server : fill usage info in embeddings and rerank responses (#10852)

* server : fill usage info in embeddings response

* server : fill usage info in reranking response

.devops/full.Dockerfile

@@ -3,23 +3,36 @@ ARG UBUNTU_VERSION=22.04
 FROM ubuntu:$UBUNTU_VERSION AS build
 
 RUN apt-get update && \
-    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1
-
-COPY requirements.txt   requirements.txt
-COPY requirements       requirements
-
-RUN pip install --upgrade pip setuptools wheel \
-    && pip install -r requirements.txt
+    apt-get install -y build-essential git cmake libcurl4-openssl-dev
 
 WORKDIR /app
 
 COPY . .
 
-ENV LLAMA_CURL=1
+RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
+    cmake --build build -j $(nproc) && \
+    mkdir -p /app/lib && \
+    find build -name "*.so" -exec cp {} /app/lib/ \;
 
+FROM ubuntu:$UBUNTU_VERSION as runtime
 
-RUN make -j$(nproc)
+WORKDIR /app
+
+RUN apt-get update && \
+    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1
+
+COPY requirements.txt   /app/requirements.txt
+COPY requirements       /app/requirements
+COPY .devops/tools.sh   /app/tools.sh
+
+RUN pip install --upgrade pip setuptools wheel && \
+    pip install -r /app/requirements.txt
+
+COPY --from=build /app/build/bin/ /app/
+COPY --from=build /app/lib/ /app/
+COPY --from=build /app/convert_hf_to_gguf.py /app/
+COPY --from=build /app/gguf-py /app/gguf-py
 
 ENV LC_ALL=C.utf8
 
-ENTRYPOINT ["/app/.devops/tools.sh"]
+ENTRYPOINT ["/app/tools.sh"]

.devops/llama-cli.Dockerfile

@@ -3,21 +3,27 @@ ARG UBUNTU_VERSION=22.04
 FROM ubuntu:$UBUNTU_VERSION AS build
 
 RUN apt-get update && \
-    apt-get install -y build-essential git
+    apt-get install -y build-essential git cmake libcurl4-openssl-dev
 
 WORKDIR /app
 
 COPY . .
 
-RUN make -j$(nproc) llama-cli
+RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
+    cmake --build build -j $(nproc) && \
+    mkdir -p /app/lib && \
+    find build -name "*.so" -exec cp {} /app/lib/ \;
 
 FROM ubuntu:$UBUNTU_VERSION AS runtime
 
-RUN apt-get update && \
-    apt-get install -y libgomp1
+WORKDIR /app
 
-COPY --from=build /app/llama-cli /llama-cli
+RUN apt-get update && \
+    apt-get install -y libcurl4-openssl-dev libgomp1 curl
+
+COPY --from=build /app/build/bin/llama-cli /app/
+COPY --from=build /app/lib/ /app/
 
 ENV LC_ALL=C.utf8
 
-ENTRYPOINT [ "/llama-cli" ]
+ENTRYPOINT [ "/app/llama-cli" ]

.devops/llama-server.Dockerfile

@@ -9,28 +9,20 @@ WORKDIR /app
 
 COPY . .
 
-
-RUN \
-    # Build multiple versions of the CPU backend
-    scripts/build-cpu.sh avx         -DGGML_AVX=ON -DGGML_AVX2=OFF && \
-    scripts/build-cpu.sh avx2        -DGGML_AVX=ON -DGGML_AVX2=ON && \
-    scripts/build-cpu.sh avx512      -DGGML_AVX=ON -DGGML_AVX2=ON -DGGML_AVX512=ON && \
-    scripts/build-cpu.sh amx         -DGGML_AVX=ON -DGGML_AVX2=ON -DGGML_AVX512=ON -DGGML_AVX_VNNI=ON -DGGML_AVX512_VNNI=ON -DGGML_AMX_TILE=ON -DGGML_AMX_INT8=ON && \
-    # Build llama-server
-    cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
-    cmake --build build --target llama-server -j $(nproc) && \
-    # Copy the built libraries to /app/lib
+RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
+    cmake --build build -j $(nproc) && \
     mkdir -p /app/lib && \
-    mv libggml-cpu* /app/lib/ && \
     find build -name "*.so" -exec cp {} /app/lib/ \;
 
 FROM ubuntu:$UBUNTU_VERSION AS runtime
 
+WORKDIR /app
+
 RUN apt-get update && \
     apt-get install -y libcurl4-openssl-dev libgomp1 curl
 
-COPY --from=build /app/build/bin/llama-server /llama-server
-COPY --from=build /app/lib/ /
+COPY --from=build /app/build/bin/llama-server /app/
+COPY --from=build /app/lib/ /app/
 
 ENV LC_ALL=C.utf8
 # Must be set to 0.0.0.0 so it can listen to requests from host machine
@@ -38,4 +30,4 @@ ENV LLAMA_ARG_HOST=0.0.0.0
 
 HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
 
-ENTRYPOINT [ "/llama-server" ]
+ENTRYPOINT [ "/app/llama-server" ]

.devops/nix/package.nix

@@ -31,6 +31,7 @@
   # Increases the runtime closure size by ~700M
   useMpi ? false,
   useRocm ? config.rocmSupport,
+  rocmGpuTargets ? builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets,
   enableCurl ? true,
   useVulkan ? false,
   llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
@@ -188,7 +189,7 @@ effectiveStdenv.mkDerivation (finalAttrs: {
     ]
     ++ optionals useRocm [
       (cmakeFeature "CMAKE_HIP_COMPILER" "${rocmPackages.llvm.clang}/bin/clang")
-      (cmakeFeature "CMAKE_HIP_ARCHITECTURES" (builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets))
+      (cmakeFeature "CMAKE_HIP_ARCHITECTURES" rocmGpuTargets)
     ]
     ++ optionals useMetalKit [
       (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1")

.devops/tools.sh

@@ -8,11 +8,11 @@ arg1="$1"
 shift
 
 if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then
-    python3 ./convert_hf_to_gguf.py "$@"
+    exec python3 ./convert_hf_to_gguf.py "$@"
 elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
-    ./llama-quantize "$@"
+    exec ./llama-quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
-    ./llama-cli "$@"
+    exec ./llama-cli "$@"
 elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
     echo "Converting PTH to GGML..."
     for i in `ls $1/$2/ggml-model-f16.bin*`; do
@@ -20,11 +20,11 @@ elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
             echo "Skip model quantization, it already exists: ${i/f16/q4_0}"
         else
             echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..."
-            ./llama-quantize "$i" "${i/f16/q4_0}" q4_0
+            exec ./llama-quantize "$i" "${i/f16/q4_0}" q4_0
         fi
     done
 elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then
-    ./llama-server "$@"
+    exec ./llama-server "$@"
 else
     echo "Unknown command: $arg1"
     echo "Available commands: "

.github/workflows/build.yml

@@ -160,66 +160,6 @@ jobs:
           path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip
           name: llama-bin-macos-x64.zip
 
-  ubuntu-focal-make:
-    runs-on: ubuntu-20.04
-    env:
-      LLAMA_NODE_AVAILABLE: true
-      LLAMA_PYTHON_AVAILABLE: true
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential gcc-8
-
-      - uses: actions/setup-node@v4
-        with:
-          node-version: "20"
-
-      - uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-
-      - name: Build
-        id: make_build
-        env:
-            LLAMA_FATAL_WARNINGS: 1
-        run: |
-          CC=gcc-8 make -j $(nproc)
-
-      - name: Test
-        id: make_test
-        run: |
-          CC=gcc-8 make tests -j $(nproc)
-          make test -j $(nproc)
-
-  ubuntu-focal-make-curl:
-    runs-on: ubuntu-20.04
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential gcc-8 libcurl4-openssl-dev
-
-      - name: Build
-        id: make_build
-        env:
-          LLAMA_FATAL_WARNINGS: 1
-          LLAMA_CURL: 1
-        run: |
-          CC=gcc-8 make -j $(nproc)
-
   ubuntu-latest-cmake:
     runs-on: ubuntu-latest
 
@@ -377,7 +317,7 @@ jobs:
           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 vulkan-sdk
+          sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk
 
       - name: Build
         id: cmake_build
@@ -387,6 +327,12 @@ jobs:
           cmake -DGGML_VULKAN=ON ..
           cmake --build . --config Release -j $(nproc)
 
+      - name: Test
+        id: cmake_test
+        run: |
+          cd build
+          ctest -L main --verbose --timeout 900
+
   ubuntu-22-cmake-hip:
     runs-on: ubuntu-22.04
     container: rocm/dev-ubuntu-22.04:6.0.2
@@ -517,36 +463,6 @@ jobs:
           cmake -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON ..
           cmake --build . --config Release -j $(nproc)
 
-  # TODO: build with GGML_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know
-  #       how to debug it.
-  #       ref: https://github.com/ggerganov/llama.cpp/actions/runs/7131777249/job/19420981052#step:5:1124
-  macOS-latest-make:
-    runs-on: macos-latest
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-
-      - name: Dependencies
-        id: depends
-        continue-on-error: true
-        run: |
-          brew update
-
-      - name: Build
-        id: make_build
-        env:
-            LLAMA_FATAL_WARNINGS: 1
-        run: |
-          GGML_NO_METAL=1 make -j $(sysctl -n hw.logicalcpu)
-
-      - name: Test
-        id: make_test
-        run: |
-          GGML_NO_METAL=1 make tests -j $(sysctl -n hw.logicalcpu)
-          GGML_NO_METAL=1 make test  -j $(sysctl -n hw.logicalcpu)
-
   # TODO: build with GGML_METAL=OFF because test-backend-ops fail on "Apple Paravirtual device" and I don't know
   #       how to debug it.
   #       ref: https://github.com/ggerganov/llama.cpp/actions/runs/7132125951/job/19422043567?pr=4359#step:5:6584
@@ -660,15 +576,26 @@ jobs:
         run: |
           brew update
 
+      - name: Build llama.cpp with CMake
+        id: cmake_build
+        run: |
+          sysctl -a
+          mkdir build
+          cd build
+          cmake -G Xcode .. \
+            -DGGML_METAL_USE_BF16=ON \
+            -DGGML_METAL_EMBED_LIBRARY=ON \
+            -DLLAMA_BUILD_EXAMPLES=OFF \
+            -DLLAMA_BUILD_TESTS=OFF \
+            -DLLAMA_BUILD_SERVER=OFF \
+            -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64"
+          cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
+          sudo cmake --install . --config Release
+
       - name: xcodebuild for swift package
         id: xcodebuild
         run: |
-          xcodebuild -scheme llama -destination "${{ matrix.destination }}"
-
-      - name: Build Swift Example
-        id: make_build_swift_example
-        run: |
-            make swift
+          xcodebuild -scheme llama-Package -destination "${{ matrix.destination }}"
 
   windows-msys2:
     runs-on: windows-latest
@@ -695,21 +622,6 @@ jobs:
             mingw-w64-${{matrix.env}}-cmake
             mingw-w64-${{matrix.env}}-openblas
 
-      - name: Build using make
-        shell: msys2 {0}
-        run: |
-            make -j $(nproc)
-
-      - name: Clean after building using make
-        shell: msys2 {0}
-        run: |
-            make clean
-
-      - name: Build using make w/ OpenBLAS
-        shell: msys2 {0}
-        run: |
-            make GGML_OPENBLAS=1 -j $(nproc)
-
       - name: Build using CMake
         shell: msys2 {0}
         run: |
@@ -756,6 +668,8 @@ jobs:
             defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
           - build: 'msvc-arm64'
             defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DGGML_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
+          - build: 'llvm-arm64-opencl-adreno'
+            defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" -DGGML_OPENCL=ON -DGGML_OPENCL_USE_ADRENO_KERNELS=ON'
 
     steps:
       - name: Clone
@@ -797,6 +711,28 @@ jobs:
         run: |
           choco install ninja
 
+      - name: Install OpenCL Headers and Libs
+        id: install_opencl
+        if: ${{ matrix.build == 'llvm-arm64-opencl-adreno' }}
+        run: |
+          git clone https://github.com/KhronosGroup/OpenCL-Headers
+          cd OpenCL-Headers
+          mkdir build && cd build
+          cmake .. `
+            -DBUILD_TESTING=OFF `
+            -DOPENCL_HEADERS_BUILD_TESTING=OFF `
+            -DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF `
+            -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
+          cmake --build . --target install
+          git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader
+          cd OpenCL-ICD-Loader
+          mkdir build-arm64-release && cd build-arm64-release
+          cmake .. `
+            -A arm64 `
+            -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/opencl-arm64-release" `
+            -DCMAKE_INSTALL_PREFIX="$env:RUNNER_TEMP/opencl-arm64-release"
+          cmake --build . --target install --config release
+
       - name: Build
         id: cmake_build
         run: |
@@ -826,7 +762,7 @@ jobs:
       - name: Test
         id: cmake_test
         # not all machines have native AVX-512
-        if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }}
+        if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'llvm-arm64-opencl-adreno' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }}
         run: |
           cd build
           ctest -L main -C Release --verbose --timeout 900
@@ -1207,6 +1143,29 @@ jobs:
       - name: Checkout code
         uses: actions/checkout@v4
 
+      - name: Build
+        id: cmake_build
+        run: |
+          sysctl -a
+          mkdir build
+          cd build
+          cmake -G Xcode .. \
+            -DGGML_METAL_USE_BF16=ON \
+            -DGGML_METAL_EMBED_LIBRARY=ON \
+            -DLLAMA_BUILD_EXAMPLES=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 . --config Release -j $(sysctl -n hw.logicalcpu) -- CODE_SIGNING_ALLOWED=NO
+          sudo cmake --install . --config Release
+
+      - name: xcodebuild for swift package
+        id: xcodebuild
+        run: |
+          xcodebuild -scheme llama-Package -destination 'generic/platform=iOS'
+
       - 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' build
 
@@ -1234,32 +1193,13 @@ jobs:
 
           ./gradlew build --no-daemon
 
-#  freeBSD-latest:
-#    runs-on: macos-12
-#    steps:
-#    - name: Clone
-#      uses: actions/checkout@v4
-#
-#    - name: Build
-#      uses: cross-platform-actions/action@v0.19.0
-#      with:
-#        operating_system: freebsd
-#        version: '13.2'
-#        hypervisor: 'qemu'
-#        run: |
-#            sudo pkg update
-#            sudo pkg install -y gmake automake autoconf pkgconf llvm15 openblas
-#            gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j `sysctl -n hw.ncpu`
-
   release:
     if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
 
     runs-on: ubuntu-latest
 
     needs:
-      - ubuntu-focal-make
       - ubuntu-latest-cmake
-      - macOS-latest-make
       - macOS-latest-cmake
       - windows-latest-cmake
       - windows-2019-cmake-cuda

.github/workflows/server.yml

@@ -76,20 +76,26 @@ jobs:
         run: |
           pip install -r examples/server/tests/requirements.txt
 
-      - name: Verify server deps
-        id: verify_server_deps
+      # Setup nodejs (to be used for verifying bundled index.html)
+      - uses: actions/setup-node@v4
+        with:
+          node-version: '22.11.0'
+
+      - name: Verify bundled index.html
+        id: verify_server_index_html
         run: |
           git config --global --add safe.directory $(realpath .)
-          cd examples/server
-          git ls-files --others --modified
+          cd examples/server/webui
           git status
-          ./deps.sh
+          npm ci
+          npm run build
           git status
-          not_ignored_files="$(git ls-files --others --modified)"
-          echo "Modified files: ${not_ignored_files}"
-          if [ -n "${not_ignored_files}" ]; then
-            echo "Repository is dirty or server deps are not built as expected"
-            echo "${not_ignored_files}"
+          modified_files="$(git status -s)"
+          echo "Modified files: ${modified_files}"
+          if [ -n "${modified_files}" ]; then
+            echo "Repository is dirty or server/webui is not built as expected"
+            echo "Hint: You may need to follow Web UI build guide in server/README.md"
+            echo "${modified_files}"
             exit 1
           fi
 

.gitignore

@@ -104,6 +104,10 @@ examples/server/*.mjs.hpp
 !examples/sycl/*.bat
 !examples/sycl/*.sh
 
+# Server Web UI temporary files
+node_modules
+examples/server/webui/dist
+
 # Python
 
 /.venv

CMakeLists.txt

@@ -46,11 +46,9 @@ if (WIN32)
     add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
 endif()
 
-if ("${CMAKE_CXX_COMPILER_ID}" STREQUAL "MSVC")
-    add_compile_options("$<$<COMPILE_LANGUAGE:C>:/source-charset:utf-8>")
-    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/source-charset:utf-8>")
-    add_compile_options("$<$<COMPILE_LANGUAGE:C>:/execution-charset:utf-8>")
-    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/execution-charset:utf-8>")
+if (MSVC)
+    add_compile_options("$<$<COMPILE_LANGUAGE:C>:/utf-8>")
+    add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/utf-8>")
 endif()
 
 #

CMakePresets.json

@@ -31,6 +31,13 @@
     { "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16":    "ON" } },
     { "name": "vulkan",   "hidden": true, "cacheVariables": { "GGML_VULKAN":      "ON" } },
 
+    {
+        "name": "x64-windows-llvm", "hidden": true,
+        "cacheVariables": {
+            "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/x64-windows-llvm.cmake"
+        }
+    },
+
     {
         "name": "arm64-windows-msvc", "hidden": true,
         "architecture": { "value": "arm64",    "strategy": "external" },
@@ -70,6 +77,11 @@
     { "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" ] },
+    { "name": "x64-windows-llvm+static-release", "inherits": [ "base", "x64-windows-llvm", "reldbg", "static" ] },
+
     { "name": "x64-windows-msvc-debug", "inherits": [ "base", "debug" ] },
     { "name": "x64-windows-msvc-release", "inherits": [ "base", "reldbg" ] },
     { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] },

CODEOWNERS

@@ -1,3 +1,5 @@
 # collaborators can optionally add themselves here to indicate their availability for reviewing related PRs
 
-ci/ @ggerganov
+/ci/ @ggerganov
+/.devops/ @ngxson
+/examples/server/ @ngxson

Makefile

@@ -1,3 +1,7 @@
+ifndef LLAMA_MAKEFILE
+$(error The Makefile build is deprecated. Use the CMake build instead. For more details, see https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+endif
+
 # Define the default target now so that it is always the first target
 BUILD_TARGETS = \
 	libllava.a \
@@ -18,6 +22,7 @@ BUILD_TARGETS = \
 	llama-infill \
 	llama-llava-cli \
 	llama-minicpmv-cli\
+	llama-qwen2vl-cli\
 	llama-lookahead \
 	llama-lookup \
 	llama-lookup-create \
@@ -441,6 +446,10 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
 	MK_CFLAGS     += -march=native -mtune=native
 	HOST_CXXFLAGS += -march=native -mtune=native
 
+	# Usage AMX build test
+	#MK_CFLAGS     += -march=graniterapids -mtune=graniterapids
+	#HOST_CXXFLAGS += -march=graniterapids -mtune=graniterapids
+
 	# Usage AVX-only
 	#MK_CFLAGS   += -mfma -mf16c -mavx
 	#MK_CXXFLAGS += -mfma -mf16c -mavx
@@ -944,7 +953,6 @@ DIR_COMMON = common
 
 OBJ_GGML = \
 	$(DIR_GGML)/src/ggml.o \
-	$(DIR_GGML)/src/ggml-aarch64.o \
 	$(DIR_GGML)/src/ggml-alloc.o \
 	$(DIR_GGML)/src/ggml-backend.o \
 	$(DIR_GGML)/src/ggml-backend-reg.o \
@@ -952,9 +960,11 @@ OBJ_GGML = \
 	$(DIR_GGML)/src/ggml-quants.o \
 	$(DIR_GGML)/src/ggml-threading.o \
 	$(DIR_GGML)/src/ggml-cpu/ggml-cpu.o \
-	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o \
+	$(DIR_GGML)/src/ggml-cpu/ggml-cpu_cpp.o \
 	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-aarch64.o \
+	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-hbm.o \
 	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-quants.o \
+	$(DIR_GGML)/src/ggml-cpu/ggml-cpu-traits.o \
 	$(OBJ_GGML_EXT)
 
 OBJ_LLAMA = \
@@ -1094,17 +1104,10 @@ DEP_FILES = $(OBJ_GGML:.o=.d) $(OBJ_LLAMA:.o=.d) $(OBJ_COMMON:.o=.d)
 # Default target
 all: $(BUILD_TARGETS)
 
+# force c++ build for source file that have same name as c file
 # Note: need this exception because `ggml-cpu.c` and `ggml-cpu.cpp` both produce the same obj/dep files
-#       g++ -M -I ./ggml/include/ -I ./ggml/src ggml/src/ggml-cpu/ggml-cpu.cpp | grep ggml
-$(DIR_GGML)/src/ggml-cpu/ggml-cpu-cpp.o: \
-	ggml/src/ggml-cpu/ggml-cpu.cpp \
-	ggml/include/ggml-backend.h \
-	ggml/include/ggml.h \
-	ggml/include/ggml-alloc.h \
-	ggml/src/ggml-backend-impl.h \
-	ggml/include/ggml-cpu.h \
-	ggml/src/ggml-impl.h
-	$(CXX) $(CXXFLAGS)   -c $< -o $@
+$(DIR_GGML)/%_cpp.o: $(DIR_GGML)/%.cpp
+	$(CXX) $(CXXFLAGS) -MMD -c $< -o $@
 
