model": "gpt-3.5-turbo",
+"tools": [
+    {
+    "type":"function",
+    "function":{
+        "name":"python",
+        "description":"Runs code in an ipython interpreter and returns the result of the execution after 60 seconds.",
+        "parameters":{
+        "type":"object",
+        "properties":{
+            "code":{
+            "type":"string",
+            "description":"The code to run in the ipython interpreter."
             }
+        },
+        "required":["code"]
         }
-        }
-    ],
-    "messages": [
-        {
-        "role": "user",
-        "content": "Print a hello world message with python."
-        }
-    ]
-}'
-
-
-curl http://localhost:8080/v1/chat/completions -d '{
-    "model": "gpt-3.5-turbo",
-    "messages": [
-        {"role": "system", "content": "You are a chatbot that uses tools/functions. Dont overthink things."},
-        {"role": "user", "content": "What is the weather in Istanbul?"}
-    ],
-    "tools": [{
-        "type":"function",
-        "function":{
-            "name":"get_current_weather",
-            "description":"Get the current weather in a given location",
-            "parameters":{
-                "type":"object",
-                "properties":{
-                    "location":{
-                        "type":"string",
-                        "description":"The city and country/state, e.g. `San Francisco, CA`, or `Paris, France`"
-                    }
-                },
-                "required":["location"]
-            }
-        }
-    }]
+    }
+    }
+],
+"messages": [
+    {
+    "role": "user",
+    "content": "Print a hello world message with python."
+    }
+]
 }'
 ```
 

docs/install.md

@@ -1,42 +1,28 @@
 # Install pre-built version of llama.cpp
 
-| Install via | Windows | Mac | Linux |
-|-------------|---------|-----|-------|
-| Winget      | ✅      |      |      |
-| Homebrew    |         | ✅   | ✅   |
-| MacPorts    |         | ✅   |      |
-| Nix         |         | ✅   | ✅   |
+## Homebrew
 
-## Winget (Windows)
-
-```sh
-winget install llama.cpp
-```
-
-The package is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggml-org/llama.cpp/issues/8188
-
-## Homebrew (Mac and Linux)
+On Mac and Linux, the homebrew package manager can be used via
 
 ```sh
 brew install llama.cpp
 ```
-
 The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggml-org/llama.cpp/discussions/7668
 
-## MacPorts (Mac)
+## MacPorts
 
 ```sh
 sudo port install llama.cpp
 ```
+see also: https://ports.macports.org/port/llama.cpp/details/
 
-See also: https://ports.macports.org/port/llama.cpp/details/
+## Nix
 
-## Nix (Mac and Linux)
+On Mac and Linux, the Nix package manager can be used via
 