 # Rules for building object files
 $(DIR_GGML)/%.o: $(DIR_GGML)/%.c
@@ -1141,8 +1144,15 @@ $(LIB_COMMON_S): $(OBJ_COMMON)
 # Include dependency files
 -include $(DEP_FILES)
 
+# Clean generated server assets
+clean-server-assets:
+	find examples/server -type f -name "*.js.hpp"   -delete
+	find examples/server -type f -name "*.mjs.hpp"  -delete
+	find examples/server -type f -name "*.css.hpp"  -delete
+	find examples/server -type f -name "*.html.hpp" -delete
+
 # Clean rule
-clean:
+clean: clean-server-assets
 	rm -vrf $(BUILD_TARGETS) $(TEST_TARGETS)
 	rm -rvf *.a *.dll *.so *.dot
 	find ggml src common tests examples pocs -type f -name "*.o" -delete
@@ -1350,20 +1360,14 @@ llama-server: \
 	examples/server/utils.hpp \
 	examples/server/httplib.h \
 	examples/server/index.html.hpp \
-	examples/server/completion.js.hpp \
 	examples/server/loading.html.hpp \
-	examples/server/deps_daisyui.min.css.hpp \
-	examples/server/deps_markdown-it.js.hpp \
-	examples/server/deps_tailwindcss.js.hpp \
-	examples/server/deps_vue.esm-browser.js.hpp \
 	common/json.hpp \
-	common/stb_image.h \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
 
 # Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`:
-examples/server/%.hpp: examples/server/public/% Makefile
+examples/server/%.hpp: examples/server/public/% FORCE Makefile
 	@( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \
 		echo "unsigned char $${NAME}[] = {" && \
 		cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \
@@ -1401,6 +1405,14 @@ llama-minicpmv-cli: examples/llava/minicpmv-cli.cpp \
 	$(OBJ_ALL)
 	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
 
+llama-qwen2vl-cli: examples/llava/qwen2vl-cli.cpp \
+	examples/llava/llava.cpp \
+	examples/llava/llava.h \
+	examples/llava/clip.cpp \
+	examples/llava/clip.h \
+	$(OBJ_ALL)
+	$(CXX) $(CXXFLAGS) $< $(filter-out %.h $<,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
+
 ifeq ($(UNAME_S),Darwin)
 swift: examples/batched.swift
 	(cd examples/batched.swift; make build)
@@ -1538,7 +1550,7 @@ llama-q8dot: pocs/vdot/q8dot.cpp ggml/src/ggml.o \
 # Deprecated binaries that we want to keep around long enough for people to migrate to the new filenames, then these can be removed.
 #
 # Mark legacy binary targets as .PHONY so that they are always checked.
-.PHONY: main quantize perplexity embedding server
+.PHONY: FORCE main quantize perplexity embedding server
 
 # Define the object file target
 examples/deprecation-warning/deprecation-warning.o: examples/deprecation-warning/deprecation-warning.cpp

Package.swift

@@ -2,59 +2,6 @@
 
 import PackageDescription
 
-var sources = [
-    "src/llama.cpp",
-    "src/llama-vocab.cpp",
-    "src/llama-grammar.cpp",
-    "src/llama-sampling.cpp",
-    "src/unicode.cpp",
-    "src/unicode-data.cpp",
-    "ggml/src/ggml.c",
-    "ggml/src/ggml-aarch64.c",
-    "ggml/src/ggml-alloc.c",
-    "ggml/src/ggml-backend.cpp",
-    "ggml/src/ggml-backend-reg.cpp",
-    "ggml/src/ggml-cpu/ggml-cpu.c",
-    "ggml/src/ggml-cpu/ggml-cpu.cpp",
-    "ggml/src/ggml-cpu/ggml-cpu-aarch64.c",
-    "ggml/src/ggml-cpu/ggml-cpu-quants.c",
-    "ggml/src/ggml-threading.cpp",
-    "ggml/src/ggml-quants.c",
-]
-
-var resources: [Resource] = []
-var linkerSettings: [LinkerSetting] = []
-var cSettings: [CSetting] =  [
-    .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
-    .unsafeFlags(["-fno-objc-arc"]),
-    .headerSearchPath("ggml/src"),
-    .headerSearchPath("ggml/src/ggml-cpu"),
-    // NOTE: NEW_LAPACK will required iOS version 16.4+
-    // We should consider add this in the future when we drop support for iOS 14
-    // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
-    // .define("ACCELERATE_NEW_LAPACK"),
-    // .define("ACCELERATE_LAPACK_ILP64")
-    .define("GGML_USE_CPU"),
-]
-
-
-#if canImport(Darwin)
-sources.append("ggml/src/ggml-common.h")
-sources.append("ggml/src/ggml-metal/ggml-metal.m")
-resources.append(.process("ggml/src/ggml-metal/ggml-metal.metal"))
-linkerSettings.append(.linkedFramework("Accelerate"))
-cSettings.append(
-    contentsOf: [
-        .define("GGML_USE_ACCELERATE"),
-        .define("GGML_USE_METAL"),
-    ]
-)
-#endif
-
-#if os(Linux)
-    cSettings.append(.define("_GNU_SOURCE"))
-#endif
-
 let package = Package(
     name: "llama",
     platforms: [
@@ -67,26 +14,6 @@ let package = Package(
         .library(name: "llama", targets: ["llama"]),
     ],
     targets: [
-        .target(
-            name: "llama",
-            path: ".",
-            exclude: [
-               "build",
-               "cmake",
-               "examples",
-               "scripts",
-               "models",
-               "tests",
-               "CMakeLists.txt",
-               "Makefile",
-               "ggml/src/ggml-metal-embed.metal"
-            ],
-            sources: sources,
-            resources: resources,
-            publicHeadersPath: "spm-headers",
-            cSettings: cSettings,
-            linkerSettings: linkerSettings
-        )
-    ],
-    cxxLanguageStandard: .cxx17
+        .systemLibrary(name: "llama", pkgConfig: "llama"),
+    ]
 )

README.md

@@ -98,6 +98,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
 - [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a)
 - [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM)
+- [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
 
 #### Multimodal
 
@@ -110,6 +111,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
 - [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
 - [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
+- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
 
 </details>
 
@@ -412,7 +414,7 @@ To learn more about model quantization, [read this documentation](examples/quant
 [^1]: [examples/perplexity/README.md](examples/perplexity/README.md)
 [^2]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
 
-## [`llama-bench`](example/bench)
+## [`llama-bench`](examples/llama-bench)
 
 #### Benchmark the performance of the inference for various parameters.
 
@@ -433,6 +435,20 @@ To learn more about model quantization, [read this documentation](examples/quant
 
     </details>
 
+## [`llama-run`](examples/run)
+
+#### A comprehensive example for running `llama.cpp` models. Useful for inferencing. Used with RamaLama [^3].
+
+- <details>
+    <summary>Run a model with a specific prompt (by default it's pulled from Ollama registry)</summary>
+
+    ```bash
+    llama-run granite-code
+    ```
+
+    </details>
+
+[^3]: [https://github.com/containers/ramalama](RamaLama)
 
 ## [`llama-simple`](examples/simple)
 

Sources/llama/llama.h

@@ -0,0 +1,4 @@
+#pragma once
+
+#include <llama.h>
+

Sources/llama/module.modulemap

@@ -0,0 +1,5 @@
+module llama [system] {
+    header "llama.h"
+    link "llama"
+    export *
+}

cmake/llama.pc.in

@@ -6,5 +6,5 @@ includedir=${prefix}/include
 Name: llama
 Description: Port of Facebook's LLaMA model in C/C++
 Version: @PROJECT_VERSION@
-Libs: -L${libdir} -lllama
+Libs: -L${libdir} -lggml  -lggml-base -lllama
 Cflags: -I${includedir}

cmake/x64-windows-llvm.cmake

@@ -0,0 +1,11 @@
+set( CMAKE_SYSTEM_NAME Windows )
+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

@@ -81,7 +81,7 @@ set(LLAMA_COMMON_EXTRA_LIBS build_info)
 # Use curl to download model url
 if (LLAMA_CURL)
     find_package(CURL REQUIRED)
-    add_definitions(-DLLAMA_USE_CURL)
+    target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL)
     include_directories(${CURL_INCLUDE_DIRS})
     find_library(CURL_LIBRARY curl REQUIRED)
     set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY})

common/arg.cpp

@@ -145,6 +145,35 @@ static void common_params_handle_model_default(common_params & params) {
     }
 }
 
+const std::vector<ggml_type> kv_cache_types = {
+    GGML_TYPE_F32,
+    GGML_TYPE_F16,
+    GGML_TYPE_BF16,
+    GGML_TYPE_Q8_0,
+    GGML_TYPE_Q4_0,
+    GGML_TYPE_Q4_1,
+    GGML_TYPE_IQ4_NL,
+    GGML_TYPE_Q5_0,
+    GGML_TYPE_Q5_1,
+};
+
+static ggml_type kv_cache_type_from_str(const std::string & s) {
+    for (const auto & type : kv_cache_types) {
+        if (ggml_type_name(type) == s) {
+            return type;
+        }
+    }
+    throw std::runtime_error("Unsupported cache type: " + s);
+}
+
+static std::string get_all_kv_cache_types() {
+    std::ostringstream msg;
+    for (const auto & type : kv_cache_types) {
+        msg << ggml_type_name(type) << (&type == &kv_cache_types.back() ? "" : ", ");
+    }
+    return msg.str();
+}
+
 //
 // CLI argument parsing functions
 //
@@ -348,6 +377,18 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e
     return true;
 }
 
+static std::string list_builtin_chat_templates() {
+    std::vector<const char *> supported_tmpl;
+    int32_t res = llama_chat_builtin_templates(nullptr, 0);
+    supported_tmpl.resize(res);
+    res = llama_chat_builtin_templates(supported_tmpl.data(), supported_tmpl.size());
+    std::ostringstream msg;
+    for (auto & tmpl : supported_tmpl) {
+        msg << tmpl << (&tmpl == &supported_tmpl.back() ? "" : ", ");
+    }
+    return msg.str();
+}
+
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
     // load dynamic backends
     ggml_backend_load_all();
@@ -579,7 +620,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.ctx_shift = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
     add_opt(common_arg(
         {"--chunks"}, "N",
         string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
@@ -774,7 +815,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.warmup = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--spm-infill"},
         string_format(
@@ -814,13 +855,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.ignore_eos = true;
         }
     ).set_sparam());
-    add_opt(common_arg(
-        {"--penalize-nl"},
-        string_format("penalize newline tokens (default: %s)", params.sampling.penalize_nl ? "true" : "false"),
-        [](common_params & params) {
-            params.sampling.penalize_nl = true;
-        }
-    ).set_sparam());
     add_opt(common_arg(
         {"--temp"}, "N",
         string_format("temperature (default: %.1f)", (double)params.sampling.temp),
@@ -875,6 +909,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--repeat-last-n"}, "N",
         string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n),
         [](common_params & params, int value) {
+            if (value < -1) {
+                throw std::runtime_error(string_format("error: invalid repeat-last-n = %d\n", value));
+            }
             params.sampling.penalty_last_n = value;
             params.sampling.n_prev = std::max(params.sampling.n_prev, params.sampling.penalty_last_n);
         }
@@ -929,6 +966,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--dry-penalty-last-n"}, "N",
         string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n),
         [](common_params & params, int value) {
+            if (value < -1) {
+                throw std::runtime_error(string_format("error: invalid dry-penalty-last-n = %d\n", value));
+            }
             params.sampling.dry_penalty_last_n = value;
         }
     ).set_sparam());
@@ -1162,18 +1202,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_NO_KV_OFFLOAD"));
     add_opt(common_arg(
         {"-ctk", "--cache-type-k"}, "TYPE",
-        string_format("KV cache data type for K (default: %s)", params.cache_type_k.c_str()),
+        string_format(
+            "KV cache data type for K\n"
+            "allowed values: %s\n"
+            "(default: %s)",
+            get_all_kv_cache_types().c_str(),
+            ggml_type_name(params.cache_type_k)
+        ),
         [](common_params & params, const std::string & value) {
-            // TODO: get the type right here
-            params.cache_type_k = value;
+            params.cache_type_k = kv_cache_type_from_str(value);
         }
     ).set_env("LLAMA_ARG_CACHE_TYPE_K"));
     add_opt(common_arg(
         {"-ctv", "--cache-type-v"}, "TYPE",
-        string_format("KV cache data type for V (default: %s)", params.cache_type_v.c_str()),
+        string_format(
+            "KV cache data type for V\n"
+            "allowed values: %s\n"
+            "(default: %s)",
+            get_all_kv_cache_types().c_str(),
+            ggml_type_name(params.cache_type_v)
+        ),
         [](common_params & params, const std::string & value) {
-            // TODO: get the type right here
-            params.cache_type_v = value;
+            params.cache_type_v = kv_cache_type_from_str(value);
         }
     ).set_env("LLAMA_ARG_CACHE_TYPE_V"));
     add_opt(common_arg(
@@ -1699,6 +1749,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.public_path = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_STATIC_PATH"));
+    add_opt(common_arg(
+        {"--no-webui"},
+        string_format("Disable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"),
+        [](common_params & params) {
+            params.webui = false;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_WEBUI"));
     add_opt(common_arg(
         {"--embedding", "--embeddings"},
         string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
@@ -1814,9 +1871,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--chat-template"}, "JINJA_TEMPLATE",
-        "set custom jinja chat template (default: template taken from model's metadata)\n"
-        "if suffix/prefix are specified, template will be disabled\n"
-        "only commonly used templates are accepted:\nhttps://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template",
+        string_format(
+            "set custom jinja chat template (default: template taken from model's metadata)\n"
+            "if suffix/prefix are specified, template will be disabled\n"
+            "list of built-in templates:\n%s", list_builtin_chat_templates().c_str()
+        ),
         [](common_params & params, const std::string & value) {
             if (!common_chat_verify_template(value)) {
                 throw std::runtime_error(string_format(
@@ -2062,35 +2121,35 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, int value) {
             params.speculative.n_max = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MAX"));
     add_opt(common_arg(
         {"--draft-min", "--draft-n-min"}, "N",
         string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.n_min),
         [](common_params & params, int value) {
             params.speculative.n_min = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MIN"));
     add_opt(common_arg(
         {"--draft-p-split"}, "P",
         string_format("speculative decoding split probability (default: %.1f)", (double)params.speculative.p_split),
         [](common_params & params, const std::string & value) {
             params.speculative.p_split = std::stof(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE}).set_env("LLAMA_ARG_DRAFT_P_SPLIT"));
     add_opt(common_arg(
         {"--draft-p-min"}, "P",
         string_format("minimum speculative decoding probability (greedy) (default: %.1f)", (double)params.speculative.p_min),
         [](common_params & params, const std::string & value) {
             params.speculative.p_min = std::stof(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_P_MIN"));
     add_opt(common_arg(
         {"-cd", "--ctx-size-draft"}, "N",
         string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx),
         [](common_params & params, int value) {
             params.speculative.n_ctx = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CTX_SIZE_DRAFT"));
     add_opt(common_arg(
         {"-devd", "--device-draft"}, "<dev1,dev2,..>",
         "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n"
@@ -2110,14 +2169,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n");
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT"));
     add_opt(common_arg(
         {"-md", "--model-draft"}, "FNAME",
         "draft model for speculative decoding (default: unused)",
         [](common_params & params, const std::string & value) {
             params.speculative.model = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT"));
 
     return ctx_arg;
 }

common/common.cpp

@@ -940,6 +940,25 @@ struct common_init_result common_init_from_params(common_params & params) {
         params.sampling.ignore_eos = false;
     }
 
+    if (params.sampling.ignore_eos) {
+        for (llama_token i = 0; i < llama_n_vocab(model); i++) {
+            if (llama_token_is_eog(model, i)) {
+                LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
+                params.sampling.logit_bias.push_back({i, -INFINITY});
+            }
+        }
+    }
+
+    if (params.sampling.penalty_last_n == -1) {
+        LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+        params.sampling.penalty_last_n = llama_n_ctx(lctx);
+    }
+
+    if (params.sampling.dry_penalty_last_n == -1) {
+        LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+        params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
+    }
+
     if (params.warmup) {
         LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
 
@@ -1015,38 +1034,6 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
     return mparams;
 }
 
-static ggml_type kv_cache_type_from_str(const std::string & s) {
-    if (s == "f32") {
-        return GGML_TYPE_F32;
-    }
-    if (s == "f16") {
-        return GGML_TYPE_F16;
-    }
-    if (s == "bf16") {
-        return GGML_TYPE_BF16;
-    }
-    if (s == "q8_0") {
-        return GGML_TYPE_Q8_0;
-    }
-    if (s == "q4_0") {
-        return GGML_TYPE_Q4_0;
-    }
-    if (s == "q4_1") {
-        return GGML_TYPE_Q4_1;
-    }
-    if (s == "iq4_nl") {
-        return GGML_TYPE_IQ4_NL;
-    }
-    if (s == "q5_0") {
-        return GGML_TYPE_Q5_0;
-    }
-    if (s == "q5_1") {
-        return GGML_TYPE_Q5_1;
-    }
-
-    throw std::runtime_error("Unsupported cache type: " + s);
-}
-
 struct llama_context_params common_context_params_to_llama(const common_params & params) {
     auto cparams = llama_context_default_params();
 
@@ -1081,8 +1068,8 @@ struct llama_context_params common_context_params_to_llama(const common_params &
         cparams.pooling_type  = LLAMA_POOLING_TYPE_RANK;
     }
 
-    cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
-    cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
+    cparams.type_k = params.cache_type_k;
+    cparams.type_v = params.cache_type_v;
 
     return cparams;
 }
@@ -1108,12 +1095,6 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_p
 #define CURL_MAX_RETRY 3
 #define CURL_RETRY_DELAY_SECONDS 2
 
-
-static bool starts_with(const std::string & str, const std::string & prefix) {
-    // While we wait for C++20's std::string::starts_with...
-    return str.rfind(prefix, 0) == 0;
-}
-
 static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_attempts, int retry_delay_seconds) {
     int remaining_attempts = max_attempts;
 

common/common.h

@@ -37,9 +37,9 @@ using llama_tokens = std::vector<llama_token>;
 
 // build info
 extern int LLAMA_BUILD_NUMBER;
-extern char const * LLAMA_COMMIT;
-extern char const * LLAMA_COMPILER;
-extern char const * LLAMA_BUILD_TARGET;
+extern const char * LLAMA_COMMIT;
+extern const char * LLAMA_COMPILER;
+extern const char * LLAMA_BUILD_TARGET;
 
 struct common_control_vector_load_info;
 
@@ -95,6 +95,7 @@ enum common_sampler_type {
     COMMON_SAMPLER_TYPE_TEMPERATURE = 7,
     COMMON_SAMPLER_TYPE_XTC         = 8,
     COMMON_SAMPLER_TYPE_INFILL      = 9,
+    COMMON_SAMPLER_TYPE_PENALTIES   = 10,
 };
 
 // dimensionality reduction methods, used by cvector-generator
@@ -130,7 +131,6 @@ struct common_params_sampling {
     int32_t mirostat           = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
     float   mirostat_tau       = 5.00f; // target entropy
     float   mirostat_eta       = 0.10f; // learning rate
-    bool    penalize_nl        = false; // consider newlines as a repeatable token
     bool    ignore_eos         = false;
     bool    no_perf            = false; // disable performance metrics
     bool    timing_per_token   = false;
@@ -139,6 +139,7 @@ struct common_params_sampling {
 
 
     std::vector<enum common_sampler_type> samplers = {
+        COMMON_SAMPLER_TYPE_PENALTIES,
         COMMON_SAMPLER_TYPE_DRY,
         COMMON_SAMPLER_TYPE_TOP_K,
         COMMON_SAMPLER_TYPE_TYPICAL_P,
@@ -193,11 +194,13 @@ struct common_params {
     float   defrag_thold          =  0.1f; // KV cache defragmentation threshold
 
     // offload params
-    std::vector<ggml_backend_dev_t> devices;         // devices to use for offloading
-    int32_t n_gpu_layers                    =    -1; // number of layers to store in VRAM (-1 - use default)
-    int32_t main_gpu                        =     0; // the GPU that is used for scratch and small tensors
-    float   tensor_split[128]               =   {0}; // how split tensors should be distributed across GPUs
-    enum llama_split_mode        split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
+    std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
+
+    int32_t n_gpu_layers      = -1;  // number of layers to store in VRAM (-1 - use default)
+    int32_t main_gpu          = 0;   // the GPU that is used for scratch and small tensors
+    float   tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
+
+    enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
 
     struct cpu_params cpuparams;
     struct cpu_params cpuparams_batch;
@@ -215,7 +218,7 @@ struct common_params {
     struct common_params_speculative speculative;
 
     std::string model                = ""; // model path                                                    // NOLINT
-    std::string model_alias          = "unknown"; // model alias                                            // NOLINT
+    std::string model_alias          = ""; // model alias                                                   // NOLINT
     std::string model_url            = ""; // model url to download                                         // NOLINT
     std::string hf_token             = ""; // HF token                                                      // NOLINT
     std::string hf_repo              = ""; // HF repo                                                       // NOLINT
@@ -286,8 +289,8 @@ struct common_params {
     bool warmup            = true;  // warmup run
     bool check_tensors     = false; // validate tensor data
 