 ```sh
 nix profile install nixpkgs#llama-cpp
 ```
-
 For flake enabled installs.
 
 Or
@@ -48,3 +34,13 @@ nix-env --file '<nixpkgs>' --install --attr llama-cpp
 For non-flake enabled installs.
 
 This expression is automatically updated within the [nixpkgs repo](https://github.com/NixOS/nixpkgs/blob/nixos-24.05/pkgs/by-name/ll/llama-cpp/package.nix#L164).
+
+## Flox
+
+On Mac and Linux, Flox can be used to install llama.cpp within a Flox environment via
+
+```sh
+flox install llama-cpp
+```
+
+Flox follows the nixpkgs build of llama.cpp.

docs/multimodal.md

@@ -1,109 +0,0 @@
-# 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
-
-Currently, we support **image** and **audio** input. Audio is highly experimental and may have reduced quality.
-
-To enable it, you can 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/collections/ggml-org/multimodal-ggufs-68244e01ff1f39e5bebeeedc
-
-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`
-
-**Vision models**:
-
-```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
-
-# Moondream2 20250414 version
-(tool_name) -hf ggml-org/moondream2-20250414-GGUF
-
-```
-
-**Audio models**:
-
-```sh
-# Ultravox 0.5
-(tool_name) -hf ggml-org/ultravox-v0_5-llama-3_2-1b-GGUF
-(tool_name) -hf ggml-org/ultravox-v0_5-llama-3_1-8b-GGUF
-
-# Qwen2-Audio and SeaLLM-Audio
-# note: no pre-quantized GGUF this model, as they have very poor result
-# ref: https://github.com/ggml-org/llama.cpp/pull/13760
-```
-
-**Mixed modalities**:
-
-```sh
-# Qwen2.5 Omni
-# Capabilities: audio input, vision input
-(tool_name) -hf ggml-org/Qwen2.5-Omni-3B-GGUF
-(tool_name) -hf ggml-org/Qwen2.5-Omni-7B-GGUF
-```

docs/multimodal/MobileVLM.md

@@ -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 ./tools/mtmd/llava_surgery.py -m path/to/MobileVLM-1.7B
+python ./examples/llava/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 ./tools/mtmd/convert_image_encoder_to_gguf.py \
+python ./examples/llava/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 ./tools/mtmd/convert_image_encoder_to_gguf.py \
 ```
 
 ```sh
-python ./tools/mtmd/convert_image_encoder_to_gguf.py \
+python ./examples/llava/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 `tools/mtmd/android/build_64.sh`
+refer to `examples/llava/android/build_64.sh`
 ```sh
-mkdir tools/mtmd/android/build_64
-cd tools/mtmd/android/build_64
+mkdir examples/llava/android/build_64
+cd examples/llava/android/build_64
 ../build_64.sh
 ```
 ### run on Android

docs/multimodal/glmedge.md

@@ -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 ./tools/mtmd/glmedge-surgery.py -m ../model_path
+python ./examples/llava/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 ./tools/mtmd/glmedge-convert-image-encoder-to-gguf.py -m ../model_path --llava-projector ../model_path/glm.projector --output-dir ../model_path
+python ./examples/llava/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/llava.md

@@ -37,19 +37,19 @@ git clone https://huggingface.co/openai/clip-vit-large-patch14-336
 2. Install the required Python packages:
 
 ```sh
-pip install -r tools/mtmd/requirements.txt
+pip install -r examples/llava/requirements.txt
 ```
 
 3. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
 
 ```sh
-python ./tools/mtmd/llava_surgery.py -m ../llava-v1.5-7b
+python ./examples/llava/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 ./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
+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
 ```
 
 5. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF:
@@ -69,12 +69,12 @@ git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
 2) Install the required Python packages:
 
 ```sh
-pip install -r tools/mtmd/requirements.txt
+pip install -r examples/llava/requirements.txt
 ```
 
 3) Use `llava_surgery_v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
 ```console
-python tools/mtmd/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/
+python examples/llava/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
 
@@ -88,7 +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 ./tools/mtmd/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
+python ./examples/llava/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
 

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 ./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 ./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 ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model
 
 # quantize int4 version

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 ./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 ./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 ./convert_hf_to_gguf.py ../MiniCPM-Llama3-V-2_5/model
 
 # quantize int4 version

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 ./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 ./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 ./convert_hf_to_gguf.py ../MiniCPM-V-2_6/model
 
 # quantize int4 version

examples/CMakeLists.txt

@@ -12,30 +12,51 @@ 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)
 
     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)
-        add_subdirectory(convert-llama2c-to-ggml)
         # 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)
+        add_subdirectory(llava)
+        if (GGML_RPC)
+            add_subdirectory(rpc)
+        endif()
         if (GGML_SYCL)
             add_subdirectory(sycl)
         endif()

examples/batched-bench/batched-bench.cpp

@@ -123,8 +123,8 @@ int main(int argc, char ** argv) {
 
                 common_batch_clear(batch);
 