-    std::string cache_type_k = "f16"; // KV cache data type for the K
-    std::string cache_type_v = "f16"; // KV cache data type for the V
+    ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
+    ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
 
     // multimodal models (see examples/llava)
     std::string mmproj = "";        // path to multimodal projector                                         // NOLINT
@@ -437,6 +440,11 @@ std::vector<std::string> string_split<std::string>(const std::string & input, ch
     return parts;
 }
 
+static bool string_starts_with(const std::string & str,
+                               const std::string & prefix) {  // While we wait for C++20's std::string::starts_with...
+    return str.rfind(prefix, 0) == 0;
+}
+
 bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
 void string_process_escapes(std::string & input);
 

common/sampling.cpp

@@ -161,32 +161,20 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                 params.logit_bias.size(),
                 params.logit_bias.data()));
 
-    llama_sampler_chain_add(result->chain,
-            llama_sampler_init_penalties(
-                llama_n_vocab  (model),
-                llama_token_eos(model),
-                llama_token_nl (model),
-                params.penalty_last_n,
-                params.penalty_repeat,
-                params.penalty_freq,
-                params.penalty_present,
-                params.penalize_nl,
-                params.ignore_eos));
-
     if (params.mirostat == 0) {
         for (const auto & cnstr : params.samplers) {
             switch (cnstr) {
-                    case COMMON_SAMPLER_TYPE_DRY:
+                case COMMON_SAMPLER_TYPE_DRY:
                     {
-                        std::vector<const char*> c_breakers;
+                        std::vector<const char *> c_breakers;
                         c_breakers.reserve(params.dry_sequence_breakers.size());
-                        for (const auto& str : params.dry_sequence_breakers) {
+                        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      (model, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                     }
-                        break;
+                    break;
                 case COMMON_SAMPLER_TYPE_TOP_K:
                     llama_sampler_chain_add(result->chain, llama_sampler_init_top_k    (params.top_k));
                     break;
@@ -208,6 +196,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                 case COMMON_SAMPLER_TYPE_INFILL:
                     llama_sampler_chain_add(result->chain, llama_sampler_init_infill   (model));
                     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");
             }
@@ -415,6 +406,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
         case COMMON_SAMPLER_TYPE_XTC:         return 'x';
         case COMMON_SAMPLER_TYPE_INFILL:      return 'i';
+        case COMMON_SAMPLER_TYPE_PENALTIES:   return 'e';
         default : return '?';
     }
 }
@@ -429,6 +421,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
         case COMMON_SAMPLER_TYPE_XTC:         return "xtc";
         case COMMON_SAMPLER_TYPE_INFILL:      return "infill";
+        case COMMON_SAMPLER_TYPE_PENALTIES:   return "penalties";
         default : return "";
     }
 }
@@ -443,6 +436,7 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         { "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
         { "xtc",         COMMON_SAMPLER_TYPE_XTC },
         { "infill",      COMMON_SAMPLER_TYPE_INFILL },
+        { "penalties",   COMMON_SAMPLER_TYPE_PENALTIES },
     };
 
     // since samplers names are written multiple ways
@@ -489,6 +483,7 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
         { 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 },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL),      COMMON_SAMPLER_TYPE_INFILL },
+        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES),   COMMON_SAMPLER_TYPE_PENALTIES },
     };
 
     std::vector<common_sampler_type> samplers;

common/speculative.cpp

@@ -62,6 +62,10 @@ struct common_speculative * common_speculative_init(
 }
 
 void common_speculative_free(struct common_speculative * spec) {
+    if (spec == nullptr) {
+        return;
+    }
+
     common_sampler_free(spec->smpl);
 
     llama_batch_free(spec->batch);

convert_hf_to_gguf.py

@@ -525,6 +525,9 @@ class Model:
             else:
                 token: str = reverse_vocab[i]
                 if token in added_vocab:
+                    # We need to manually encode and decode the added tokens in case special characters
+                    # used for `\n` / `\t` have been manually added in the added tokens
+                    token = tokenizer.decode(tokenizer.encode(token))
                     if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token):
                         toktypes.append(gguf.TokenType.CONTROL)
                     else:
@@ -571,6 +574,9 @@ class Model:
         if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
             # ref: https://huggingface.co/tiiuae/falcon-7b
             res = "falcon"
+        if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e":
+            # ref: https://huggingface.co/tiiuae/Falcon3-7B-Base
+            res = "falcon3"
         if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
             # ref: https://huggingface.co/BAAI/bge-small-en-v1.5
             res = "bert-bge"
@@ -658,6 +664,15 @@ class Model:
         if chkhsh == "60824e3c0d9401f89943cbb2fff727f0e2d4c545ba4df2d6e4f09a6db0f5b450":
             # ref: https://huggingface.co/facebook/chameleon-7b
             res = "chameleon"
+        if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
+            # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
+            res = "minerva-7b"
+        if chkhsh == "8b5a93ed704057481f240da0be7e7dca721d7f8f4755263b6807227a2cbeae65":
+            # ref: https://huggingface.co/sentence-transformers/stsb-roberta-base
+            res = "roberta-bpe"
+        if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb":
+            # ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct
+            res = "gigachat"
 
         if res is None:
             logger.warning("\n")
@@ -1831,29 +1846,40 @@ class MiniCPMModel(Model):
     model_arch = gguf.MODEL_ARCH.MINICPM
 
     def set_gguf_parameters(self):
-        block_count = self.hparams["num_hidden_layers"]
-        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
-        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
-        self.gguf_writer.add_block_count(block_count)
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
-        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
-        self.gguf_writer.add_file_type(self.ftype)
+        super().set_gguf_parameters()
+        embedding_scale = float(self.hparams["scale_emb"])
+        self.gguf_writer.add_embedding_scale(embedding_scale)
+        logger.info(f"gguf: (minicpm) embedding_scale = {embedding_scale}")
+        residual_scale = self.hparams["scale_depth"] / self.hparams["num_hidden_layers"] ** 0.5
+        self.gguf_writer.add_residual_scale(residual_scale)
+        logger.info(f"gguf: (minicpm) residual_scale = {residual_scale}")
+        logit_scale = self.hparams["hidden_size"] / self.hparams["dim_model_base"]
+        self.gguf_writer.add_logit_scale(logit_scale)
+        logger.info(f"gguf: (minicpm) logit_scale = {logit_scale}")
+        if self.hparams.get("rope_scaling") is not None:
+            if self.hparams["rope_scaling"].get("type") == "longrope":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LONGROPE)
+                logger.info(f"gguf: (minicpm) rope_scaling_type = {gguf.RopeScalingType.LONGROPE}")
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        rope_dims = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+
+        rope_scaling = self.find_hparam(['rope_scaling'], True)
+        if rope_scaling is not None:
+            long_factors = rope_scaling.get('long_factor', None)
+            short_factors = rope_scaling.get('short_factor', None)
+
+            if long_factors is None or short_factors is None:
+                raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor')
+
+            if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2:
+                raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}')
+
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))
 
     def set_vocab(self):
-        self._set_vocab_llama_hf()
-
-    def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
-        if n_kv_head is not None and n_head != n_kv_head:
-            n_head //= n_kv_head
-
-        return (
-            weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-            .swapaxes(1, 2)
-            .reshape(weights.shape)
-        )
+        self._set_vocab_sentencepiece()
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
@@ -1863,9 +1889,9 @@ class MiniCPMModel(Model):
 
         # HF models permute some of the tensors, so we need to undo that
         if name.endswith(("q_proj.weight")):
-            data_torch = self._reverse_hf_permute(data_torch, n_head, n_head)
+            data_torch = LlamaModel.permute(data_torch, n_head, n_head)
         if name.endswith(("k_proj.weight")):
-            data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head)
+            data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
 
         return [(self.map_tensor_name(name), data_torch)]
 
@@ -1975,6 +2001,37 @@ class Qwen2Model(Model):
         except FileNotFoundError:
             self._set_vocab_gpt2()
 
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
+            if self.hparams["rope_scaling"].get("type") == "yarn":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
+                self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"])
+
+
+@Model.register("Qwen2VLForConditionalGeneration")
+class Qwen2VLModel(Model):
+    model_arch = gguf.MODEL_ARCH.QWEN2VL
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        mrope_section = self.hparams["rope_scaling"]["mrope_section"]
+        mrope_section += [0] * max(0, 4 - len(mrope_section))
+        self.gguf_writer.add_rope_dimension_sections(mrope_section)
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
+        for name, data in super().get_tensors():
+            if name.startswith("visual."):
+                continue
+            yield name, data
+
 
 @Model.register("Qwen2MoeForCausalLM")
 class Qwen2MoeModel(Model):
@@ -2519,7 +2576,7 @@ class InternLM2Model(Model):
             return [(self.map_tensor_name(name), data_torch)]
 
 
-@Model.register("BertModel", "CamembertModel")
+@Model.register("BertModel", "CamembertModel", "RobertaModel")
 class BertModel(Model):
     model_arch = gguf.MODEL_ARCH.BERT
 
@@ -2560,7 +2617,8 @@ class BertModel(Model):
 
         # we need this to validate the size of the token_type embeddings
         # though currently we are passing all zeros to the token_type embeddings
-        self.gguf_writer.add_token_type_count(2)  # "Sequence A" or "Sequence B"
+        # "Sequence A" or "Sequence B"
+        self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))
 
         # convert to phantom space vocab
         def phantom(tok):
@@ -3378,6 +3436,97 @@ class ArcticModel(Model):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@Model.register("DeepseekForCausalLM")
+class DeepseekModel(Model):
+    model_arch = gguf.MODEL_ARCH.DEEPSEEK
+
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        if "head_dim" in hparams:
+            rope_dim = hparams["head_dim"]
+        else:
+            rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
+
+        self.gguf_writer.add_rope_dimension_count(rope_dim)
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+        self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
+        self.gguf_writer.add_expert_weights_scale(1.0)
+        self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
+        self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    @staticmethod
+    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
+        if n_head_kv is not None and n_head != n_head_kv:
+            n_head = n_head_kv
+        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
+                .swapaxes(1, 2)
+                .reshape(weights.shape))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        n_head = self.hparams["num_attention_heads"]
+        n_kv_head = self.hparams.get("num_key_value_heads")
+
+        if name.endswith(("q_proj.weight", "q_proj.bias")):
+            data_torch = DeepseekModel.permute(data_torch, n_head, n_head)
+        if name.endswith(("k_proj.weight", "k_proj.bias")):
+            data_torch = DeepseekModel.permute(data_torch, n_head, n_kv_head)
+
+        # process the experts separately
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["n_routed_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
 @Model.register("DeepseekV2ForCausalLM")
 class DeepseekV2Model(Model):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK2

convert_hf_to_gguf_update.py

@@ -17,7 +17,7 @@
 #
 #   python3 convert_hf_to_gguf_update.py <huggingface_token>
 #
-# - Copy-paste the generated get_vocab_base_pre() function into convert_hf_to_gguf.py
+# - 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
@@ -72,6 +72,7 @@ models = [
     {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
     {"name": "falcon",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
     {"name": "bert-bge",       "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
+    {"name": "falcon3",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", },
     {"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
     {"name": "mpt",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
     {"name": "starcoder",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
@@ -102,6 +103,9 @@ 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"},
 ]
 
 

docs/backend/BLIS.md

@@ -27,13 +27,6 @@ We recommend using openmp since it's easier to modify the cores being used.
 
 ### llama.cpp compilation
 
-Makefile:
-
-```bash
-make GGML_BLIS=1 -j
-# make GGML_BLIS=1 llama-benchmark-matmult
-```
-
 CMake:
 
 ```bash

docs/build.md

@@ -7,124 +7,75 @@ git clone https://github.com/ggerganov/llama.cpp
 cd llama.cpp
 ```
 
-In order to build llama.cpp you have four different options.
+The following sections describe how to build with different backends and options.
 
-- Using `make`:
-  - On Linux or MacOS:
+## CPU Build
 
-      ```bash
-      make
-      ```
+Build llama.cpp using `CMake`:
 
-  - On Windows (x86/x64 only, arm64 requires cmake):
+```bash
+cmake -B build
+cmake --build build --config Release
+```
 
-    1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
-    2. Extract `w64devkit` on your pc.
-    3. Run `w64devkit.exe`.
-    4. Use the `cd` command to reach the `llama.cpp` folder.
-    5. From here you can run:
-        ```bash
-        make
-        ```
+**Notes**:
 
-  - Notes:
-    - For `Q4_0_4_4` quantization type build, add the `GGML_NO_LLAMAFILE=1` flag. For example, use `make GGML_NO_LLAMAFILE=1`.
-    - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel.
-    - For faster repeated compilation, install [ccache](https://ccache.dev/).
-    - For debug builds, run `make LLAMA_DEBUG=1`
+- For faster compilation, add the `-j` argument to run multiple jobs in parallel, or use a generator that does this automatically such as Ninja. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel.
+- For faster repeated compilation, install [ccache](https://ccache.dev/)
+- For debug builds, there are two cases:
 
-- Using `CMake`:
+    1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
 
-  ```bash
-  cmake -B build
+       ```bash
+       cmake -B build -DCMAKE_BUILD_TYPE=Debug
+       cmake --build build
+       ```
+
+    2. Multi-config generators (`-G` param set to Visual Studio, XCode...):
+
+       ```bash
+       cmake -B build -G "Xcode"
+       cmake --build build --config Debug
+       ```
+
+    For more details and a list of supported generators, see the [CMake documentation](https://cmake.org/cmake/help/latest/manual/cmake-generators.7.html).
+- For static builds, add `-DBUILD_SHARED_LIBS=OFF`:
+  ```
+  cmake -B build -DBUILD_SHARED_LIBS=OFF
   cmake --build build --config Release
   ```
 
-  **Notes**:
-
-    - For `Q4_0_4_4` quantization type build, add the `-DGGML_LLAMAFILE=OFF` cmake option. For example, use `cmake -B build -DGGML_LLAMAFILE=OFF`.
-    - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel.
-    - For faster repeated compilation, install [ccache](https://ccache.dev/).
-    - For debug builds, there are two cases:
-
-      1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
+- Building for Windows (x86, x64 and arm64) with MSVC or clang as compilers:
+    - Install Visual Studio 2022, e.g. via the [Community Edition](https://visualstudio.microsoft.com/de/vs/community/). In the installer, select at least the following options (this also automatically installs the required additional tools like CMake,...):
+    - Tab Workload: Desktop-development with C++
+    - Tab Components (select quickly via search): C++-_CMake_ Tools for Windows, _Git_ for Windows, C++-_Clang_ Compiler for Windows, MS-Build Support for LLVM-Toolset (clang)
+    - Please remember to always use a Developer Command Prompt / PowerShell for VS2022 for git, build, test
+    - For Windows on ARM (arm64, WoA) build with:
+    ```bash
+    cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF
+    cmake --build build-arm64-windows-llvm-release
+    ```
+    Building for arm64 can also be done with the MSVC compiler with the build-arm64-windows-MSVC preset, or the standard CMake build instructions. However, note that the MSVC compiler does not support inline ARM assembly code, used e.g. for the accelerated Q4_0_N_M CPU kernels.
 
+    For building with ninja generator and clang compiler as default:
+      -set path:set LIB=C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\x64;C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.41.34120\lib\x64\uwp;C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\x64
       ```bash
-      cmake -B build -DCMAKE_BUILD_TYPE=Debug
-      cmake --build build
+      cmake --preset x64-windows-llvm-release
+      cmake --build build-x64-windows-llvm-release
       ```
 
-      2. Multi-config generators (`-G` param set to Visual Studio, XCode...):
-
-      ```bash
-      cmake -B build -G "Xcode"
-      cmake --build build --config Debug
-      ```
-    - Building for Windows (x86, x64 and arm64) with MSVC or clang as compilers:
-      - Install Visual Studio 2022, e.g. via the [Community Edition](https://visualstudio.microsoft.com/de/vs/community/). In the installer, select at least the following options (this also automatically installs the required additional tools like CMake,...):
-        - Tab Workload: Desktop-development with C++
-        - Tab Components (select quickly via search): C++-_CMake_ Tools for Windows, _Git_ for Windows, C++-_Clang_ Compiler for Windows, MS-Build Support for LLVM-Toolset (clang)
-      - Please remember to always use a Developer Command Prompt / PowerShell for VS2022 for git, build, test
-      - For Windows on ARM (arm64, WoA) build with:
-        ```bash
-        cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF
-        cmake --build build-arm64-windows-llvm-release
-        ```
-        Note: Building for arm64 could also be done just with MSVC (with the build-arm64-windows-MSVC preset, or the standard CMake build instructions). But MSVC does not support inline ARM assembly-code, used e.g. for the accelerated Q4_0_4_8 CPU kernels.
-
--   Using `gmake` (FreeBSD):
-
-    1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics)
-    2. Add your user to **video** group
-    3. Install compilation dependencies.
-
-        ```bash
-        sudo pkg install gmake automake autoconf pkgconf llvm15 openblas
-
-        gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4
-        ```
-
-## Metal Build
-
-On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
-To disable the Metal build at compile time use the `GGML_NO_METAL=1` flag or the `GGML_METAL=OFF` cmake option.
-
-When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line
-argument.
-
 ## BLAS Build
 
-Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS. There are currently several different BLAS implementations available for build and use:
+Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Using BLAS doesn't affect the generation performance. There are currently several different BLAS implementations available for build and use:
 
-### Accelerate Framework:
+### Accelerate Framework
 
 This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
 
-### OpenBLAS:
+### OpenBLAS
 
 This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
 
-- Using `make`:
-  - On Linux:
-    ```bash
-    make GGML_OPENBLAS=1
-    ```
-
-  - On Windows:
-
-    1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
-    2. Download the latest version of [OpenBLAS for Windows](https://github.com/xianyi/OpenBLAS/releases).
-    3. Extract `w64devkit` on your pc.
-    4. From the OpenBLAS zip that you just downloaded copy `libopenblas.a`, located inside the `lib` folder, inside `w64devkit\x86_64-w64-mingw32\lib`.
-    5. From the same OpenBLAS zip copy the content of the `include` folder inside `w64devkit\x86_64-w64-mingw32\include`.
-    6. Run `w64devkit.exe`.
-    7. Use the `cd` command to reach the `llama.cpp` folder.
-    8. From here you can run:
-
-        ```bash
-        make GGML_OPENBLAS=1
-        ```
-
 - Using `CMake` on Linux:
 
     ```bash
@@ -136,14 +87,6 @@ This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS i
 
 Check [BLIS.md](./backend/BLIS.md) for more information.
 
-### SYCL
-
-SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
-
-llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
-
-For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
-
 ### Intel oneMKL
 
 Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
@@ -161,16 +104,29 @@ Building through oneAPI compilers will make avx_vnni instruction set available f
 
 Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information.
 
-### CUDA
+### Other BLAS libraries
 
-This provides GPU acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
+Any other BLAS library can be used by setting the `GGML_BLAS_VENDOR` option. See the [CMake documentation](https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors) for a list of supported vendors.
 
-For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling.
+## Metal Build
+
+On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
+To disable the Metal build at compile time use the `-DGGML_METAL=OFF` cmake option.
+
+When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers 0` command-line argument.
+
+## SYCL
+
+SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
+
+llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
+
+For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
+
+## CUDA
+
+This provides GPU acceleration using an NVIDIA GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from the [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads).
 
-- Using `make`:
-  ```bash
-  make GGML_CUDA=1
-  ```
 - Using `CMake`:
 
   ```bash
@@ -192,14 +148,10 @@ The following compilation options are also available to tweak performance:
 | GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer       | 128     | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial.                                                                         |
 | GGML_CUDA_FA_ALL_QUANTS       | Boolean                | false   | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer.                                                                                                  |
 
-### MUSA
+## MUSA
 
 This provides GPU acceleration using the MUSA cores of your Moore Threads MTT GPU. Make sure to have the MUSA SDK installed. You can download it from here: [MUSA SDK](https://developer.mthreads.com/sdk/download/musa).
 