-                for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) {
-                    for (int i = 0; i < pp; ++i) {
+                for (int i = 0; i < pp; ++i) {
+                    for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) {
                         common_batch_add(batch, 0, i, { j }, false);
                     }
                 }

examples/embedding/embedding.cpp

@@ -35,14 +35,23 @@ 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_decode(ctx, batch) < 0) {
-        LOG_ERR("%s : failed to process\n", __func__);
+    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__);
+        }
     }
 
     for (int i = 0; i < batch.n_tokens; i++) {

examples/imatrix/README.md

@@ -1,4 +1,4 @@
-# llama.cpp/tools/imatrix
+# llama.cpp/examples/imatrix
 
 Compute an importance matrix for a model and given text dataset. Can be used during quantization to enhance the quality of the quantized models.
 More information is available here: https://github.com/ggml-org/llama.cpp/pull/4861

examples/imatrix/imatrix.cpp

@@ -24,8 +24,7 @@ static void print_usage(int, char ** argv) {
     LOG("\n    %s \\\n"
             "       -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] \\\n"
             "       [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n"
-            "       [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...] \\\n"
-            "       [--parse-special]\n" , argv[0]);
+            "       [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]\n" , argv[0]);
     LOG("\n");
 }
 
@@ -47,7 +46,7 @@ private:
     common_params                          m_params;
     std::mutex                             m_mutex;
     int                                    m_last_call = 0;
-    std::vector<char>                      m_src1_data;
+    std::vector<float>                     m_src1_data;
     std::vector<char>                      m_ids; // the expert ids from ggml_mul_mat_id
 };
 
@@ -94,13 +93,11 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
     const bool is_host = ggml_backend_buffer_is_host(src1->buffer);
 
     if (!is_host) {
-        const size_t src1_nbytes = ggml_nbytes(src1);
-        m_src1_data.resize(src1_nbytes);
-        ggml_backend_tensor_get(src1, m_src1_data.data(), 0, src1_nbytes);
+        m_src1_data.resize(ggml_nelements(src1));
+        ggml_backend_tensor_get(src1, m_src1_data.data(), 0, ggml_nbytes(src1));
     }
 
-    const char * data = is_host ? (const char *) src1->data : m_src1_data.data();
-    GGML_ASSERT(src1->nb[0] == ggml_element_size(src1));
+    const float * data = is_host ? (const float *) src1->data : m_src1_data.data();
 
     // this has been adapted to the new format of storing merged experts in a single 3d tensor
     // ref: https://github.com/ggml-org/llama.cpp/pull/6387
@@ -147,7 +144,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
 
                     const int64_t i11 = idx % src1->ne[1];
                     const int64_t i12 = row;
-                    const float * x = (const float *)(data + i11*src1->nb[1] + i12*src1->nb[2]);
+                    const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]);
 
                     for (int j = 0; j < (int)src1->ne[0]; ++j) {
                         e.values[e_start + j] += x[j]*x[j];
@@ -183,7 +180,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
         ++e.ncall;
         LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
         for (int row = 0; row < (int)src1->ne[1]; ++row) {
-            const float * x = (const float *) (data + row * src1->nb[1]);
+            const float * x = data + row * src1->ne[0];
             for (int j = 0; j < (int)src1->ne[0]; ++j) {
                 e.values[j] += x[j]*x[j];
                 e.counts[j]++;
@@ -440,7 +437,7 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
     auto tim1 = std::chrono::high_resolution_clock::now();
     LOG_INF("%s: tokenizing the input ..\n", __func__);
 
-    std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, true, params.parse_special);
+    std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, true);
 
     auto tim2 = std::chrono::high_resolution_clock::now();
     LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
@@ -586,6 +583,7 @@ int main(int argc, char ** argv) {
     params.out_file = "imatrix.dat" ;
 
     params.n_ctx = 512;
+    params.logits_all = true;
     params.escape = false;
 
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_IMATRIX, print_usage)) {

examples/infill/CMakeLists.txt

@@ -0,0 +1,5 @@
+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

@@ -0,0 +1,47 @@
+# 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

@@ -0,0 +1,590 @@
+#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/llama-bench/README.md

@@ -1,4 +1,4 @@
-# llama.cpp/tools/llama-bench
+# llama.cpp/examples/llama-bench
 
 Performance testing tool for llama.cpp.
 
@@ -20,20 +20,10 @@ Performance testing tool for llama.cpp.
 ## Syntax
 
 ```
-usage: llama-bench [options]
+usage: ./llama-bench [options]
 
 options:
   -h, --help
-  --numa <distribute|isolate|numactl>       numa mode (default: disabled)
-  -r, --repetitions <n>                     number of times to repeat each test (default: 5)
-  --prio <0|1|2|3>                          process/thread priority (default: 0)
-  --delay <0...N> (seconds)                 delay between each test (default: 0)
-  -o, --output <csv|json|jsonl|md|sql>      output format printed to stdout (default: md)
-  -oe, --output-err <csv|json|jsonl|md|sql> output format printed to stderr (default: none)
-  -v, --verbose                             verbose output
-  --progress                                print test progress indicators
-
-test parameters:
   -m, --model <filename>                    (default: models/7B/ggml-model-q4_0.gguf)
   -p, --n-prompt <n>                        (default: 512)
   -n, --n-gen <n>                           (default: 128)
@@ -43,27 +33,28 @@ test parameters:
   -ub, --ubatch-size <n>                    (default: 512)
   -ctk, --cache-type-k <t>                  (default: f16)
   -ctv, --cache-type-v <t>                  (default: f16)
-  -dt, --defrag-thold <f>                   (default: -1)
-  -t, --threads <n>                         (default: system dependent)
+  -t, --threads <n>                         (default: 8)
   -C, --cpu-mask <hex,hex>                  (default: 0x0)
   --cpu-strict <0|1>                        (default: 0)
   --poll <0...100>                          (default: 50)
   -ngl, --n-gpu-layers <n>                  (default: 99)
-  -rpc, --rpc <rpc_servers>                 (default: none)
+  -rpc, --rpc <rpc_servers>                 (default: )
   -sm, --split-mode <none|layer|row>        (default: layer)
   -mg, --main-gpu <i>                       (default: 0)
   -nkvo, --no-kv-offload <0|1>              (default: 0)
   -fa, --flash-attn <0|1>                   (default: 0)
   -mmp, --mmap <0|1>                        (default: 1)
+  --numa <distribute|isolate|numactl>       (default: disabled)
   -embd, --embeddings <0|1>                 (default: 0)
   -ts, --tensor-split <ts0/ts1/..>          (default: 0)
-  -ot --override-tensors <tensor name pattern>=<buffer type>;...
-                                            (default: disabled)
-  -nopo, --no-op-offload <0|1>              (default: 0)
+  -r, --repetitions <n>                     (default: 5)
+  --prio <0|1|2|3>                          (default: 0)
+  --delay <0...N> (seconds)                 (default: 0)
+  -o, --output <csv|json|jsonl|md|sql>      (default: md)
+  -oe, --output-err <csv|json|jsonl|md|sql> (default: none)
+  -v, --verbose                             (default: 0)
 
-Multiple values can be given for each parameter by separating them with ','
-or by specifying the parameter multiple times. Ranges can be given as
-'first-last' or 'first-last+step' or 'first-last*mult'.
+Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.
 ```
 
 llama-bench can perform three types of tests:
@@ -80,6 +71,10 @@ Using the `-d <n>` option, each test can be run at a specified context depth, pr
 
 For a description of the other options, see the [main example](../main/README.md).
 
+Note:
+
+- When using SYCL backend, there would be hang issue in some cases. Please set `--mmp 0`.
+
 ## Examples
 
 ### Text generation with different models

examples/llava/CMakeLists.txt

@@ -0,0 +1,81 @@
+# 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 ${CMAKE_THREAD_LIBS_INIT})
+    install(TARGETS mtmd_shared LIBRARY)
+endif()
+
+if (NOT MSVC)
+    target_compile_options(llava PRIVATE -Wno-cast-qual) # stb_image.h
+    target_compile_options(mtmd PRIVATE -Wno-cast-qual) # stb_image.h
+endif()
+
+if(TARGET BUILD_INFO)
+    add_dependencies(llava BUILD_INFO)
+    add_dependencies(mtmd BUILD_INFO)
+endif()
+
+add_executable(llama-llava-cli    deprecation-warning.cpp)
+add_executable(llama-gemma3-cli   deprecation-warning.cpp)
+add_executable(llama-minicpmv-cli deprecation-warning.cpp)
+add_executable(llama-qwen2vl-cli  deprecation-warning.cpp)
+
+set(TARGET llama-mtmd-cli)
+add_executable(${TARGET} mtmd-cli.cpp)
+set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-mtmd-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