-- Using `make`:
-  ```bash
-  make GGML_MUSA=1
-  ```
 - Using `CMake`:
 
   ```bash
@@ -213,16 +165,12 @@ The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enab
 
 Most of the compilation options available for CUDA should also be available for MUSA, though they haven't been thoroughly tested yet.
 
-### hipBLAS
+## HIP
 
-This provides BLAS acceleration on HIP-supported AMD GPUs.
+This provides GPU acceleration on HIP-supported AMD GPUs.
 Make sure to have ROCm installed.
 You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick).
 
-- Using `make`:
-  ```bash
-  make GGML_HIP=1
-  ```
 - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
   ```bash
   HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
@@ -247,11 +195,6 @@ You can download it from your Linux distro's package manager or from here: [ROCm
       && cmake --build build -- -j 16
   ```
 
-- Using `make` (example for target gfx1030, build with 16 CPU threads):
-  ```bash
-  make -j16 GGML_HIP=1 GGML_HIP_UMA=1 AMDGPU_TARGETS=gfx1030
-  ```
-
 - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
   ```bash
   set PATH=%HIP_PATH%\bin;%PATH%
@@ -265,11 +208,11 @@ You can download it from your Linux distro's package manager or from here: [ROCm
 The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
 If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
 
-### Vulkan
+## Vulkan
 
 **Windows**
 
-#### w64devkit
+### w64devkit
 
 Download and extract [`w64devkit`](https://github.com/skeeto/w64devkit/releases).
 
@@ -289,9 +232,14 @@ Libs: -lvulkan-1
 EOF
 
 ```
-Switch into the `llama.cpp` directory and run `make GGML_VULKAN=1`.
 
-#### Git Bash MINGW64
+Switch into the `llama.cpp` directory and build using CMake.
+```sh
+cmake -B build -DGGML_VULKAN=ON
+cmake --build build --config Release
+```
+
+### Git Bash MINGW64
 
 Download and install [`Git-SCM`](https://git-scm.com/downloads/win) with the default settings
 
@@ -310,20 +258,21 @@ cmake --build build --config Release
 
 Now you can load the model in conversation mode using `Vulkan`
 
-```
-build/bin/release/llama-cli -m "[PATH TO MODEL]" -ngl 100 -c 16384 -t 10 -n -2 -cnv
+```sh
+build/bin/Release/llama-cli -m "[PATH TO MODEL]" -ngl 100 -c 16384 -t 10 -n -2 -cnv
 ```
 
-#### MSYS2
+### MSYS2
 Install [MSYS2](https://www.msys2.org/) and then run the following commands in a UCRT terminal to install dependencies.
-  ```sh
-  pacman -S git \
-      mingw-w64-ucrt-x86_64-gcc \
-      mingw-w64-ucrt-x86_64-cmake \
-      mingw-w64-ucrt-x86_64-vulkan-devel \
-      mingw-w64-ucrt-x86_64-shaderc
-  ```
-Switch into `llama.cpp` directory and build using CMake.
+```sh
+pacman -S git \
+    mingw-w64-ucrt-x86_64-gcc \
+    mingw-w64-ucrt-x86_64-cmake \
+    mingw-w64-ucrt-x86_64-vulkan-devel \
+    mingw-w64-ucrt-x86_64-shaderc
+```
+
+Switch into the `llama.cpp` directory and build using CMake.
 ```sh
 cmake -B build -DGGML_VULKAN=ON
 cmake --build build --config Release
@@ -372,7 +321,7 @@ cmake --build build --config Release
 # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32
 ```
 
-### CANN
+## CANN
 This provides NPU acceleration using the AI cores of your Ascend NPU. And [CANN](https://www.hiascend.com/en/software/cann) is a hierarchical APIs to help you to quickly build AI applications and service based on Ascend NPU.
 
 For more information about Ascend NPU in [Ascend Community](https://www.hiascend.com/en/).
@@ -387,22 +336,26 @@ cmake --build build --config release
 
 You can test with:
 
-`./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32`
-
-If the fllowing info is output on screen, you are using `llama.cpp by CANN backend`:
 ```bash
-llm_load_tensors:       CANN buffer size = 13313.00 MiB
+./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32
+```
+
+If the following info is output on screen, you are using `llama.cpp` with the CANN backend:
+```bash
+llm_load_tensors:       CANN model buffer size = 13313.00 MiB
 llama_new_context_with_model:       CANN compute buffer size =  1260.81 MiB
 ```
 
 For detailed info, such as model/device supports, CANN install, please refer to [llama.cpp for CANN](./backend/CANN.md).
 
-### Android
+## Android
 
 To read documentation for how to build on Android, [click here](./android.md)
 
-### Arm CPU optimized mulmat kernels
+## Notes about GPU-accelerated backends
 
-Llama.cpp includes a set of optimized mulmat kernels for the Arm architecture, leveraging Arm® Neon™, int8mm and SVE instructions. These kernels are enabled at build time through the appropriate compiler cpu-type flags, such as `-DCMAKE_C_FLAGS=-march=armv8.2a+i8mm+sve`. Note that these optimized kernels require the model to be quantized into one of the formats: `Q4_0_4_4` (Arm Neon), `Q4_0_4_8` (int8mm) or `Q4_0_8_8` (SVE). The SVE mulmat kernel specifically requires a vector width of 256 bits. When running on devices with a different vector width, it is recommended to use the `Q4_0_4_8` (int8mm) or `Q4_0_4_4` (Arm Neon) formats for better performance. Refer to [examples/quantize/README.md](../examples/quantize/README.md) for more information on the quantization formats.
+The GPU may still be used to accelerate some parts of the computation even when using the `-ngl 0` option. You can fully disable GPU acceleration by using `--device none`.
 
-To support `Q4_0_4_4`, you must build with `GGML_NO_LLAMAFILE=1` (`make`) or `-DGGML_LLAMAFILE=OFF` (`cmake`).
+In most cases, it is possible to build and use multiple backends at the same time. For example, you can build llama.cpp with both CUDA and Vulkan support by using the `-DGGML_CUDA=ON -DGGML_VULKAN=ON` options with CMake. At runtime, you can specify which backend devices to use with the `--device` option. To see a list of available devices, use the `--list-devices` option.
+
+Backends can be built as dynamic libraries that can be loaded dynamically at runtime. This allows you to use the same llama.cpp binary on different machines with different GPUs. To enable this feature, use the `GGML_BACKEND_DL` option when building.

examples/CMakeLists.txt

@@ -20,7 +20,12 @@ else()
     add_subdirectory(batched)
     add_subdirectory(embedding)
     add_subdirectory(eval-callback)
-    add_subdirectory(gbnf-validator)
+
+    if (NOT WIN32)
+        # disabled on Windows because it uses internal functions not exported with LLAMA_API
+        add_subdirectory(gbnf-validator)
+    endif()
+
     add_subdirectory(gguf-hash)
     add_subdirectory(gguf-split)
     add_subdirectory(gguf)
@@ -46,12 +51,16 @@ else()
     add_subdirectory(speculative)
     add_subdirectory(speculative-simple)
     add_subdirectory(tokenize)
+    add_subdirectory(gen-docs)
     if (NOT GGML_BACKEND_DL)
         # these examples use the backends directly and cannot be built with dynamic loading
         add_subdirectory(convert-llama2c-to-ggml)
         add_subdirectory(cvector-generator)
         add_subdirectory(export-lora)
-        add_subdirectory(quantize-stats)
+        if (NOT WIN32)
+            # disabled on Windows because it uses internal functions not exported with LLAMA_API
+            add_subdirectory(quantize-stats)
+        endif()
         add_subdirectory(llava)
         if (GGML_RPC)
             add_subdirectory(rpc)

examples/base-translate.sh

@@ -1,61 +0,0 @@
-#!/bin/bash
-#
-# Few-shot translation example.
-# Requires a base model (i.e. no fine-tuned or instruct models).
-#
-# Usage:
-#
-#   cd llama.cpp
-#   make -j
-#
-#   ./examples/base-translate.sh <model-base> "<text>" [extra-main-args]
-#
-
-if [ $# -lt 2 ]; then
-  echo "Usage: ./base-translate.sh <model-base> \"<text>\" [extra-main-args]"
-  exit 1
-fi
-
-eargs=""
-if [ $# -gt 2 ]; then
-  eargs="${@:3}"
-fi
-
-ftmp="__llama.cpp_example_tmp__.txt"
-trap "rm -f $ftmp" EXIT
-
-echo "Translate from English to French:
-
-===
-
-sea otter, peppermint, plush girafe:
-
-sea otter => loutre de mer
-peppermint => menthe poivrée
-plush girafe => girafe peluche
-
-===
-
-violin
-
-violin => violon
-
-===
-
-phone, computer, mouse, keyboard:
-
-phone => téléphone
-computer => ordinateur
-mouse => souris
-keyboard => clavier
-
-===
-" > $ftmp
-
-echo "$2
-" >> $ftmp
-
-model=$1
-
-# generate the most likely continuation until the string "===" is found
-./llama-cli -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs

examples/batched/batched.cpp

@@ -65,6 +65,7 @@ int main(int argc, char ** argv) {
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
     auto sparams = llama_sampler_chain_default_params();
+    sparams.no_perf = false;
 
     llama_sampler * smpl = llama_sampler_chain_init(sparams);
 

examples/convert-llama2c-to-ggml/README.md

@@ -2,11 +2,8 @@
 
 This example reads weights from project [llama2.c](https://github.com/karpathy/llama2.c) and saves them in ggml compatible format. The vocab that is available in `models/ggml-vocab.bin` is used by default.
 
-To convert the model first download the models from the [llama2.c](https://github.com/karpathy/llama2.c) repository:
+To convert the model first download the models from the [llama2.c](https://github.com/karpathy/llama2.c) repository.
 
-`$ make -j`
-
-After successful compilation, following usage options are available:
 ```
 usage: ./llama-convert-llama2c-to-ggml [options]
 

examples/deprecation-warning/deprecation-warning.cpp

@@ -12,7 +12,7 @@ int main(int argc, char** argv) {
     }
 
     // Get only the program name from the full path
-    auto pos = filename.find_last_of('/');
+    auto pos = filename.find_last_of("/\\");
     if (pos != std::string::npos) {
         filename = filename.substr(pos+1);
     }

examples/gguf-split/gguf-split.cpp

@@ -287,7 +287,7 @@ struct split_strategy {
     }
 
     void print_info() {
-        printf("n_split: %ld\n", ctx_outs.size());
+        printf("n_split: %zu\n", ctx_outs.size());
         int i_split = 0;
         for (auto & ctx_out : ctx_outs) {
             // re-calculate the real gguf size for each split (= metadata size + total size of all tensors)
@@ -297,7 +297,7 @@ struct split_strategy {
                 total_size += ggml_nbytes(t);
             }
             total_size = total_size / 1000 / 1000; // convert to megabytes
-            printf("split %05d: n_tensors = %d, total_size = %ldM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
+            printf("split %05d: n_tensors = %d, total_size = %zuM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
             i_split++;
         }
     }

examples/imatrix/README.md

@@ -25,8 +25,6 @@ For faster computation, make sure to use GPU offloading via the `-ngl` argument
 ## Example
 
 ```bash
-GGML_CUDA=1 make -j
-
 # generate importance matrix (imatrix.dat)
 ./llama-imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99
 

examples/infill/README.md

@@ -14,7 +14,7 @@ In this section, we cover the most commonly used options for running the `infill
 -   `-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 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
+-   `-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

examples/llama-bench/llama-bench.cpp

@@ -1521,7 +1521,7 @@ int main(int argc, char ** argv) {
     for (const auto & inst : params_instances) {
         params_idx++;
         if (params.progress) {
-            fprintf(stderr, "llama-bench: benchmark %d/%ld: starting\n", params_idx, params_count);
+            fprintf(stderr, "llama-bench: benchmark %d/%zu: starting\n", params_idx, params_count);
         }
         // keep the same model between tests when possible
         if (!lmodel || !prev_inst || !inst.equal_mparams(*prev_inst)) {
@@ -1573,14 +1573,14 @@ int main(int argc, char ** argv) {
         // warmup run
         if (t.n_prompt > 0) {
             if (params.progress) {
-                fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup prompt run\n", params_idx, params_count);
+                fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup prompt run\n", params_idx, params_count);
             }
             //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
             test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
         }
         if (t.n_gen > 0) {
             if (params.progress) {
-                fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup generation run\n", params_idx, params_count);
+                fprintf(stderr, "llama-bench: benchmark %d/%zu: warmup generation run\n", params_idx, params_count);
             }
             test_gen(ctx, 1, t.n_threads);
         }
@@ -1592,14 +1592,14 @@ int main(int argc, char ** argv) {
 
             if (t.n_prompt > 0) {
                 if (params.progress) {
-                    fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count,
+                    fprintf(stderr, "llama-bench: benchmark %d/%zu: prompt run %d/%d\n", params_idx, params_count,
                             i + 1, params.reps);
                 }
                 test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
             }
             if (t.n_gen > 0) {
                 if (params.progress) {
-                    fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count,
+                    fprintf(stderr, "llama-bench: benchmark %d/%zu: generation run %d/%d\n", params_idx, params_count,
                             i + 1, params.reps);
                 }
                 test_gen(ctx, t.n_gen, t.n_threads);

examples/llama.swiftui/llama.cpp.swift/LibLlama.swift

@@ -210,20 +210,20 @@ actor LlamaContext {
 
             llama_kv_cache_clear(context)
 
-            let t_pp_start = ggml_time_us()
+            let t_pp_start = DispatchTime.now().uptimeNanoseconds / 1000;
 
             if llama_decode(context, batch) != 0 {
                 print("llama_decode() failed during prompt")
             }
             llama_synchronize(context)
 
-            let t_pp_end = ggml_time_us()
+            let t_pp_end = DispatchTime.now().uptimeNanoseconds / 1000;
 
             // bench text generation
 
             llama_kv_cache_clear(context)
 
-            let t_tg_start = ggml_time_us()
+            let t_tg_start = DispatchTime.now().uptimeNanoseconds / 1000;
 
             for i in 0..<tg {
                 llama_batch_clear(&batch)
@@ -238,7 +238,7 @@ actor LlamaContext {
                 llama_synchronize(context)
             }
 
-            let t_tg_end = ggml_time_us()
+            let t_tg_end = DispatchTime.now().uptimeNanoseconds / 1000;
 
             llama_kv_cache_clear(context)
 

examples/llama.swiftui/llama.swiftui.xcodeproj/project.pbxproj

@@ -7,6 +7,7 @@
 	objects = {
 
 /* Begin PBXBuildFile section */
+		1809696D2D05A39F00400EE8 /* llama in Frameworks */ = {isa = PBXBuildFile; productRef = 1809696C2D05A39F00400EE8 /* llama */; };
 		549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */ = {isa = PBXBuildFile; fileRef = 549479CA2AC9E16000E0F78B /* Metal.framework */; };
 		79E1D9CD2B4CD16E005F8E46 /* InputButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 79E1D9CC2B4CD16E005F8E46 /* InputButton.swift */; };
 		7FA3D2B32B2EA2F600543F92 /* DownloadButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = 7FA3D2B22B2EA2F600543F92 /* DownloadButton.swift */; };
@@ -17,7 +18,6 @@
 		8A3F84242AC4C891005E2EE8 /* models in Resources */ = {isa = PBXBuildFile; fileRef = 8A3F84232AC4C891005E2EE8 /* models */; };
 		8A907F332AC7138A006146EA /* LibLlama.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A907F322AC7134E006146EA /* LibLlama.swift */; };
 		8A9F7C4D2AC332EE008AE1EA /* LlamaState.swift in Sources */ = {isa = PBXBuildFile; fileRef = 8A9F7C4C2AC332EE008AE1EA /* LlamaState.swift */; };
-		DF810E132B4A5BA200301144 /* llama in Frameworks */ = {isa = PBXBuildFile; productRef = DF810E122B4A5BA200301144 /* llama */; };
 		F1FE20E22B465ECA00B45541 /* LoadCustomButton.swift in Sources */ = {isa = PBXBuildFile; fileRef = F1FE20E12B465EC900B45541 /* LoadCustomButton.swift */; };
 /* End PBXBuildFile section */
 
@@ -42,7 +42,7 @@
 			isa = PBXFrameworksBuildPhase;
 			buildActionMask = 2147483647;
 			files = (
-				DF810E132B4A5BA200301144 /* llama in Frameworks */,
+				1809696D2D05A39F00400EE8 /* llama in Frameworks */,
 				549479CB2AC9E16000E0F78B /* Metal.framework in Frameworks */,
 				8A39BE0A2AC7601100BFEB40 /* Accelerate.framework in Frameworks */,
 			);
@@ -151,7 +151,7 @@
 			);
 			name = llama.swiftui;
 			packageProductDependencies = (
-				DF810E122B4A5BA200301144 /* llama */,
+				1809696C2D05A39F00400EE8 /* llama */,
 			);
 			productName = llama.swiftui;
 			productReference = 8A1C83732AC328BD0096AF73 /* llama.swiftui.app */;
@@ -429,7 +429,7 @@
 /* End XCConfigurationList section */
 
 /* Begin XCSwiftPackageProductDependency section */
-		DF810E122B4A5BA200301144 /* llama */ = {
+		1809696C2D05A39F00400EE8 /* llama */ = {
 			isa = XCSwiftPackageProductDependency;
 			productName = llama;
 		};

examples/llava/CMakeLists.txt

@@ -43,3 +43,10 @@ set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-minicpmv-cli)
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
+
+set(TARGET llama-qwen2vl-cli)
+add_executable(${TARGET} qwen2vl-cli.cpp)
+set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-qwen2vl-cli)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)

examples/llava/clip.cpp

@@ -12,6 +12,10 @@
 #include "ggml-cuda.h"
 #endif
 
+#ifdef GGML_USE_SYCL
+#include "ggml-sycl.h"
+#endif
+
 #ifdef GGML_USE_METAL
 #include "ggml-metal.h"
 #endif
@@ -98,7 +102,9 @@ static std::string format(const char * fmt, ...) {
 #define KEY_HAS_LLAVA_PROJ      "clip.has_llava_projector"
 #define KEY_HAS_MINICPMV_PROJ   "clip.has_minicpmv_projector"
 #define KEY_MINICPMV_VERSION    "clip.minicpmv_version"
+#define KEY_HAS_QWEN2VL_MERGER  "clip.has_qwen2vl_merger"
 #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"
@@ -125,7 +131,8 @@ static std::string format(const char * fmt, ...) {
 #define TN_TOKEN_EMBD      "%s.token_embd.weight"
 #define TN_POS_EMBD        "%s.position_embd.weight"
 #define TN_CLASS_EMBD      "v.class_embd"
-#define TN_PATCH_EMBD      "v.patch_embd.weight"
+#define TN_PATCH_EMBD      "v.patch_embd.weight"  // not rename tensor with ".0" postfix for backwrad compat
+#define TN_PATCH_EMBD_1    "v.patch_embd.weight.1"
 #define TN_PATCH_BIAS      "v.patch_embd.bias"
 #define TN_ATTN_K          "%s.blk.%d.attn_k.%s"
 #define TN_ATTN_Q          "%s.blk.%d.attn_q.%s"
@@ -159,6 +166,7 @@ enum projector_type {
     PROJECTOR_TYPE_LDP,
     PROJECTOR_TYPE_LDPV2,
     PROJECTOR_TYPE_RESAMPLER,
+    PROJECTOR_TYPE_MERGER,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -167,6 +175,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_LDP, "ldp" },
     { PROJECTOR_TYPE_LDPV2, "ldpv2"},
     { PROJECTOR_TYPE_RESAMPLER, "resampler"},
+    { PROJECTOR_TYPE_MERGER, "qwen2vl_merger"},
 };
 
 
@@ -459,7 +468,8 @@ struct clip_vision_model {
 
     // embeddings
     struct ggml_tensor * class_embedding;
-    struct ggml_tensor * patch_embeddings;
+    struct ggml_tensor * patch_embeddings_0;
+    struct ggml_tensor * patch_embeddings_1;  // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL)
     struct ggml_tensor * patch_bias;
     struct ggml_tensor * position_embeddings;
 
@@ -549,6 +559,7 @@ struct clip_ctx {
     bool has_vision_encoder  = false;
     bool has_llava_projector = false;
     bool has_minicpmv_projector = false;
+    bool has_qwen2vl_merger = false;
     int minicpmv_version = 2;
 
     struct clip_vision_model vision_model;
@@ -557,6 +568,7 @@ struct clip_ctx {
     float image_mean[3];
     float image_std[3];
     bool use_gelu = false;
+    bool use_silu = false;
     int32_t ftype = 1;
 
     bool has_class_embedding = true;
@@ -602,14 +614,26 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             image_size_height = imgs->data->ny;
         }
     }
+    else if (ctx->has_qwen2vl_merger) {
+        // use the image's native resolution when image is avaible
+        if (is_inf) {
+        // if (imgs->data->nx && imgs->data->ny) {
+            image_size_width  = imgs->data->nx;
+            image_size_height = imgs->data->ny;
+        }
+    }
     const int patch_size           = hparams.patch_size;
     const int num_patches          = ((image_size_width / patch_size) * (image_size_height / patch_size));
+    const int patches_w            = image_size_width / patch_size;
+    const int patches_h            = image_size_height / patch_size;
     const int num_positions        = num_patches + (ctx->has_class_embedding ? 1 : 0);
+    const int num_position_ids     = ctx->has_qwen2vl_merger ? num_positions * 4 : num_positions;
     const int hidden_size          = hparams.hidden_size;
     const int n_head               = hparams.n_head;
     const int d_head               = hidden_size / n_head;
     int n_layer                    = hparams.n_layer;
     const float eps                = hparams.eps;
+    int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
 
     const int batch_size = imgs->size;
 