@@ -0,0 +1,44 @@
+# 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/README.md

@@ -16,7 +16,25 @@ The naming and structure related to multimodal support have evolved, which might
 
 ## Pre-quantized models
 
-See the list of pre-quantized model [here](../../docs/multimodal.md)
+These are ready-to-use models, most of them come with `Q4_K_M` quantization by default:
+
+```sh
+# Gemma 3
+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
+
+# SmolVLM
+llama-mtmd-cli -hf ggml-org/SmolVLM-Instruct-GGUF
+llama-mtmd-cli -hf ggml-org/SmolVLM-256M-Instruct-GGUF
+llama-mtmd-cli -hf ggml-org/SmolVLM-500M-Instruct-GGUF
+llama-mtmd-cli -hf ggml-org/SmolVLM2-2.2B-Instruct-GGUF
+llama-mtmd-cli -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF
+llama-mtmd-cli -hf ggml-org/SmolVLM2-500M-Video-Instruct-GGUF
+
+# Pixtral 12B
+llama-mtmd-cli -hf ggml-org/pixtral-12b-GGUF
+```
 
 ## How it works and what is `mmproj`?
 
@@ -39,20 +57,7 @@ Built upon `clip.cpp` (similar to `llava.cpp`), `libmtmd` offers several advanta
 
 ## How to obtain `mmproj`
 
-Multimodal projector (`mmproj`) files are specific to each model architecture.
-
-For the following models, you can use `convert_hf_to_gguf.py` with `--mmproj` flag to get the `mmproj` file:
-- [Gemma 3](https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d) ; See the guide [here](../../docs/multimodal/gemma3.md) - Note: 1B variant does not have vision support
-- SmolVLM (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB))
-- SmolVLM2 (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB))
-- [Pixtral 12B](https://huggingface.co/mistral-community/pixtral-12b) - only works with `transformers`-compatible checkpoint
-- Qwen 2 VL and Qwen 2.5 VL (from [Qwen](https://huggingface.co/Qwen))
-- [Mistral Small 3.1 24B](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503)
-- InternVL 2.5 and InternVL 3 from [OpenGVLab](https://huggingface.co/OpenGVLab) (note: we don't support conversion of `InternVL3-*-hf` model, only non-HF version is supported ; `InternLM2Model` **text** model is not supported)
-
-For older models, please refer to the relevant guide for instructions on how to obtain or create them:
-
-NOTE: conversion scripts are located under `tools/mtmd/legacy-models`
+Multimodal projector (`mmproj`) files are specific to each model architecture. Please refer to the relevant guide for instructions on how to obtain or create them:
 
 - [LLaVA](../../docs/multimodal/llava.md)
 - [MobileVLM](../../docs/multimodal/MobileVLM.md)
@@ -61,3 +66,10 @@ NOTE: conversion scripts are located under `tools/mtmd/legacy-models`
 - [MiniCPM-V 2.6](../../docs/multimodal/minicpmv2.6.md)
 - [MiniCPM-o 2.6](../../docs/multimodal/minicpmo2.6.md)
 - [IBM Granite Vision](../../docs/multimodal/granitevision.md)
+- [Google Gemma 3](../../docs/multimodal/gemma3.md)
+
+For the following models, you can use `convert_hf_to_gguf.py`with `--mmproj` flag to get the `mmproj` file:
+- [Gemma 3](https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d) - Note: 1B variant does not have vision support
+- SmolVLM (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB))
+- SmolVLM2 (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB))
+- [Pixtral 12B](https://huggingface.co/mistral-community/pixtral-12b) - only works with `transformers`-compatible checkpoint

examples/llava/android/adb_run.sh

@@ -0,0 +1,53 @@
+#!/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-mtmd-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

@@ -0,0 +1,8 @@
+#!/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-impl.h

@@ -4,7 +4,6 @@
 
 #include <climits>
 #include <cstdarg>
-#include <cinttypes>
 #include <string>
 #include <map>
 #include <sstream>
@@ -16,38 +15,31 @@
 #define KEY_FTYPE               "general.file_type"
 #define KEY_NAME                "general.name"
 #define KEY_DESCRIPTION         "general.description"
-#define KEY_PROJ_TYPE           "clip.projector_type"
-#define KEY_HAS_AUDIO_ENC       "clip.has_audio_encoder"
-#define KEY_HAS_VISION_ENC      "clip.has_vision_encoder"
+#define KEY_MINICPMV_VERSION    "clip.minicpmv_version"
 #define KEY_USE_GELU            "clip.use_gelu"
 #define KEY_USE_SILU            "clip.use_silu"
-
-#define KEY_N_EMBD              "clip.%s.embedding_length"
-#define KEY_N_FF                "clip.%s.feed_forward_length"
-#define KEY_N_BLOCK             "clip.%s.block_count"
-#define KEY_PROJ_DIM            "clip.%s.projection_dim"
-#define KEY_N_HEAD              "clip.%s.attention.head_count"
-#define KEY_LAYER_NORM_EPS      "clip.%s.attention.layer_norm_epsilon"
-
-// vision-specific
+#define KEY_N_EMBD              "clip.vision.embedding_length"
+#define KEY_N_FF                "clip.vision.feed_forward_length"
+#define KEY_N_BLOCK             "clip.vision.block_count"
+#define KEY_N_HEAD              "clip.vision.attention.head_count"
+#define KEY_LAYER_NORM_EPS      "clip.vision.attention.layer_norm_epsilon"
+#define KEY_PROJ_DIM            "clip.vision.projection_dim"
 #define KEY_IMAGE_SIZE          "clip.vision.image_size"
 #define KEY_PATCH_SIZE          "clip.vision.patch_size"
 #define KEY_IMAGE_MEAN          "clip.vision.image_mean"
 #define KEY_IMAGE_STD           "clip.vision.image_std"
 #define KEY_FEATURE_LAYER       "clip.vision.feature_layer"
 #define KEY_PROJ_SCALE_FACTOR   "clip.vision.projector.scale_factor"
-#define KEY_SPATIAL_MERGE_SIZE  "clip.vision.spatial_merge_size"
+#define KEY_PROJ_TYPE           "clip.projector_type"
+
+#define KEY_USE_GLU_MLP         "clip.use_glu_mlp"  // for qwen2.5vl
+#define KEY_USE_RMS_NORM        "clip.use_rms_norm" // for qwen2.5vl
 
 #define KEY_MM_PATCH_MERGE_TYPE   "clip.vision.mm_patch_merge_type"
 #define KEY_IMAGE_GRID_PINPOINTS  "clip.vision.image_grid_pinpoints"
 #define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution"
 #define KEY_WIN_ATTN_PATTERN      "clip.vision.n_wa_pattern"
 #define KEY_ATTN_WINDOW_SIZE      "clip.vision.window_size"
-#define KEY_MINICPMV_VERSION      "clip.minicpmv_version"
-
-// audio-specific
-#define KEY_A_NUM_MEL_BINS      "clip.audio.num_mel_bins"
-#define KEY_A_PROJ_STACK_FACTOR "clip.audio.projector.stack_factor"
 