@@ -630,10 +654,30 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     ggml_set_name(inp_raw, "inp_raw");
     ggml_set_input(inp_raw);
 
-    struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+    struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
 
-    inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
-    inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
+    if (ctx->has_qwen2vl_merger) {
+        GGML_ASSERT(image_size_width % (patch_size * 2) == 0);
+        GGML_ASSERT(image_size_height % (patch_size * 2) == 0);
+
+        auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+        inp = ggml_add(ctx0, inp, inp_1);
+        inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 2, 0, 3));  // [w, h, c, b] -> [c, w, h, b]
+        inp = ggml_reshape_4d(
+            ctx0, inp,
+            hidden_size * 2, patches_w / 2, patches_h, batch_size);
+        inp = ggml_reshape_4d(
+            ctx0, inp,
+            hidden_size * 2, patches_w / 2, 2, batch_size * (patches_h / 2));
+        inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 0, 2, 1, 3));
+        inp = ggml_reshape_3d(
+            ctx0, inp,
+            hidden_size, patches_w * patches_h, batch_size);
+    }
+    else {
+        inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
+        inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
+    }
 
     if (ctx->has_patch_bias) {
         // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
@@ -655,12 +699,14 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
         }
     }
 
-    struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
+    struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids);
     ggml_set_name(positions, "positions");
     ggml_set_input(positions);
 
-    embeddings =
-        ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
+    if (!ctx->has_qwen2vl_merger) { // qwen2vl use rope position embedding
+        embeddings =
+            ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));
+    }
 
     if (ctx->has_minicpmv_projector) {
         int pos_w = image_size_width/patch_size;
@@ -684,7 +730,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
     }
 
     // loop over layers
-    if (ctx->has_minicpmv_projector) {
+    if (ctx->has_minicpmv_projector || ctx->has_qwen2vl_merger) {
+        // TODO: figure out why we doing thing in this way ???
         n_layer += 1;
     }
     for (int il = 0; il < n_layer - 1; il++) {
@@ -706,8 +753,13 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             struct ggml_tensor * Q =
                 ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b);
 
-            Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head));
             Q = ggml_reshape_4d(ctx0, Q, d_head, n_head, num_positions, batch_size);
+            if (ctx->has_qwen2vl_merger) {
+                Q = ggml_rope_multi(
+                    ctx0, Q, positions, nullptr,
+                    d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
+            }
+            Q = ggml_scale_inplace(ctx0, Q, 1.0f / sqrt((float)d_head));
             Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
             Q = ggml_reshape_3d(ctx0, Q, d_head, num_positions, n_head * batch_size);
 
@@ -715,6 +767,11 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
                 ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b);
 
             K = ggml_reshape_4d(ctx0, K, d_head, n_head, num_positions, batch_size);
+            if (ctx->has_qwen2vl_merger) {
+                K = ggml_rope_multi(
+                    ctx0, K, positions, nullptr,
+                    d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
+            }
             K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
             K = ggml_reshape_3d(ctx0, K, d_head, num_positions, n_head * batch_size);
 
@@ -754,6 +811,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
 
         if (ctx->use_gelu) {
             cur = ggml_gelu_inplace(ctx0, cur);
+        } else if (ctx->use_silu) {
+            cur = ggml_silu_inplace(ctx0, cur);
         } else {
             cur = ggml_gelu_quick_inplace(ctx0, cur);
         }
@@ -765,6 +824,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
         cur = ggml_add(ctx0, embeddings, cur);
 
         embeddings = cur;
+
     }
 
     // post-layernorm
@@ -1026,6 +1086,19 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             GGML_ASSERT(false);
         }
     }
+    else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
+        embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size);
+
+        embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
+        embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
+
+        // GELU activation
+        embeddings = ggml_gelu(ctx0, embeddings);
+
+        // Second linear layer
+        embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings);
+        embeddings = ggml_add(ctx0, embeddings, model.mm_1_b);
+    }
 
     // build the graph
     ggml_build_forward_expand(gf, embeddings);
@@ -1169,6 +1242,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
     LOG_INF("%s: CLIP using Vulkan backend\n", __func__);
 #endif
 
+#ifdef GGML_USE_SYCL
+    new_clip->backend = ggml_backend_sycl_init(0);
+    LOG_INF("%s: CLIP using SYCL backend\n", __func__);
+#endif
+
     if (!new_clip->backend) {
         new_clip->backend = ggml_backend_cpu_init();
         LOG_INF("%s: CLIP using CPU backend\n", __func__);
@@ -1197,6 +1275,10 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             new_clip->minicpmv_version = gguf_get_val_i32(ctx, idx);
         }
 
+        idx = gguf_find_key(ctx, KEY_HAS_QWEN2VL_MERGER);
+        if (idx != -1) {
+            new_clip->has_qwen2vl_merger = gguf_get_val_bool(ctx, idx);
+        }
         // GGML_ASSERT(new_clip->has_llava_projector); // see monatis/clip.cpp for image and/or text encoding for semantic search
 
         GGML_ASSERT(new_clip->has_vision_encoder);
@@ -1205,6 +1287,13 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         idx = get_key_idx(ctx, KEY_USE_GELU);
         new_clip->use_gelu = gguf_get_val_bool(ctx, idx);
 
+        try {
+            idx = get_key_idx(ctx, KEY_USE_SILU);
+            new_clip->use_silu = gguf_get_val_bool(ctx, idx);
+        } catch (std::runtime_error & /*e*/) {
+            new_clip->use_silu = false;
+        }
+
         if (verbosity >= 1) {
             LOG_INF("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
             LOG_INF("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
@@ -1380,11 +1469,16 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         }
 
         try {
-            vision_model.patch_embeddings    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
+            vision_model.patch_embeddings_0    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
             vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
         } catch(const std::exception& /*e*/) {
             LOG_ERR("%s: failed to load vision model tensors\n", __func__);
         }
+        try {
+            vision_model.patch_embeddings_1    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD_1);
+        } catch(const std::exception& /*e*/) {
+            new_clip->has_qwen2vl_merger = false;
+        }
 
         // LLaVA projection
         if (new_clip->proj_type == PROJECTOR_TYPE_MLP || new_clip->proj_type == PROJECTOR_TYPE_MLP_NORM) {
@@ -1472,6 +1566,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             vision_model.mm_model_ln_post_w = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "weight"));
             vision_model.mm_model_ln_post_b = get_tensor(new_clip->ctx_data, format(TN_MINICPMV_LN, "post", "bias"));
         }
+        else if (new_clip->proj_type == PROJECTOR_TYPE_MERGER) {
+            vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight"));
+            vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias"));
+            vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
+            vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
+        }
         else {
             std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type];
             throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
@@ -1510,6 +1610,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         new_clip->compute_alloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(new_clip->backend));
         clip_image_f32_batch batch;
         batch.size = 1;
+        batch.data = nullptr;
         ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch, nullptr, false);
         ggml_gallocr_reserve(new_clip->compute_alloc, gf);
         size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
@@ -1523,6 +1624,10 @@ void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size
     ctx_clip->load_image_size = load_image_size;
 }
 
+struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip) {
+    return ctx_clip->load_image_size;
+}
+
 struct clip_image_size * clip_image_size_init() {
     struct clip_image_size * load_image_size = new struct clip_image_size();
     load_image_size->width = 448;
@@ -1975,6 +2080,23 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
         }
         return true;
     }
+    else if (ctx->has_qwen2vl_merger) {
+        clip_image_u8 * resized = clip_image_u8_init();
+        auto patch_size = clip_patch_size(ctx) * 2;
+        int nx = ceil((float)img->nx / patch_size) * patch_size;
+        int ny = ceil((float)img->ny / patch_size) * patch_size;
+        bicubic_resize(*img, *resized, nx, ny);
+
+        res_imgs->data = new clip_image_f32[1];
+        // clip_image_f32 * res = clip_image_f32_init();
+        normalize_image_u8_to_f32(resized, res_imgs->data, ctx->image_mean, ctx->image_std);
+        // res_imgs->data[0] = *res;
+        res_imgs->size = 1;
+
+        // clip_image_f32_free(res);
+        clip_image_u8_free(resized);
+        return true;
+    }
 
     bool pad_to_square = true;
     if (!ctx->has_vision_encoder) {
@@ -2164,6 +2286,13 @@ size_t clip_embd_nbytes(const struct clip_ctx * ctx) {
     return clip_n_patches(ctx) * clip_n_mmproj_embd(ctx) * sizeof(float);
 }
 
+size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w) {
+    clip_image_f32 img;
+    img.nx = img_w;
+    img.ny = img_h;
+    return clip_n_patches_by_img(ctx, &img) * clip_n_mmproj_embd(ctx) * sizeof(float);
+}
+
 int32_t clip_image_size(const struct clip_ctx * ctx) {
     return ctx->vision_model.hparams.image_size;
 }
@@ -2185,6 +2314,13 @@ const int32_t * clip_image_grid(const struct clip_ctx * ctx) {
 }
 
 int clip_n_patches(const struct clip_ctx * ctx) {
+    clip_image_f32 img;
+    img.nx = ctx->vision_model.hparams.image_size;
+    img.ny = ctx->vision_model.hparams.image_size;
+    return clip_n_patches_by_img(ctx, &img);
+}
+
+int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
     const auto & params = ctx->vision_model.hparams;
 
     int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
@@ -2198,6 +2334,11 @@ int clip_n_patches(const struct clip_ctx * ctx) {
         else if (ctx->minicpmv_version == 3) {
             n_patches = 64;
         }
+    } else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
+        int patch_size = params.patch_size * 2;
+        int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0);
+        int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0);
+        n_patches = x_patch * y_patch;
     }
 
     return n_patches;
@@ -2326,7 +2467,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     const int image_size = hparams.image_size;
     int image_size_width  = image_size;
     int image_size_height = image_size;
-    if (ctx->has_minicpmv_projector) {
+    if (ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger) {
         image_size_width  = imgs->data[0].nx;
         image_size_height = imgs->data[0].ny;
     }
@@ -2346,7 +2487,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
         for (size_t i = 0; i < imgs->size; i++) {
             const int nx = imgs->data[i].nx;
             const int ny = imgs->data[i].ny;
-            if (!ctx->has_minicpmv_projector) {
+            if (!(ctx->has_minicpmv_projector | ctx->has_qwen2vl_merger)) {
                 GGML_ASSERT(nx == image_size && ny == image_size);
             }
 
@@ -2404,9 +2545,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             auto pos_embed_t = get_2d_sincos_pos_embed(embed_dim, std::make_pair(pos_w, pos_h));
 
             float * pos_embed_data = (float *)malloc(ggml_nbytes(pos_embed));
-            for(int i=0;i<pos_w * pos_h;++i){
-                for(int j=0;j<embed_dim;++j){
-                    pos_embed_data[i*embed_dim+j]=pos_embed_t[i][j];
+            for(int i=0;i < pos_w * pos_h; ++i){
+                for(int j=0; j < embed_dim; ++j){
+                    pos_embed_data[i * embed_dim + j] = pos_embed_t[i][j];
                 }
             }
 
@@ -2426,7 +2567,34 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             }
         }
 
-        {
+        if (ctx->has_qwen2vl_merger) {
+            struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
+
+            const int pw = image_size_width / patch_size;
+            const int ph = image_size_height / patch_size;
+            int* positions_data = (int*)malloc(ggml_nbytes(positions));
+
+            int ptr = 0;
+            for (int y = 0; y < ph; y+=2)
+            {
+                for (int x = 0; x < pw; x+=2)
+                {
+                    for (int dy = 0; dy < 2; dy++) {
+                        for (int dx = 0; dx < 2; dx++) {
+                            positions_data[ptr]                 = y + dy;
+                            positions_data[num_patches + ptr]     = x + dx;
+                            positions_data[num_patches * 2 + ptr] = y + dy;
+                            positions_data[num_patches * 3 + ptr] = x + dx;
+                            ptr++;
+                        }
+                    }
+                }
+            }
+
+            ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
+            free(positions_data);
+        }
+        else {
             struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");
 
             int* positions_data = (int*)malloc(ggml_nbytes(positions));
@@ -2435,16 +2603,16 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             }
             ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
             free(positions_data);
-        }
 
-        {
-            struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
-            int* patches_data = (int*)malloc(ggml_nbytes(patches));
-            for (int i = 0; i < num_patches; i++) {
-                patches_data[i] = i + 1;
+            {
+                struct ggml_tensor * patches = ggml_graph_get_tensor(gf, "patches");
+                int* patches_data = (int*)malloc(ggml_nbytes(patches));
+                for (int i = 0; i < num_patches; i++) {
+                    patches_data[i] = i + 1;
+                }
+                ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
+                free(patches_data);
             }
-            ggml_backend_tensor_set(patches, patches_data, 0, ggml_nbytes(patches));
-            free(patches_data);
         }
     }
 
@@ -2617,6 +2785,9 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
             return 3584;
         }
     }
+    if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
+        return ctx->vision_model.mm_1_b->ne[0];
+    }
 
     std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type];
     throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
@@ -2628,3 +2799,21 @@ int clip_is_minicpmv(const struct clip_ctx * ctx) {
     }
     return 0;
 }
+
+bool clip_is_qwen2vl(const struct clip_ctx * ctx) {
+    return ctx->has_qwen2vl_merger;
+}
+
+
+bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec) {
+    clip_image_f32 clip_img;
+    clip_img.buf.resize(h * w * 3);
+    for (int i = 0; i < h*w*3; i++)
+    {
+        clip_img.buf[i] = img[i];
+    }
+    clip_img.nx = w;
+    clip_img.ny = h;
+    clip_image_encode(ctx, n_threads, &clip_img, vec);
+    return true;
+}

examples/llava/clip.h

@@ -45,6 +45,7 @@ CLIP_API struct clip_ctx * clip_model_load_cpu(const char * fname, int verbosity
 CLIP_API void clip_free(struct clip_ctx * ctx);
 
 CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
+CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w);
 
 CLIP_API int32_t clip_image_size (const struct clip_ctx * ctx);
 CLIP_API int32_t clip_patch_size (const struct clip_ctx * ctx);
@@ -55,11 +56,13 @@ CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx);
 
 CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx);
 
-CLIP_API int clip_n_patches    (const struct clip_ctx * ctx);
-CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
+CLIP_API int clip_n_patches        (const struct clip_ctx * ctx);
+CLIP_API int clip_n_patches_by_img (const struct clip_ctx * ctx, struct clip_image_f32 * img);
+CLIP_API int clip_n_mmproj_embd    (const struct clip_ctx * ctx);
 
 CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip);
 CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size);
+CLIP_API struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip);
 
 CLIP_API struct clip_image_size * clip_image_size_init();
 CLIP_API struct clip_image_u8  * clip_image_u8_init ();
@@ -86,6 +89,9 @@ CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, cons
 CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype);
 
 CLIP_API int clip_is_minicpmv(const struct clip_ctx * ctx);
+CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx);
+
+CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);
 
 #ifdef __cplusplus
 }

examples/llava/llava.cpp

@@ -259,25 +259,33 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
 
     const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip);
 
-    if (clip_is_minicpmv(ctx_clip)) {
+    if (clip_is_minicpmv(ctx_clip) || clip_is_qwen2vl(ctx_clip)) {
         std::vector<float *> image_embd_v;
         image_embd_v.resize(img_res_v.size);
         struct clip_image_size * load_image_size = clip_image_size_init();
+
         for (size_t i = 0; i < img_res_v.size; i++) {
             const int64_t t_img_enc_step_start_us = ggml_time_us();
-            image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip));
+            image_embd_v[i] = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img_res_v.data[i].nx, img_res_v.data[i].ny));
             int patch_size=14;
             load_image_size->width = img_res_v.data[i].nx;
             load_image_size->height = img_res_v.data[i].ny;
             clip_add_load_image_size(ctx_clip, load_image_size);
+
             bool encoded = false;
-            int has_minicpmv_projector = clip_is_minicpmv(ctx_clip);
-            if (has_minicpmv_projector == 2) {
-                encoded = clip_image_encode(ctx_clip, n_threads, only_v2_5_reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
-            }
-            else if (has_minicpmv_projector == 3) {
+            if (clip_is_qwen2vl(ctx_clip)) {
                 encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]);
             }
+            else {
+                int has_minicpmv_projector = clip_is_minicpmv(ctx_clip);
+                if (has_minicpmv_projector == 2) {
+                    encoded = clip_image_encode(ctx_clip, n_threads, only_v2_5_reshape_by_patch(&img_res_v.data[i], patch_size), image_embd_v[i]);
+                }
+                else if (has_minicpmv_projector == 3) {
+                    encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]);
+                }
+            }
+
             if (!encoded) {
                 LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
                 return false;
@@ -290,8 +298,11 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
 
         int n_img_pos_out = 0;
         for (size_t i = 0; i < image_embd_v.size(); i++) {
-            std::memcpy(image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip), image_embd_v[i], clip_embd_nbytes(ctx_clip));
-            n_img_pos_out += clip_n_patches(ctx_clip);
+            std::memcpy(
+                image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip),
+                image_embd_v[i],
+                clip_embd_nbytes_by_img(ctx_clip, img_res_v.data[i].nx, img_res_v.data[i].ny));
+            n_img_pos_out += clip_n_patches_by_img(ctx_clip, &img_res_v.data[i]);
         }
         *n_img_pos = n_img_pos_out;
         for (size_t i = 0; i < image_embd_v.size(); i++) {
@@ -387,7 +398,13 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co
     if (clip_is_minicpmv(ctx_clip)) {
         num_max_patches = 10;
     }
-    float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model
+    float * image_embd;
+    if (clip_is_qwen2vl(ctx_clip)) {
+        // qwen2vl don't split image into chunks, so `num_max_patches` is not needed.
+        image_embd = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img->nx, img->ny));
+    } else {
+        image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model
+    }
     if (!image_embd) {
         LOG_ERR("Unable to allocate memory for image embeddings\n");
         return false;