 
 //
@@ -63,16 +55,12 @@
 #define TN_ATTN_Q          "%s.blk.%d.attn_q.%s"
 #define TN_ATTN_V          "%s.blk.%d.attn_v.%s"
 #define TN_ATTN_OUTPUT     "%s.blk.%d.attn_out.%s"
-#define TN_ATTN_K_NORM     "%s.blk.%d.attn_k_norm.%s"
-#define TN_ATTN_Q_NORM     "%s.blk.%d.attn_q_norm.%s"
 #define TN_FFN_DOWN        "%s.blk.%d.ffn_down.%s"
 #define TN_FFN_GATE        "%s.blk.%d.ffn_gate.%s"
 #define TN_FFN_UP          "%s.blk.%d.ffn_up.%s"
 #define TN_FFN_GATE        "%s.blk.%d.ffn_gate.%s"
-#define TN_LN_1            "%s.blk.%d.ln1.%s" // layer norm
-#define TN_LN_2            "%s.blk.%d.ln2.%s" // layer norm
-#define TN_LS_1            "%s.blk.%d.ls1.%s" // layer scale
-#define TN_LS_2            "%s.blk.%d.ls2.%s" // layer scale
+#define TN_LN_1            "%s.blk.%d.ln1.%s"
+#define TN_LN_2            "%s.blk.%d.ln2.%s"
 #define TN_LN_PRE          "%s.pre_ln.%s"
 #define TN_LN_POST         "%s.post_ln.%s"
 #define TN_LLAVA_PROJ      "mm.%d.%s"
@@ -80,14 +68,10 @@
 #define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s"
 #define TN_MVLM_PROJ_PEG   "mm.model.peg.%d.%s"
 #define TN_IMAGE_NEWLINE   "model.image_newline"
-#define TN_MM_INP_NORM     "mm.input_norm.weight"
 #define TN_MM_INP_PROJ     "mm.input_projection.weight" // gemma3
 #define TN_MM_SOFT_EMB_N   "mm.soft_emb_norm.weight"    // gemma3
 #define TN_MM_PROJECTOR    "mm.model.fc.weight"         // idefics3
-#define TN_MM_PATCH_MERGER "mm.patch_merger.weight"     // mistral small 3.1
 #define TN_TOK_IMG_BREAK   "v.token_embd.img_break"     // pixtral
-#define TN_TOK_GLM_BOI     "adapter.boi"                // glm-edge (these embeddings are not in text model)
-#define TN_TOK_GLM_EOI     "adapter.eoi"                // glm-edge (these embeddings are not in text model)
 
 // mimicpmv
 #define TN_MINICPMV_POS_EMBD_K "resampler.pos_embed_k"
@@ -104,16 +88,6 @@
 #define TN_GLM_ADAPTER_GATE     "adapter.linear.gate.%s"
 #define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s"
 
-// ultravox
-#define TN_CONV1D       "a.conv1d.%d.%s"
-#define TN_MM_AUDIO_MLP "mm.a.mlp.%d.%s"
-#define TN_MM_AUDIO_FC  "mm.a.fc.%s" // fully connected layer
-#define TN_MM_NORM_PRE  "mm.a.norm_pre.%s"
-#define TN_MM_NORM_MID  "mm.a.norm_mid.%s"
-
-// align x to upper multiple of n
-#define CLIP_ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n))
-
 enum projector_type {
     PROJECTOR_TYPE_MLP,
     PROJECTOR_TYPE_MLP_NORM,
@@ -126,11 +100,6 @@ enum projector_type {
     PROJECTOR_TYPE_IDEFICS3,
     PROJECTOR_TYPE_PIXTRAL,
     PROJECTOR_TYPE_QWEN25VL,
-    PROJECTOR_TYPE_ULTRAVOX,
-    PROJECTOR_TYPE_INTERNVL,
-    PROJECTOR_TYPE_LLAMA4,
-    PROJECTOR_TYPE_QWEN2A,
-    PROJECTOR_TYPE_QWEN25O, // will be replaced by QWEN2A or QWEN25VL depending on clip_ctx
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -145,11 +114,6 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_GEMMA3,    "gemma3"},
     { PROJECTOR_TYPE_IDEFICS3,  "idefics3"},
     { PROJECTOR_TYPE_PIXTRAL,   "pixtral"},
-    { PROJECTOR_TYPE_ULTRAVOX,  "ultravox"},
-    { PROJECTOR_TYPE_INTERNVL,  "internvl"},
-    { PROJECTOR_TYPE_LLAMA4,    "llama4"},
-    { PROJECTOR_TYPE_QWEN2A,    "qwen2a"},
-    { PROJECTOR_TYPE_QWEN25O,   "qwen2.5o"},
 };
 
 static projector_type clip_projector_type_from_string(const std::string & str) {
@@ -169,10 +133,8 @@ struct clip_image_u8 {
     std::vector<uint8_t> buf;
 };
 