examples/llava/qwen2_vl_surgery.py

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

examples/llava/qwen2vl-cli.cpp

@@ -0,0 +1,581 @@
+#include "arg.h"
+#include "base64.hpp"
+#include "log.h"
+#include "common.h"
+#include "sampling.h"
+#include "clip.h"
+#include "llava.h"
+#include "llama.h"
+#include "ggml.h"
+
+#ifdef GGML_USE_CUDA
+#include "ggml-cuda.h"
+#endif
+#ifdef NDEBUG
+#include "ggml-alloc.h"
+#include "ggml-backend.h"
+#endif
+
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+#include <vector>
+#include <algorithm>
+#include <iostream>
+#include <fstream>
+
+
+static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed,
+                                     int n_batch, int * n_past, int * st_pos_id, struct clip_image_size * image_size) {
+    int n_embd  = llama_n_embd(llama_get_model(ctx_llama));
+    const int patch_size = 14 * 2;
+    const int ph = image_size->height / patch_size + (image_size->height % patch_size > 0);
+    const int pw = image_size->width / patch_size + (image_size->width % patch_size > 0);
+    auto img_tokens = image_embed->n_image_pos;
+    // llama_pos mrope_pos[img_tokens * 4];
+    std::vector<llama_pos> mrope_pos;
+    mrope_pos.resize(img_tokens * 4);
+
+    for (int y = 0; y < ph; y++)
+    {
+        for (int x = 0; x < pw; x++)
+        {
+            int i = y * pw + x;
+            mrope_pos[i] = *st_pos_id;
+            mrope_pos[i + img_tokens] = *st_pos_id + y;
+            mrope_pos[i + img_tokens * 2] = *st_pos_id + x;
+            mrope_pos[i + img_tokens * 3] = 0;
+        }
+    }
+    *st_pos_id += std::max(pw, ph);
+
+    int processed = 0;
+    std::vector<llama_pos> batch_mrope_pos;
+    batch_mrope_pos.resize(img_tokens * 4);
+
+    for (int i = 0; i < img_tokens; i += n_batch) {
+        int n_eval = img_tokens - i;
+        if (n_eval > n_batch) {
+            n_eval = n_batch;
+        }
+
+        // llama_pos batch_mrope_pos[n_eval * 4];
+        std::fill(batch_mrope_pos.begin(), batch_mrope_pos.end(), 0);
+        memcpy(batch_mrope_pos.data(), &mrope_pos[processed], n_eval * sizeof(llama_pos));
+        memcpy(&batch_mrope_pos[n_eval * 1], &mrope_pos[img_tokens * 1 + processed], n_eval * sizeof(llama_pos));
+        memcpy(&batch_mrope_pos[n_eval * 2], &mrope_pos[img_tokens * 2 + processed], n_eval * sizeof(llama_pos));
+        memcpy(&batch_mrope_pos[n_eval * 3], &mrope_pos[img_tokens * 3 + processed], n_eval * sizeof(llama_pos));
+
+        llama_batch batch = {
+            int32_t(n_eval),                // n_tokens
+            nullptr,                        // token
+            (image_embed->embed+i*n_embd),  // embed
+            batch_mrope_pos.data(),         // pos
+            nullptr,  // n_seq_id
+            nullptr,  // seq_id
+            nullptr,  // logits
+        };
+
+        if (llama_decode(ctx_llama, batch)) {
+            LOG_ERR("%s : failed to eval\n", __func__);
+            return false;
+        }
+        *n_past += n_eval;
+        processed += n_eval;
+    }
+    return true;
+}
+
+
+static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past, int * st_pos_id) {
+    int N = (int) tokens.size();
+    std::vector<llama_pos> pos;
+    for (int i = 0; i < N; i += n_batch) {
+        int n_eval = (int) tokens.size() - i;
+        if (n_eval > n_batch) {
+            n_eval = n_batch;
+        }
+        auto batch = llama_batch_get_one(&tokens[i], n_eval);
+        // TODO: add mrope pos ids somewhere else
+        pos.resize(batch.n_tokens * 4);
+        std::fill(pos.begin(), pos.end(), 0);
+        for (int j = 0; j < batch.n_tokens * 3; j ++) {
+            pos[j] = *st_pos_id + (j % batch.n_tokens);
+        }
+        batch.pos = pos.data();
+
+        if (llama_decode(ctx_llama, batch)) {
+            LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
+            return false;
+        }
+        *n_past += n_eval;
+        *st_pos_id += n_eval;
+    }
+    return true;
+}
+
+static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past, int * st_pos_id) {
+    std::vector<llama_token> tokens;
+    tokens.push_back(id);
+    return eval_tokens(ctx_llama, tokens, 1, n_past, st_pos_id);
+}
+
+static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, int * st_pos_id, bool add_bos){
+    std::string              str2     = str;
+    std::vector<llama_token> embd_inp = common_tokenize(ctx_llama, str2, add_bos, true);
+    eval_tokens(ctx_llama, embd_inp, n_batch, n_past, st_pos_id);
+    return true;
+}
+
+static const char * sample(struct common_sampler * smpl,
+                           struct llama_context * ctx_llama,
+                           int * n_past, int * st_pos_id) {
+    const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
+    common_sampler_accept(smpl, id, true);
+    static std::string ret;
+    if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
+        ret = "</s>";
+    } else {
+        ret = common_token_to_piece(ctx_llama, id);
+    }
+    eval_id(ctx_llama, id, n_past, st_pos_id);
+    return ret.c_str();
+}
+
+static const char* IMG_BASE64_TAG_BEGIN = "<img src=\"data:image/jpeg;base64,";
+static const char* IMG_BASE64_TAG_END = "\">";
+
+static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) {
+    begin_out = prompt.find(IMG_BASE64_TAG_BEGIN);
+    end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out);
+}
+
+static bool prompt_contains_image(const std::string& prompt) {
+    size_t begin, end;
+    find_image_tag_in_prompt(prompt, begin, end);
+    return (begin != std::string::npos);
+}
+
+// replaces the base64 image tag in the prompt with `replacement`
+static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) {
+    size_t img_base64_str_start, img_base64_str_end;
+    find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
+    if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
+        LOG_ERR("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
+        return NULL;
+    }
+
+    auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN);
+    auto base64_bytes_count = img_base64_str_end - base64_bytes_start;
+    auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count );
+
+    auto required_bytes = base64::required_encode_size(base64_str.size());
+    auto img_bytes = std::vector<unsigned char>(required_bytes);
+    base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin());
+
+    auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
+    if (!embed) {
+        LOG_ERR("%s: could not load image from base64 string.\n", __func__);
+        return NULL;
+    }
+
+    return embed;
+}
+
+static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") {
+    size_t begin, end;
+    find_image_tag_in_prompt(prompt, begin, end);
+    if (begin == std::string::npos || end == std::string::npos) {
+        return prompt;
+    }
+    auto pre = prompt.substr(0, begin);
+    auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END));
+    return pre + replacement + post;
+}
+
+struct llava_context {
+    struct clip_ctx * ctx_clip = NULL;
+    struct llama_context * ctx_llama = NULL;
+    struct llama_model * model = NULL;
+};
+
+static void print_usage(int, char ** argv) {
+    LOG("\n example usage:\n");
+    LOG("\n     %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
+}
+
+static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) {
+
+    // load and preprocess the image
+    llava_image_embed * embed = NULL;
+    auto prompt = params->prompt;
+    if (prompt_contains_image(prompt)) {
+        if (!params->image.empty()) {
+            LOG_INF("using base64 encoded image instead of command line image path\n");
+        }
+        embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt);
+        if (!embed) {
+            LOG_ERR("%s: can't load image from prompt\n", __func__);
+            return NULL;
+        }
+        params->prompt = remove_image_from_prompt(prompt);
+    } else {
+        embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str());
+        if (!embed) {
+            fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str());
+            return NULL;
+        }
+    }
+
+    return embed;
+}
+
+static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) {
+    int n_past = 0;
+    int cur_pos_id = 0;
+
+    const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
+
+    std::string system_prompt, user_prompt;
+    size_t image_pos = prompt.find("<|vision_start|>");
+    if (image_pos != std::string::npos) {
+        // new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
+        system_prompt = prompt.substr(0, image_pos);
+        user_prompt = prompt.substr(image_pos + std::string("<|vision_pad|>").length());
+        LOG_INF("system_prompt: %s\n", system_prompt.c_str());
+        if (params->verbose_prompt) {
+            auto tmp = common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
+            for (int i = 0; i < (int) tmp.size(); i++) {
+                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+            }
+        }
+        LOG_INF("user_prompt: %s\n", user_prompt.c_str());
+        if (params->verbose_prompt) {
+            auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
+            for (int i = 0; i < (int) tmp.size(); i++) {
+                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+            }
+        }
+    } else {
+        // llava-1.5 native mode
+        system_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|>";
+        user_prompt = "<|vision_end|>" + prompt + "<|im_end|>\n<|im_start|>assistant\n";
+        if (params->verbose_prompt) {
+            auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
+            for (int i = 0; i < (int) tmp.size(); i++) {
+                LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+            }
+        }
+    }
+
+    eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, true);
+    if (image_embed != nullptr) {
+        auto image_size = clip_get_load_image_size(ctx_llava->ctx_clip);
+        qwen2vl_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past, &cur_pos_id, image_size);
+    }
+    eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, false);
+
+    // generate the response
+
+    LOG("\n");
+
+    struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling);
+    if (!smpl) {
+        LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
+        exit(1);
+    }
+
+    std::string response = "";
+    for (int i = 0; i < max_tgt_len; i++) {
+        const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past, &cur_pos_id);
+        response += tmp;
+        if (strcmp(tmp, "</s>") == 0) break;
+        if (strstr(tmp, "###")) break; // Yi-VL behavior
+        LOG("%s", tmp);
+        if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works)
+        if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6
+        if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6
+
+        fflush(stdout);
+    }
+
+    common_sampler_free(smpl);
+    LOG("\n");
+}
+
+static struct llama_model * llava_init(common_params * params) {
+    llama_backend_init();
+    llama_numa_init(params->numa);
+
+    llama_model_params model_params = common_model_params_to_llama(*params);
+
+    llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
+    if (model == NULL) {
+        LOG_ERR("%s: unable to load model\n" , __func__);
+        return NULL;
+    }
+    return model;
+}
+
+static struct llava_context * llava_init_context(common_params * params, llama_model * model) {
+    const char * clip_path = params->mmproj.c_str();
+
+    auto prompt = params->prompt;
+    if (prompt.empty()) {
+        prompt = "describe the image in detail.";
+    }
+
+    auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
+
+
+    llama_context_params ctx_params = common_context_params_to_llama(*params);
+    ctx_params.n_ctx           = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
+
+    llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
+
+    if (ctx_llama == NULL) {
+        LOG_ERR("%s: failed to create the llama_context\n" , __func__);
+        return NULL;
+    }
+
+    auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context));
+
+    ctx_llava->ctx_llama = ctx_llama;
+    ctx_llava->ctx_clip = ctx_clip;
+    ctx_llava->model = model;
+    return ctx_llava;
+}
+
+static void llava_free(struct llava_context * ctx_llava) {
+    if (ctx_llava->ctx_clip) {
+        clip_free(ctx_llava->ctx_clip);
+        ctx_llava->ctx_clip = NULL;
+    }
+
+    llama_free(ctx_llava->ctx_llama);
+    llama_free_model(ctx_llava->model);
+    llama_backend_free();
+}
+
+#ifndef NDEBUG
+
+static void debug_test_mrope_2d() {
+    // 1. Initialize backend
+    ggml_backend_t backend = NULL;
+    std::string backend_name = "";
+#ifdef GGML_USE_CUDA
+    fprintf(stderr, "%s: using CUDA backend\n", __func__);
+    backend = ggml_backend_cuda_init(0); // init device 0
+    backend_name = "cuda";
+    if (!backend) {
+        fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
+    }
+#endif
+    // if there aren't GPU Backends fallback to CPU backend
+    if (!backend) {
+        backend = ggml_backend_cpu_init();
+        backend_name = "cpu";
+    }
+
+    // Calculate the size needed to allocate
+    size_t ctx_size = 0;
+    ctx_size += 2 * ggml_tensor_overhead(); // tensors
+    // no need to allocate anything else!
+
+    // 2. Allocate `ggml_context` to store tensor data
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ ctx_size,
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true, // the tensors will be allocated later by ggml_backend_alloc_ctx_tensors()
+    };
+    struct ggml_context * ctx = ggml_init(params);
+
+    struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 128, 12, 30);
+    ggml_set_name(inp_raw, "inp_raw");
+    ggml_set_input(inp_raw);
+
+    struct ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 30 * 4);
+    ggml_set_name(pos, "pos");
+    ggml_set_input(pos);
+
+    std::vector<float> dummy_q;
+    dummy_q.resize(128 * 12 * 30);
+    std::fill(dummy_q.begin(), dummy_q.end(), 0.1);
+    // memcpy(inp_raw->data, dummy_q.data(), 128 * 12 * 30 * ggml_element_size(inp_raw));
+
+    std::vector<int> pos_id;
+    pos_id.resize(30 * 4);
+    for (int i = 0; i < 30; i ++) {
+        pos_id[i] = i;
+        pos_id[i + 30] = i + 10;
+        pos_id[i + 60] = i + 20;
+        pos_id[i + 90] = i + 30;
+    }
+    int sections[4] = {32, 32, 0, 0};
+
+    // 4. Allocate a `ggml_backend_buffer` to store all tensors
+    ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
+
+    // 5. Copy tensor data from main memory (RAM) to backend buffer
+    ggml_backend_tensor_set(inp_raw, dummy_q.data(), 0, ggml_nbytes(inp_raw));
+    ggml_backend_tensor_set(pos, pos_id.data(), 0, ggml_nbytes(pos));
+
+    // 6. Create a `ggml_cgraph` for mul_mat operation
+    struct ggml_cgraph * gf = NULL;
+    struct ggml_context * ctx_cgraph = NULL;
+
+    // create a temporally context to build the graph
+    struct ggml_init_params params0 = {
+        /*.mem_size   =*/ ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(),
+        /*.mem_buffer =*/ NULL,
+        /*.no_alloc   =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
+    };
+    ctx_cgraph = ggml_init(params0);
+    gf = ggml_new_graph(ctx_cgraph);
+
+    struct ggml_tensor * result0 = ggml_rope_multi(
+        ctx_cgraph, inp_raw, pos, nullptr,
+        128/2, sections, LLAMA_ROPE_TYPE_VISION, 32768, 1000000, 1,
+        0, 1, 32, 1);
+
+    // Add "result" tensor and all of its dependencies to the cgraph
+    ggml_build_forward_expand(gf, result0);
+
+    // 7. Create a `ggml_gallocr` for cgraph computation
+    ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
+    ggml_gallocr_alloc_graph(allocr, gf);
+
+    // 9. Run the computation
+    int n_threads = 1; // Optional: number of threads to perform some operations with multi-threading
+    if (ggml_backend_is_cpu(backend)) {
+        ggml_backend_cpu_set_n_threads(backend, n_threads);
+    }
+    ggml_backend_graph_compute(backend, gf);
+
+    // 10. Retrieve results (output tensors)
+    // in this example, output tensor is always the last tensor in the graph
+    struct ggml_tensor * result = result0;
+    // struct ggml_tensor * result = gf->nodes[gf->n_nodes - 1];
+    float * result_data = (float *)malloc(ggml_nbytes(result));
+    // because the tensor data is stored in device buffer, we need to copy it back to RAM
+    ggml_backend_tensor_get(result, result_data, 0, ggml_nbytes(result));
+    const std::string bin_file = "mrope_2d_" + backend_name +".bin";
+    std::ofstream outFile(bin_file, std::ios::binary);
+
+    if (outFile.is_open()) {
+        outFile.write(reinterpret_cast<const char*>(result_data), ggml_nbytes(result));
+        outFile.close();
+        std::cout << "Data successfully written to " + bin_file << std::endl;
+    } else {
+        std::cerr << "Error opening file!" << std::endl;
+    }
+
+    free(result_data);
+    // 11. Free memory and exit
+    ggml_free(ctx_cgraph);
+    ggml_gallocr_free(allocr);
+    ggml_free(ctx);
+    ggml_backend_buffer_free(buffer);
+    ggml_backend_free(backend);
+}
+
+static void debug_dump_img_embed(struct llava_context * ctx_llava) {
+    int n_embd  = llama_n_embd(llama_get_model(ctx_llava->ctx_llama));
+    int ne = n_embd * 4;
+    float vals[56 * 56 * 3];
+    // float embd[ne];
+    std::vector<float> embd;
+    embd.resize(ne);
+
+    for (int i = 0; i < 56*56; i++)
+    {
+        for (int c = 0; c < 3; c++)
+            vals[i * 3 + c] = (float)(i % (56 * 56)) / (56*56);
+    }
+
+    clip_encode_float_image(ctx_llava->ctx_clip, 16, vals, 56, 56, embd.data());
+
+    std::ofstream outFile("img_embed.bin", std::ios::binary);
+    if (outFile.is_open()) {
+        outFile.write(reinterpret_cast<const char*>(embd.data()), ne * sizeof(float));
+
+        outFile.close();
+        std::cout << "Data successfully written to mrope.bin" << std::endl;
+    } else {
+        std::cerr << "Error opening file!" << std::endl;
+    }
+}
+
+#endif
+
+
+int main(int argc, char ** argv) {
+    ggml_time_init();
+
+    common_params params;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
+        return 1;
+    }
+
+    common_init();
+
+    if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
+        print_usage(argc, argv);
+        return 1;
+    }
+
+    auto * model = llava_init(&params);
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: failed to init llava model\n", __func__);
+        return 1;
+    }
+
+    if (prompt_contains_image(params.prompt)) {
+        auto * ctx_llava = llava_init_context(&params, model);
+
+        auto * image_embed = load_image(ctx_llava, &params, "");
+
+        // process the prompt
+        process_prompt(ctx_llava, image_embed, &params, params.prompt);
+
+        llama_perf_context_print(ctx_llava->ctx_llama);
+        llava_image_embed_free(image_embed);
+        ctx_llava->model = NULL;
+        llava_free(ctx_llava);
+#ifndef NDEBUG
+    } else if (params.image[0].empty()) {
+        auto ctx_llava = llava_init_context(&params, model);
+
+        debug_test_mrope_2d();
+        debug_dump_img_embed(ctx_llava);
+
+        llama_perf_context_print(ctx_llava->ctx_llama);
+        ctx_llava->model = NULL;
+        llava_free(ctx_llava);
+#endif
+    } else {
+        for (auto & image : params.image) {
+            auto * ctx_llava = llava_init_context(&params, model);
+
+            auto * image_embed = load_image(ctx_llava, &params, image);
+            if (!image_embed) {
+                LOG_ERR("%s: failed to load image %s. Terminating\n\n", __func__, image.c_str());
+                return 1;
+            }
+
+            // process the prompt
+            process_prompt(ctx_llava, image_embed, &params, params.prompt);
+
+            llama_perf_context_print(ctx_llava->ctx_llama);
+            llava_image_embed_free(image_embed);
+            ctx_llava->model = NULL;
+            llava_free(ctx_llava);
+        }
+    }
+
+    llama_free_model(model);
+
+    return 0;
+}

examples/main/README.md

@@ -66,7 +66,7 @@ In this section, we cover the most commonly used options for running the `llama-
 -   `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g [https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true](https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true)).
 -   `-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 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
+-   `-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.
 -   `-mli, --multiline-input`: Allows you to write or paste multiple lines without ending each in '\'
 -   `-t N, --threads N`: Set the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has.
 -   `-ngl N, --n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
@@ -131,7 +131,7 @@ During text generation, LLaMA models have a limited context size, which means th
 
 ### Context Size
 
-- `-c N, --ctx-size N`: Set the size of the prompt context (default: 0, 0 = loaded from model). The LLaMA models were built with a context of 2048-8192, which will yield the best results on longer input/inference.
+- `-c N, --ctx-size N`: Set the size of the prompt context (default: 4096, 0 = loaded from model). If a LLaMA model was built with a longer context, increasing this value will yield the best results on longer input/inference.
 
 ### Extended Context Size
 
@@ -177,16 +177,11 @@ Example usage: `--temp 0`
 
 -   `--repeat-penalty N`: Control the repetition of token sequences in the generated text default: 1.0, 1.0 = disabled).
 -   `--repeat-last-n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
--   `--no-penalize-nl`: Disable penalization for newline tokens when applying the repeat penalty.
 
 The `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.
 
 The `repeat-last-n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty, and a value of -1 sets the number of tokens considered equal to the context size (`ctx-size`).
 
-Use the `--no-penalize-nl` option to disable newline penalization when applying the repeat penalty. This option is particularly useful for generating chat conversations, dialogues, code, poetry, or any text where newline tokens play a significant role in structure and formatting. Disabling newline penalization helps maintain the natural flow and intended formatting in these specific use cases.
-
-Example usage: `--repeat-penalty 1.15 --repeat-last-n 128 --no-penalize-nl`
-
 ### DRY Repetition Penalty
 
 DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repetition in generated text even across long contexts by penalizing tokens based on their recent usage patterns (original [PR link](https://github.com/oobabooga/text-generation-webui/pull/5677)).
@@ -348,6 +343,7 @@ These options provide extra functionality and customization when running the LLa
 
 -   `-h, --help`: Display a help message showing all available options and their default values. This is particularly useful for checking the latest options and default values, as they can change frequently, and the information in this document may become outdated.
 -   `--verbose-prompt`: Print the prompt before generating text.
+-   `--no-display-prompt`: Don't print prompt at generation.
 -   `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used.
 -   `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance.
 -   `-hfr URL --hf-repo URL`: The url to the Hugging Face model repository. Used in conjunction with `--hf-file` or `-hff`. The model is downloaded and stored in the file provided by `-m` or `--model`. If `-m` is not provided, the model is auto-stored in the path specified by the `LLAMA_CACHE` environment variable  or in an OS-specific local cache.

examples/quantize/README.md

@@ -54,8 +54,6 @@ As the models are currently fully loaded into memory, you will need adequate dis
 
 Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
 
-The quantization formats `Q4_0_4_4`, `Q4_0_4_8` and `Q4_0_8_8` are block interleaved variants of the `Q4_0` format, providing a data layout that is better suited for specific implementations of optimized mulmat kernels. Since these formats differ only in data layout, they have the same quantized size as the `Q4_0` format.
-
 *(outdated)*
 
 | Model | Measure      |    F16 |   Q4_0 |   Q4_1 |   Q5_0 |   Q5_1 |   Q8_0 |
@@ -83,7 +81,7 @@ The quantization formats `Q4_0_4_4`, `Q4_0_4_8` and `Q4_0_8_8` are block interle
   - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930)
   - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957)
   - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969)
-  - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
+  - [#4996 - k-quants tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
   - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060)
   - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196)
   - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361)

examples/quantize/quantize.cpp

@@ -48,9 +48,6 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "Q5_K_M",   LLAMA_FTYPE_MOSTLY_Q5_K_M,   " 5.33G, +0.0569 ppl @ Llama-3-8B",  },
     { "Q6_K",     LLAMA_FTYPE_MOSTLY_Q6_K,     " 6.14G, +0.0217 ppl @ Llama-3-8B",  },
     { "Q8_0",     LLAMA_FTYPE_MOSTLY_Q8_0,     " 7.96G, +0.0026 ppl @ Llama-3-8B",  },
-    { "Q4_0_4_4", LLAMA_FTYPE_MOSTLY_Q4_0_4_4, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
-    { "Q4_0_4_8", LLAMA_FTYPE_MOSTLY_Q4_0_4_8, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
-    { "Q4_0_8_8", LLAMA_FTYPE_MOSTLY_Q4_0_8_8, " 4.34G, +0.4685 ppl @ Llama-3-8B",  },
     { "F16",      LLAMA_FTYPE_MOSTLY_F16,      "14.00G, +0.0020 ppl @ Mistral-7B",  },
     { "BF16",     LLAMA_FTYPE_MOSTLY_BF16,     "14.00G, -0.0050 ppl @ Mistral-7B",  },
     { "F32",      LLAMA_FTYPE_ALL_F32,         "26.00G              @ 7B",          },

examples/retrieval/retrieval.cpp

@@ -143,7 +143,7 @@ int main(int argc, char ** argv) {
         std::vector<chunk> file_chunk = chunk_file(context_file, params.chunk_size, params.chunk_separator);
         chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end());
     }
-    LOG_INF("Number of chunks: %ld\n", chunks.size());
+    LOG_INF("Number of chunks: %zu\n", chunks.size());
 
     llama_backend_init();
     llama_numa_init(params.numa);

examples/run/CMakeLists.txt

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

examples/run/README.md

@@ -3,5 +3,45 @@
 The purpose of this example is to demonstrate a minimal usage of llama.cpp for running models.
 
 ```bash
-./llama-run Meta-Llama-3.1-8B-Instruct.gguf
+llama-run granite-code
+...
+
+```bash
+llama-run -h
+Description:
+  Runs a llm
+
+Usage:
+  llama-run [options] model [prompt]
+
+Options:
+  -c, --context-size <value>
+      Context size (default: 2048)
+  -n, --ngl <value>
+      Number of GPU layers (default: 0)
+  -h, --help
+      Show help message
+
+Commands:
+  model
+      Model is a string with an optional prefix of
+      huggingface:// (hf://), ollama://, https:// or file://.
+      If no protocol is specified and a file exists in the specified
+      path, file:// is assumed, otherwise if a file does not exist in
+      the specified path, ollama:// is assumed. Models that are being
+      pulled are downloaded with .partial extension while being
+      downloaded and then renamed as the file without the .partial
+      extension when complete.
+
+Examples:
+  llama-run llama3
+  llama-run ollama://granite-code
+  llama-run ollama://smollm:135m
+  llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf
+  llama-run huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf
+  llama-run https://example.com/some-file1.gguf
+  llama-run some-file2.gguf
+  llama-run file://some-file3.gguf
+  llama-run --ngl 99 some-file4.gguf
+  llama-run --ngl 99 some-file5.gguf Hello World
 ...

examples/run/run.cpp

@@ -1,128 +1,350 @@
 #if defined(_WIN32)
-#include <windows.h>
+#    include <windows.h>
 #else
-#include <unistd.h>
+#    include <unistd.h>
 #endif
 
-#include <climits>
+#if defined(LLAMA_USE_CURL)
+#    include <curl/curl.h>
+#endif
+
+#include <cstdarg>
 #include <cstdio>
 #include <cstring>
+#include <filesystem>
 #include <iostream>
 #include <sstream>
 #include <string>
-#include <unordered_map>
 #include <vector>
 
+#include "common.h"
+#include "json.hpp"
 #include "llama-cpp.h"
 
-typedef std::unique_ptr<char[]> char_array_ptr;
+#define printe(...)                   \
+    do {                              \
+        fprintf(stderr, __VA_ARGS__); \
+    } while (0)
 
-struct Argument {
-    std::string flag;
-    std::string help_text;
-};
+class Opt {
+  public:
+    int init(int argc, const char ** argv) {
+        construct_help_str_();
+        // Parse arguments
+        if (parse(argc, argv)) {
+            printe("Error: Failed to parse arguments.\n");
+            help();
+            return 1;
+        }
 
-struct Options {
-    std::string model_path, prompt_non_interactive;
-    int ngl = 99;
-    int n_ctx = 2048;
-};
+        // If help is requested, show help and exit
+        if (help_) {
+            help();
+            return 2;
+        }
 
-class ArgumentParser {
-   public:
-    ArgumentParser(const char * program_name) : program_name(program_name) {}
-
-    void add_argument(const std::string & flag, std::string & var, const std::string & help_text = "") {
-        string_args[flag] = &var;
-        arguments.push_back({flag, help_text});
+        return 0;  // Success
     }
 
-    void add_argument(const std::string & flag, int & var, const std::string & help_text = "") {
-        int_args[flag] = &var;
-        arguments.push_back({flag, help_text});
+    std::string model_;
+    std::string user_;
+    int         context_size_ = 2048, ngl_ = -1;
+
+  private:
+    std::string help_str_;
+    bool        help_ = false;
+
+    void construct_help_str_() {
+        help_str_ =
+            "Description:\n"
+            "  Runs a llm\n"
+            "\n"
+            "Usage:\n"
+            "  llama-run [options] model [prompt]\n"
+            "\n"
+            "Options:\n"
+            "  -c, --context-size <value>\n"
+            "      Context size (default: " +
+            std::to_string(context_size_);
+        help_str_ +=
+            ")\n"
+            "  -n, --ngl <value>\n"
+            "      Number of GPU layers (default: " +
+            std::to_string(ngl_);
+        help_str_ +=
+            ")\n"
+            "  -h, --help\n"
+            "      Show help message\n"
+            "\n"
+            "Commands:\n"
+            "  model\n"
+            "      Model is a string with an optional prefix of \n"
+            "      huggingface:// (hf://), ollama://, https:// or file://.\n"
+            "      If no protocol is specified and a file exists in the specified\n"
+            "      path, file:// is assumed, otherwise if a file does not exist in\n"
+            "      the specified path, ollama:// is assumed. Models that are being\n"
+            "      pulled are downloaded with .partial extension while being\n"
+            "      downloaded and then renamed as the file without the .partial\n"
+            "      extension when complete.\n"
+            "\n"
+            "Examples:\n"
+            "  llama-run llama3\n"
+            "  llama-run ollama://granite-code\n"
+            "  llama-run ollama://smollm:135m\n"
+            "  llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf\n"
+            "  llama-run huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf\n"
+            "  llama-run https://example.com/some-file1.gguf\n"
+            "  llama-run some-file2.gguf\n"
+            "  llama-run file://some-file3.gguf\n"
+            "  llama-run --ngl 99 some-file4.gguf\n"
+            "  llama-run --ngl 99 some-file5.gguf Hello World\n";
     }
 
     int parse(int argc, const char ** argv) {
+        int positional_args_i = 0;
         for (int i = 1; i < argc; ++i) {
-            std::string arg = argv[i];
-            if (string_args.count(arg)) {
-                if (i + 1 < argc) {
-                    *string_args[arg] = argv[++i];
-                } else {
-                    fprintf(stderr, "error: missing value for %s\n", arg.c_str());
-                    print_usage();
+            if (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0) {
+                if (i + 1 >= argc) {
                     return 1;
                 }
-            } else if (int_args.count(arg)) {
-                if (i + 1 < argc) {
-                    if (parse_int_arg(argv[++i], *int_args[arg]) != 0) {
-                        fprintf(stderr, "error: invalid value for %s: %s\n", arg.c_str(), argv[i]);
-                        print_usage();
-                        return 1;
-                    }
-                } else {
-                    fprintf(stderr, "error: missing value for %s\n", arg.c_str());
-                    print_usage();
+
+                context_size_ = std::atoi(argv[++i]);
+            } else if (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "--ngl") == 0) {
+                if (i + 1 >= argc) {
                     return 1;
                 }
+
+                ngl_ = std::atoi(argv[++i]);
+            } else if (strcmp(argv[i], "-h") == 0 || strcmp(argv[i], "--help") == 0) {
+                help_ = true;
+                return 0;
+            } else if (!positional_args_i) {
+                ++positional_args_i;
+                model_ = argv[i];
+            } else if (positional_args_i == 1) {
+                ++positional_args_i;
+                user_ = argv[i];
             } else {
-                fprintf(stderr, "error: unrecognized argument %s\n", arg.c_str());
-                print_usage();
-                return 1;
+                user_ += " " + std::string(argv[i]);
             }
         }
 
-        if (string_args["-m"]->empty()) {
-            fprintf(stderr, "error: -m is required\n");
-            print_usage();
+        return model_.empty();  // model_ is the only required value
+    }
+
+    void help() const { printf("%s", help_str_.c_str()); }
+};
+
+struct progress_data {
+    size_t file_size = 0;
+    std::chrono::steady_clock::time_point start_time = std::chrono::steady_clock::now();
+    bool   printed   = false;
+};
+
+struct FileDeleter {
+    void operator()(FILE * file) const {
+        if (file) {
+            fclose(file);
+        }
+    }
+};
+
+typedef std::unique_ptr<FILE, FileDeleter> FILE_ptr;
+
+#ifdef LLAMA_USE_CURL
+class CurlWrapper {
+  public:
+    int init(const std::string & url, const std::vector<std::string> & headers, const std::string & output_file,
+             const bool progress, std::string * response_str = nullptr) {
+        std::string output_file_partial;
+        curl = curl_easy_init();
+        if (!curl) {
             return 1;
         }
 
+        progress_data data;
+        FILE_ptr      out;
+        if (!output_file.empty()) {
+            output_file_partial = output_file + ".partial";
+            out.reset(fopen(output_file_partial.c_str(), "ab"));
+        }
+
+        set_write_options(response_str, out);
+        data.file_size = set_resume_point(output_file_partial);
+        set_progress_options(progress, data);
+        set_headers(headers);
+        perform(url);
+        if (!output_file.empty()) {
+            std::filesystem::rename(output_file_partial, output_file);
+        }
+
         return 0;
     }
 
-   private:
-    const char * program_name;
-    std::unordered_map<std::string, std::string *> string_args;
-    std::unordered_map<std::string, int *> int_args;
-    std::vector<Argument> arguments;
+    ~CurlWrapper() {
+        if (chunk) {
+            curl_slist_free_all(chunk);
+        }
 
-    int parse_int_arg(const char * arg, int & value) {
-        char * end;
-        const long val = std::strtol(arg, &end, 10);
-        if (*end == '\0' && val >= INT_MIN && val <= INT_MAX) {
-            value = static_cast<int>(val);
+        if (curl) {
+            curl_easy_cleanup(curl);
+        }
+    }
+
+  private:
+    CURL *              curl  = nullptr;
+    struct curl_slist * chunk = nullptr;
+
+    void set_write_options(std::string * response_str, const FILE_ptr & out) {
+        if (response_str) {
+            curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, capture_data);
+            curl_easy_setopt(curl, CURLOPT_WRITEDATA, response_str);
+        } else {
+            curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, write_data);
+            curl_easy_setopt(curl, CURLOPT_WRITEDATA, out.get());
+        }
+    }
+
+    size_t set_resume_point(const std::string & output_file) {
+        size_t file_size = 0;
+        if (std::filesystem::exists(output_file)) {
+            file_size = std::filesystem::file_size(output_file);
+            curl_easy_setopt(curl, CURLOPT_RESUME_FROM_LARGE, static_cast<curl_off_t>(file_size));
+        }
+
+        return file_size;
+    }
+
+    void set_progress_options(bool progress, progress_data & data) {
+        if (progress) {
+            curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L);
+            curl_easy_setopt(curl, CURLOPT_XFERINFODATA, &data);
+            curl_easy_setopt(curl, CURLOPT_XFERINFOFUNCTION, progress_callback);
+        }
+    }
+
+    void set_headers(const std::vector<std::string> & headers) {
+        if (!headers.empty()) {
+            if (chunk) {
+                curl_slist_free_all(chunk);
+                chunk = 0;
+            }
+
+            for (const auto & header : headers) {
+                chunk = curl_slist_append(chunk, header.c_str());
+            }
+
+            curl_easy_setopt(curl, CURLOPT_HTTPHEADER, chunk);
+        }
+    }
+
+    void perform(const std::string & url) {
+        CURLcode res;
+        curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
+        curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L);
+        curl_easy_setopt(curl, CURLOPT_DEFAULT_PROTOCOL, "https");
+        curl_easy_setopt(curl, CURLOPT_FAILONERROR, 1L);
+        res = curl_easy_perform(curl);
+        if (res != CURLE_OK) {
+            printe("curl_easy_perform() failed: %s\n", curl_easy_strerror(res));
+        }
+    }
+
+    static std::string human_readable_time(double seconds) {
+        int hrs  = static_cast<int>(seconds) / 3600;
+        int mins = (static_cast<int>(seconds) % 3600) / 60;
+        int secs = static_cast<int>(seconds) % 60;
+
+        std::ostringstream out;
+        if (hrs > 0) {
+            out << hrs << "h " << std::setw(2) << std::setfill('0') << mins << "m " << std::setw(2) << std::setfill('0')
+                << secs << "s";
+        } else if (mins > 0) {
+            out << mins << "m " << std::setw(2) << std::setfill('0') << secs << "s";
+        } else {
+            out << secs << "s";
+        }
+
+        return out.str();
+    }
+
+    static std::string human_readable_size(curl_off_t size) {
+        static const char * suffix[] = { "B", "KB", "MB", "GB", "TB" };
+        char         length   = sizeof(suffix) / sizeof(suffix[0]);
+        int          i        = 0;
+        double       dbl_size = size;
+        if (size > 1024) {
+            for (i = 0; (size / 1024) > 0 && i < length - 1; i++, size /= 1024) {
+                dbl_size = size / 1024.0;
+            }
+        }
+
+        std::ostringstream out;
+        out << std::fixed << std::setprecision(2) << dbl_size << " " << suffix[i];
+        return out.str();
+    }
+
+    static int progress_callback(void * ptr, curl_off_t total_to_download, curl_off_t now_downloaded, curl_off_t,
+                                 curl_off_t) {
+        progress_data * data = static_cast<progress_data *>(ptr);
+        if (total_to_download <= 0) {
             return 0;
         }
-        return 1;
-    }
 
-    void print_usage() const {
-        printf("\nUsage:\n");
-        printf("  %s [OPTIONS]\n\n", program_name);
-        printf("Options:\n");
-        for (const auto & arg : arguments) {
-            printf("  %-10s %s\n", arg.flag.c_str(), arg.help_text.c_str());
+        total_to_download += data->file_size;
+        const curl_off_t now_downloaded_plus_file_size = now_downloaded + data->file_size;
+        const curl_off_t percentage                    = (now_downloaded_plus_file_size * 100) / total_to_download;
+        const curl_off_t pos                           = (percentage / 5);
+        std::string progress_bar;
+        for (int i = 0; i < 20; ++i) {
+            progress_bar.append((i < pos) ? "█" : " ");
         }
 
-        printf("\n");
+        // Calculate download speed and estimated time to completion
+        const auto                          now             = std::chrono::steady_clock::now();
+        const std::chrono::duration<double> elapsed_seconds = now - data->start_time;
+        const double                        speed           = now_downloaded / elapsed_seconds.count();
+        const double                        estimated_time  = (total_to_download - now_downloaded) / speed;
+        printe("\r%ld%% |%s| %s/%s  %.2f MB/s  %s      ", percentage, progress_bar.c_str(),
+               human_readable_size(now_downloaded).c_str(), human_readable_size(total_to_download).c_str(),
+               speed / (1024 * 1024), human_readable_time(estimated_time).c_str());
+        fflush(stderr);
+        data->printed = true;
+
+        return 0;
+    }
+
+    // Function to write data to a file
+    static size_t write_data(void * ptr, size_t size, size_t nmemb, void * stream) {
+        FILE * out = static_cast<FILE *>(stream);
+        return fwrite(ptr, size, nmemb, out);
+    }
+
+    // Function to capture data into a string
+    static size_t capture_data(void * ptr, size_t size, size_t nmemb, void * stream) {
+        std::string * str = static_cast<std::string *>(stream);
+        str->append(static_cast<char *>(ptr), size * nmemb);
+        return size * nmemb;
     }
 };
+#endif
 
 class LlamaData {
-   public:
-    llama_model_ptr model;
-    llama_sampler_ptr sampler;
-    llama_context_ptr context;
+  public:
+    llama_model_ptr                 model;
+    llama_sampler_ptr               sampler;
+    llama_context_ptr               context;
     std::vector<llama_chat_message> messages;
+    std::vector<std::string>        msg_strs;
+    std::vector<char>               fmtted;
 
-    int init(const Options & opt) {
-        model = initialize_model(opt.model_path, opt.ngl);
+    int init(Opt & opt) {
+        model = initialize_model(opt);
         if (!model) {
             return 1;
         }
 
-        context = initialize_context(model, opt.n_ctx);
+        context = initialize_context(model, opt.context_size_);
         if (!context) {
             return 1;
         }
@@ -131,15 +353,123 @@ class LlamaData {
         return 0;
     }
 
-   private:
-    // Initializes the model and returns a unique pointer to it
-    llama_model_ptr initialize_model(const std::string & model_path, const int ngl) {
-        llama_model_params model_params = llama_model_default_params();
-        model_params.n_gpu_layers = ngl;
+  private:
+#ifdef LLAMA_USE_CURL
+    int download(const std::string & url, const std::vector<std::string> & headers, const std::string & output_file,
+                 const bool progress, std::string * response_str = nullptr) {
+        CurlWrapper curl;
+        if (curl.init(url, headers, output_file, progress, response_str)) {
+            return 1;
+        }
 
-        llama_model_ptr model(llama_load_model_from_file(model_path.c_str(), model_params));
+        return 0;
+    }
+#else
+    int download(const std::string &, const std::vector<std::string> &, const std::string &, const bool,
+                 std::string * = nullptr) {
+        printe("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
+        return 1;
+    }
+#endif
+
+    int huggingface_dl(const std::string & model, const std::vector<std::string> headers, const std::string & bn) {
+        // Find the second occurrence of '/' after protocol string
+        size_t pos = model.find('/');
+        pos        = model.find('/', pos + 1);
+        if (pos == std::string::npos) {
+            return 1;
+        }
+
+        const std::string hfr = model.substr(0, pos);
+        const std::string hff = model.substr(pos + 1);
+        const std::string url = "https://huggingface.co/" + hfr + "/resolve/main/" + hff;
+        return download(url, headers, bn, true);
+    }
+
+    int ollama_dl(std::string & model, const std::vector<std::string> headers, const std::string & bn) {
+        if (model.find('/') == std::string::npos) {
+            model = "library/" + model;
+        }
+
+        std::string model_tag = "latest";
+        size_t      colon_pos = model.find(':');
+        if (colon_pos != std::string::npos) {
+            model_tag = model.substr(colon_pos + 1);
+            model     = model.substr(0, colon_pos);
+        }
+
+        std::string manifest_url = "https://registry.ollama.ai/v2/" + model + "/manifests/" + model_tag;
+        std::string manifest_str;
+        const int   ret = download(manifest_url, headers, "", false, &manifest_str);
+        if (ret) {
+            return ret;
+        }
+
+        nlohmann::json manifest = nlohmann::json::parse(manifest_str);
+        std::string    layer;
+        for (const auto & l : manifest["layers"]) {
+            if (l["mediaType"] == "application/vnd.ollama.image.model") {
+                layer = l["digest"];
+                break;
+            }
+        }
+
+        std::string blob_url = "https://registry.ollama.ai/v2/" + model + "/blobs/" + layer;
+        return download(blob_url, headers, bn, true);
+    }
+
+    std::string basename(const std::string & path) {
+        const size_t pos = path.find_last_of("/\\");
+        if (pos == std::string::npos) {
+            return path;
+        }
+
+        return path.substr(pos + 1);
+    }
+
+    int remove_proto(std::string & model_) {
+        const std::string::size_type pos = model_.find("://");
+        if (pos == std::string::npos) {
+            return 1;
+        }
+
+        model_ = model_.substr(pos + 3);  // Skip past "://"
+        return 0;
+    }
+
+    int resolve_model(std::string & model_) {
+        const std::string              bn      = basename(model_);
+        const std::vector<std::string> headers = { "--header",
+                                                   "Accept: application/vnd.docker.distribution.manifest.v2+json" };
+        int                            ret     = 0;
+        if (string_starts_with(model_, "file://") || std::filesystem::exists(bn)) {
+            remove_proto(model_);
+        } else if (string_starts_with(model_, "hf://") || string_starts_with(model_, "huggingface://")) {
+            remove_proto(model_);
+            ret = huggingface_dl(model_, headers, bn);
+        } else if (string_starts_with(model_, "ollama://")) {
+            remove_proto(model_);
+            ret = ollama_dl(model_, headers, bn);
+        } else if (string_starts_with(model_, "https://")) {
+            download(model_, headers, bn, true);
+        } else {
+            ret = ollama_dl(model_, headers, bn);
+        }
+
+        model_ = bn;
+
+        return ret;
+    }
+
+    // Initializes the model and returns a unique pointer to it
+    llama_model_ptr initialize_model(Opt & opt) {
+        ggml_backend_load_all();
+        llama_model_params model_params = llama_model_default_params();
+        model_params.n_gpu_layers       = opt.ngl_ >= 0 ? opt.ngl_ : model_params.n_gpu_layers;
+        resolve_model(opt.model_);
+        llama_model_ptr model(llama_load_model_from_file(opt.model_.c_str(), model_params));
         if (!model) {
-            fprintf(stderr, "%s: error: unable to load model\n", __func__);
+            printe("%s: error: unable to load model from file: %s\n", __func__, opt.model_.c_str());
         }
 
         return model;
@@ -148,12 +478,11 @@ class LlamaData {
     // Initializes the context with the specified parameters
     llama_context_ptr initialize_context(const llama_model_ptr & model, const int n_ctx) {
         llama_context_params ctx_params = llama_context_default_params();
-        ctx_params.n_ctx = n_ctx;
-        ctx_params.n_batch = n_ctx;
-
+        ctx_params.n_ctx                = n_ctx;
+        ctx_params.n_batch              = n_ctx;
         llama_context_ptr context(llama_new_context_with_model(model.get(), ctx_params));
         if (!context) {
-            fprintf(stderr, "%s: error: failed to create the llama_context\n", __func__);
+            printe("%s: error: failed to create the llama_context\n", __func__);
         }
 
         return context;
@@ -170,23 +499,22 @@ class LlamaData {
     }
 };
 
-// Add a message to `messages` and store its content in `owned_content`
-static void add_message(const char * role, const std::string & text, LlamaData & llama_data,
-                        std::vector<char_array_ptr> & owned_content) {
-    char_array_ptr content(new char[text.size() + 1]);
-    std::strcpy(content.get(), text.c_str());
-    llama_data.messages.push_back({role, content.get()});
-    owned_content.push_back(std::move(content));
+// Add a message to `messages` and store its content in `msg_strs`
+static void add_message(const char * role, const std::string & text, LlamaData & llama_data) {
+    llama_data.msg_strs.push_back(std::move(text));
+    llama_data.messages.push_back({ role, llama_data.msg_strs.back().c_str() });
 }
 
 // Function to apply the chat template and resize `formatted` if needed
-static int apply_chat_template(const LlamaData & llama_data, std::vector<char> & formatted, const bool append) {
-    int result = llama_chat_apply_template(llama_data.model.get(), nullptr, llama_data.messages.data(),
-                                           llama_data.messages.size(), append, formatted.data(), formatted.size());
-    if (result > static_cast<int>(formatted.size())) {
-        formatted.resize(result);
+static int apply_chat_template(LlamaData & llama_data, const bool append) {
+    int result = llama_chat_apply_template(
+        llama_data.model.get(), nullptr, llama_data.messages.data(), llama_data.messages.size(), append,
+        append ? llama_data.fmtted.data() : nullptr, append ? llama_data.fmtted.size() : 0);
+    if (append && result > static_cast<int>(llama_data.fmtted.size())) {
+        llama_data.fmtted.resize(result);
         result = llama_chat_apply_template(llama_data.model.get(), nullptr, llama_data.messages.data(),
-                                           llama_data.messages.size(), append, formatted.data(), formatted.size());
+                                           llama_data.messages.size(), append, llama_data.fmtted.data(),
+                                           llama_data.fmtted.size());
     }
 
     return result;
@@ -199,7 +527,8 @@ static int tokenize_prompt(const llama_model_ptr & model, const std::string & pr
     prompt_tokens.resize(n_prompt_tokens);
     if (llama_tokenize(model.get(), prompt.c_str(), prompt.size(), prompt_tokens.data(), prompt_tokens.size(), true,
                        true) < 0) {
-        GGML_ABORT("failed to tokenize the prompt\n");
+        printe("failed to tokenize the prompt\n");
+        return -1;
     }
 
     return n_prompt_tokens;
@@ -207,11 +536,11 @@ static int tokenize_prompt(const llama_model_ptr & model, const std::string & pr
 
 // Check if we have enough space in the context to evaluate this batch
 static int check_context_size(const llama_context_ptr & ctx, const llama_batch & batch) {
-    const int n_ctx = llama_n_ctx(ctx.get());
+    const int n_ctx      = llama_n_ctx(ctx.get());
     const int n_ctx_used = llama_get_kv_cache_used_cells(ctx.get());
     if (n_ctx_used + batch.n_tokens > n_ctx) {
         printf("\033[0m\n");
-        fprintf(stderr, "context size exceeded\n");
+        printe("context size exceeded\n");
         return 1;
     }
 
@@ -221,9 +550,10 @@ static int check_context_size(const llama_context_ptr & ctx, const llama_batch &
 // convert the token to a string
 static int convert_token_to_string(const llama_model_ptr & model, const llama_token token_id, std::string & piece) {
     char buf[256];
-    int n = llama_token_to_piece(model.get(), token_id, buf, sizeof(buf), 0, true);
+    int  n = llama_token_to_piece(model.get(), token_id, buf, sizeof(buf), 0, true);
     if (n < 0) {
-        GGML_ABORT("failed to convert token to piece\n");
+        printe("failed to convert token to piece\n");
+        return 1;
     }
 
     piece = std::string(buf, n);
@@ -238,19 +568,19 @@ static void print_word_and_concatenate_to_response(const std::string & piece, st
 
 // helper function to evaluate a prompt and generate a response
 static int generate(LlamaData & llama_data, const std::string & prompt, std::string & response) {
-    std::vector<llama_token> prompt_tokens;
-    const int n_prompt_tokens = tokenize_prompt(llama_data.model, prompt, prompt_tokens);
-    if (n_prompt_tokens < 0) {
+    std::vector<llama_token> tokens;
+    if (tokenize_prompt(llama_data.model, prompt, tokens) < 0) {
         return 1;
     }
 
     // prepare a batch for the prompt
-    llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size());
+    llama_batch batch = llama_batch_get_one(tokens.data(), tokens.size());
     llama_token new_token_id;
     while (true) {
         check_context_size(llama_data.context, batch);
         if (llama_decode(llama_data.context.get(), batch)) {
-            GGML_ABORT("failed to decode\n");
+            printe("failed to decode\n");
+            return 1;
         }
 
         // sample the next token, check is it an end of generation?
@@ -273,22 +603,9 @@ static int generate(LlamaData & llama_data, const std::string & prompt, std::str
     return 0;
 }
 
-static int parse_arguments(const int argc, const char ** argv, Options & opt) {
-    ArgumentParser parser(argv[0]);
-    parser.add_argument("-m", opt.model_path, "model");
-    parser.add_argument("-p", opt.prompt_non_interactive, "prompt");
-    parser.add_argument("-c", opt.n_ctx, "context_size");
-    parser.add_argument("-ngl", opt.ngl, "n_gpu_layers");
-    if (parser.parse(argc, argv)) {
-        return 1;
-    }
-
-    return 0;
-}
-
 static int read_user_input(std::string & user) {
     std::getline(std::cin, user);
-    return user.empty();  // Indicate an error or empty input
+    return user.empty();  // Should have data in happy path
 }
 
 // Function to generate a response based on the prompt
@@ -296,7 +613,7 @@ static int generate_response(LlamaData & llama_data, const std::string & prompt,
     // Set response color
     printf("\033[33m");
     if (generate(llama_data, prompt, response)) {
-        fprintf(stderr, "failed to generate response\n");
+        printe("failed to generate response\n");
         return 1;
     }
 
@@ -306,11 +623,10 @@ static int generate_response(LlamaData & llama_data, const std::string & prompt,
 }
 
 // Helper function to apply the chat template and handle errors
-static int apply_chat_template_with_error_handling(const LlamaData & llama_data, std::vector<char> & formatted,
-                                                   const bool is_user_input, int & output_length) {
-    const int new_len = apply_chat_template(llama_data, formatted, is_user_input);
+static int apply_chat_template_with_error_handling(LlamaData & llama_data, const bool append, int & output_length) {
+    const int new_len = apply_chat_template(llama_data, append);
     if (new_len < 0) {
-        fprintf(stderr, "failed to apply the chat template\n");
+        printe("failed to apply the chat template\n");
         return -1;
     }
 
@@ -319,56 +635,63 @@ static int apply_chat_template_with_error_handling(const LlamaData & llama_data,
 }
 
 // Helper function to handle user input
-static bool handle_user_input(std::string & user_input, const std::string & prompt_non_interactive) {
-    if (!prompt_non_interactive.empty()) {
-        user_input = prompt_non_interactive;
-        return true;  // No need for interactive input
+static int handle_user_input(std::string & user_input, const std::string & user_) {
+    if (!user_.empty()) {
+        user_input = user_;
+        return 0;  // No need for interactive input
     }
 
-    printf("\033[32m> \033[0m");
-    return !read_user_input(user_input);  // Returns false if input ends the loop
+    printf(
+        "\r                                                                       "
+        "\r\033[32m> \033[0m");
+    return read_user_input(user_input);  // Returns true if input ends the loop
 }
 
 // Function to tokenize the prompt
-static int chat_loop(LlamaData & llama_data, std::string & prompt_non_interactive) {
-    std::vector<char_array_ptr> owned_content;
-    std::vector<char> fmtted(llama_n_ctx(llama_data.context.get()));
+static int chat_loop(LlamaData & llama_data, const std::string & user_) {
     int prev_len = 0;
-
+    llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get()));
     while (true) {
         // Get user input
         std::string user_input;
-        if (!handle_user_input(user_input, prompt_non_interactive)) {
-            break;
+        while (handle_user_input(user_input, user_)) {
         }
 
-        add_message("user", prompt_non_interactive.empty() ? user_input : prompt_non_interactive, llama_data,
-                    owned_content);
-
+        add_message("user", user_.empty() ? user_input : user_, llama_data);
         int new_len;
-        if (apply_chat_template_with_error_handling(llama_data, fmtted, true, new_len) < 0) {
+        if (apply_chat_template_with_error_handling(llama_data, true, new_len) < 0) {
             return 1;
         }
 
-        std::string prompt(fmtted.begin() + prev_len, fmtted.begin() + new_len);
+        std::string prompt(llama_data.fmtted.begin() + prev_len, llama_data.fmtted.begin() + new_len);
         std::string response;
         if (generate_response(llama_data, prompt, response)) {
             return 1;
         }
+
+        if (!user_.empty()) {
+            break;
+        }
+
+        add_message("assistant", response, llama_data);
+        if (apply_chat_template_with_error_handling(llama_data, false, prev_len) < 0) {
+            return 1;
+        }
     }
+
     return 0;
 }
 
 static void log_callback(const enum ggml_log_level level, const char * text, void *) {
     if (level == GGML_LOG_LEVEL_ERROR) {
-        fprintf(stderr, "%s", text);
+        printe("%s", text);
     }
 }
 
 static bool is_stdin_a_terminal() {
 #if defined(_WIN32)
     HANDLE hStdin = GetStdHandle(STD_INPUT_HANDLE);
-    DWORD mode;
+    DWORD  mode;
     return GetConsoleMode(hStdin, &mode);
 #else
     return isatty(STDIN_FILENO);
@@ -382,17 +705,20 @@ static std::string read_pipe_data() {
 }
 
 int main(int argc, const char ** argv) {
-    Options opt;
-    if (parse_arguments(argc, argv, opt)) {
+    Opt       opt;
+    const int ret = opt.init(argc, argv);
+    if (ret == 2) {
+        return 0;
+    } else if (ret) {
         return 1;
     }
 
     if (!is_stdin_a_terminal()) {
-        if (!opt.prompt_non_interactive.empty()) {
-            opt.prompt_non_interactive += "\n\n";
+        if (!opt.user_.empty()) {
+            opt.user_ += "\n\n";
         }
 
-        opt.prompt_non_interactive += read_pipe_data();
+        opt.user_ += read_pipe_data();
     }
 
     llama_log_set(log_callback, nullptr);
@@ -401,7 +727,7 @@ int main(int argc, const char ** argv) {
         return 1;
     }
 
-    if (chat_loop(llama_data, opt.prompt_non_interactive)) {
+    if (chat_loop(llama_data, opt.user_)) {
         return 1;
     }
 

examples/server/CMakeLists.txt

@@ -15,13 +15,8 @@ set(TARGET_SRCS
     httplib.h
 )
 set(PUBLIC_ASSETS
-    index.html
-    completion.js
+    index.html.gz
     loading.html
-    deps_daisyui.min.css
-    deps_markdown-it.js
-    deps_tailwindcss.js
-    deps_vue.esm-browser.js
 )
 
 foreach(asset ${PUBLIC_ASSETS})
@@ -33,6 +28,7 @@ foreach(asset ${PUBLIC_ASSETS})
         OUTPUT "${output}"
         COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake"
     )
+    set_source_files_properties(${output} PROPERTIES GENERATED TRUE)
 endforeach()
 
 add_executable(${TARGET} ${TARGET_SRCS})

examples/server/README.md

@@ -62,13 +62,15 @@ The project is under active development, and we are [looking for feedback and co
 | `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: 32.0)<br/>(env: LLAMA_ARG_YARN_BETA_FAST) |
 | `-dkvc, --dump-kv-cache` | verbose print of the KV cache |
 | `-nkvo, --no-kv-offload` | disable KV offload<br/>(env: LLAMA_ARG_NO_KV_OFFLOAD) |
-| `-ctk, --cache-type-k TYPE` | KV cache data type for K (default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
-| `-ctv, --cache-type-v TYPE` | KV cache data type for V (default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
+| `-ctk, --cache-type-k TYPE` | KV cache data type for K<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
+| `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (default: 0.1, < 0 - disabled)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
 | `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_NO_MMAP) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggerganov/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
+| `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
+| `--list-devices` | print list of available devices and exit |
 | `-ngl, --gpu-layers, --n-gpu-layers N` | number of layers to store in VRAM<br/>(env: LLAMA_ARG_N_GPU_LAYERS) |
 | `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:<br/>- none: use one GPU only<br/>- layer (default): split layers and KV across GPUs<br/>- row: split rows across GPUs<br/>(env: LLAMA_ARG_SPLIT_MODE) |
 | `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1<br/>(env: LLAMA_ARG_TENSOR_SPLIT) |
@@ -102,7 +104,6 @@ The project is under active development, and we are [looking for feedback and co
 | `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) |
 | `--sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: dkypmxt) |
 | `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) |
-| `--penalize-nl` | penalize newline tokens (default: false) |
 | `--temp N` | temperature (default: 0.8) |
 | `--top-k N` | top-k sampling (default: 40, 0 = disabled) |
 | `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) |
@@ -136,6 +137,7 @@ The project is under active development, and we are [looking for feedback and co
 | -------- | ----------- |
 | `--no-context-shift` | disables context shift on inifinite text generation (default: disabled)<br/>(env: LLAMA_ARG_NO_CONTEXT_SHIFT) |
 | `-sp, --special` | special tokens output enabled (default: false) |
+| `--no-warmup` | skip warming up the model with an empty run |
 | `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
 | `--pooling {none,mean,cls,last,rank}` | pooling type for embeddings, use model default if unspecified<br/>(env: LLAMA_ARG_POOLING) |
 | `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
@@ -144,6 +146,7 @@ The project is under active development, and we are [looking for feedback and co
 | `--host HOST` | ip address to listen (default: 127.0.0.1)<br/>(env: LLAMA_ARG_HOST) |
 | `--port PORT` | port to listen (default: 8080)<br/>(env: LLAMA_ARG_PORT) |
 | `--path PATH` | path to serve static files from (default: )<br/>(env: LLAMA_ARG_STATIC_PATH) |
+| `--no-webui` | Disable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_NO_WEBUI) |
 | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
 | `--reranking, --rerank` | enable reranking endpoint on server (default: disabled)<br/>(env: LLAMA_ARG_RERANKING) |
 | `--api-key KEY` | API key to use for authentication (default: none)<br/>(env: LLAMA_API_KEY) |
@@ -158,9 +161,16 @@ The project is under active development, and we are [looking for feedback and co
 | `--props` | enable changing global properties via POST /props (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_PROPS) |
 | `--no-slots` | disables slots monitoring endpoint<br/>(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) |
 | `--slot-save-path PATH` | path to save slot kv cache (default: disabled) |
-| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted:<br/>https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
+| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>list of built-in templates:<br/>chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, exaone3, gemma, granite, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, monarch, openchat, orion, phi3, rwkv-world, vicuna, vicuna-orca, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
 | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.50, 0.0 = disabled)<br/> |
 | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
+| `--draft-max, --draft, --draft-n N` | number of tokens to draft for speculative decoding (default: 16)<br/>(env: LLAMA_ARG_DRAFT_MAX) |
+| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 5)<br/>(env: LLAMA_ARG_DRAFT_MIN) |
+| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.9)<br/>(env: LLAMA_ARG_DRAFT_P_MIN) |
+| `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model)<br/>(env: LLAMA_ARG_CTX_SIZE_DRAFT) |
+| `-devd, --device-draft <dev1,dev2,..>` | comma-separated list of devices to use for offloading the draft model (none = don't offload)<br/>use --list-devices to see a list of available devices |
+| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model<br/>(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) |
+| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)<br/>(env: LLAMA_ARG_MODEL_DRAFT) |
 
 
 Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var.
@@ -188,12 +198,6 @@ services:
 
 `llama-server` is built alongside everything else from the root of the project
 
-- Using `make`:
-
-  ```bash
-  make llama-server
-  ```
-
 - Using `CMake`:
 
   ```bash
@@ -207,15 +211,6 @@ services:
 