-// For images, buf.size() == nx*ny*3
-//     Memory layout: RGBRGBRGB...
-// For audio, only one channel is used, buf.size() == nx*ny
-//     nx will be n_frames and ny will be n_mel
+// RGB float32 image (NHWC)
+// Memory layout: RGBRGBRGB...
 struct clip_image_f32 {
     int nx;
     int ny;
@@ -266,26 +228,6 @@ struct clip_image_u8_batch {
 
 struct clip_image_f32_batch {
     std::vector<clip_image_f32_ptr> entries;
-    bool is_audio = false;
-
-    // for llava-uhd style models, we need to know the grid size
-    // note: entries.size() == grid_x * grid_y + 1 (one overview image)
-    int grid_x = 0;
-    int grid_y = 0;
-
-    clip_image_f32_batch clone() const {
-        clip_image_f32_batch new_batch{
-            /* entries  */ {},
-            /* is_audio */ is_audio,
-            /* grid_x   */ grid_x,
-            /* grid_y   */ grid_y,
-        };
-        new_batch.entries.reserve(entries.size());
-        for (const auto & entry : entries) {
-            new_batch.entries.emplace_back(new clip_image_f32(*entry));
-        }
-        return new_batch;
-    }
 };
 
 //
@@ -396,70 +338,6 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
     }
 }
 
-//
-// debugging
-//
-
-static void print_tensor_shape(ggml_tensor * t) {
-    printf("%s.shape = [", t->name);
-    for (int i = 0; i < ggml_n_dims(t); ++i) {
-        printf("%" PRId64, t->ne[i]);
-        if (i < ggml_n_dims(t) - 1) {
-            printf(", ");
-        }
-    }
-    printf("]\n");
-}
-
-static void print_tensor_data(ggml_tensor * t, uint8_t * data, int64_t n) {
-    ggml_type type = t->type;
-    int64_t * ne = t->ne;
-    size_t * nb = t->nb;
-    for (int64_t i3 = 0; i3 < ne[3]; i3++) {
-        printf("%s.data: [\n", t->name);
-        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
-            if (i2 == n && ne[2] > 2*n) {
-                printf("     ..., \n");
-                i2 = ne[2] - n;
-            }
-            printf("     [\n");
-            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
-                if (i1 == n && ne[1] > 2*n) {
-                    printf("      ..., \n");
-                    i1 = ne[1] - n;
-                }
-                printf("      [");
-                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
-                    if (i0 == n && ne[0] > 2*n) {
-                        printf("..., ");
-                        i0 = ne[0] - n;
-                    }
-                    size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
-                    float v;
-                    if (type == GGML_TYPE_F16) {
-                        v = ggml_fp16_to_fp32(*(ggml_fp16_t *) &data[i]);
-                    } else if (type == GGML_TYPE_F32) {
-                        v = *(float *) &data[i];
-                    } else if (type == GGML_TYPE_I32) {
-                        v = (float) *(int32_t *) &data[i];
-                    } else if (type == GGML_TYPE_I16) {
-                        v = (float) *(int16_t *) &data[i];
-                    } else if (type == GGML_TYPE_I8) {
-                        v = (float) *(int8_t *) &data[i];
-                    } else {
-                        GGML_ABORT("fatal error");
-                    }
-                    printf("%8.4f", v);
-                    if (i0 < ne[0] - 1) printf(", ");
-                }
-                printf("],\n");
-            }
-            printf("     ],\n");
-        }
-        printf("    ]\n");
-    }
-}
-
 //
 // API used internally with mtmd
 //

examples/llava/clip-quantize-cli.cpp

@@ -0,0 +1,59 @@
+#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;
+}