 `llama-server` can also be built with SSL support using OpenSSL 3
 
-- Using `make`:
-
-  ```bash
-  # NOTE: For non-system openssl, use the following:
-  #   CXXFLAGS="-I /path/to/openssl/include"
-  #   LDFLAGS="-L /path/to/openssl/lib"
-  make LLAMA_SERVER_SSL=true llama-server
-  ```
-
 - Using `CMake`:
 
   ```bash
@@ -223,6 +218,37 @@ services:
   cmake --build build --config Release -t llama-server
   ```
 
+## Web UI
+
+The project includes a web-based user interface that enables interaction with the model through the `/chat/completions` endpoint.
+
+The web UI is developed using:
+- `vue` framework for frontend development
+- `tailwindcss` and `daisyui` for styling
+- `vite` for build tooling
+
+A pre-built version is available as a single HTML file under `/public` directory.
+
+To build or to run the dev server (with hot reload):
+
+```sh
+# make sure you have nodejs installed
+cd examples/server/webui
+npm i
+
+# to run the dev server
+npm run dev
+
+# to build the public/index.html
+npm run build
+```
+
+NOTE: if you are using the vite dev server, you can change the API base URL to llama.cpp. To do that, run this code snippet in browser's console:
+
+```js
+localStorage.setItem('base', 'http://localhost:8080')
+```
+
 ## Quick Start
 
 To get started right away, run the following command, making sure to use the correct path for the model you have:
@@ -277,23 +303,23 @@ mkdir llama-client
 cd llama-client
 ```
 
-Create a index.js file and put this inside:
+Create an index.js file and put this inside:
 
 ```javascript
-const prompt = `Building a website can be done in 10 simple steps:`;
+const prompt = "Building a website can be done in 10 simple steps:"
 
-async function Test() {
+async function test() {
     let response = await fetch("http://127.0.0.1:8080/completion", {
-        method: 'POST',
+        method: "POST",
         body: JSON.stringify({
             prompt,
-            n_predict: 512,
+            n_predict: 64,
         })
     })
     console.log((await response.json()).content)
 }
 
-Test()
+test()
 ```
 
 And run it:
@@ -317,110 +343,108 @@ node index.js
 
 ### POST `/completion`: Given a `prompt`, it returns the predicted completion.
 
-    *Options:*
+*Options:*
 
-    `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true:
+`prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true:
 
-      - The prompt is a string or an array with the first element given as a string
-      - The model's `tokenizer.ggml.add_bos_token` metadata is `true`
+  - The prompt is a string or an array with the first element given as a string
+  - The model's `tokenizer.ggml.add_bos_token` metadata is `true`
 
-    These input shapes and data type are allowed for `prompt`:
+These input shapes and data type are allowed for `prompt`:
 
-      - Single string: `"string"`
-      - Single sequence of tokens: `[12, 34, 56]`
-      - Mixed tokens and strings: `[12, 34, "string", 56, 78]`
+  - Single string: `"string"`
+  - Single sequence of tokens: `[12, 34, 56]`
+  - Mixed tokens and strings: `[12, 34, "string", 56, 78]`
 
-    Multiple prompts are also supported. In this case, the completion result will be an array.
+Multiple prompts are also supported. In this case, the completion result will be an array.
 
-      - Only strings: `["string1", "string2"]`
-      - Strings and sequences of tokens: `["string1", [12, 34, 56]]`
-      - Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string"]`
+  - Only strings: `["string1", "string2"]`
+  - Strings and sequences of tokens: `["string1", [12, 34, 56]]`
+  - Mixed types: `[[12, 34, "string", 56, 78], [12, 34, 56], "string"]`
 
-    `temperature`: Adjust the randomness of the generated text. Default: `0.8`
+`temperature`: Adjust the randomness of the generated text. Default: `0.8`
 
-    `dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled.
+`dynatemp_range`: Dynamic temperature range. The final temperature will be in the range of `[temperature - dynatemp_range; temperature + dynatemp_range]` Default: `0.0`, which is disabled.
 
-    `dynatemp_exponent`: Dynamic temperature exponent. Default: `1.0`
+`dynatemp_exponent`: Dynamic temperature exponent. Default: `1.0`
 
-    `top_k`: Limit the next token selection to the K most probable tokens.  Default: `40`
+`top_k`: Limit the next token selection to the K most probable tokens.  Default: `40`
 
-    `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P. Default: `0.95`
+`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P. Default: `0.95`
 
-    `min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token. Default: `0.05`
+`min_p`: The minimum probability for a token to be considered, relative to the probability of the most likely token. Default: `0.05`
 
-    `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity.
+`n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. Default: `-1`, where `-1` is infinity.
 
-    `n_indent`: Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks. Default: `0`
+`n_indent`: Specify the minimum line indentation for the generated text in number of whitespace characters. Useful for code completion tasks. Default: `0`
 
-    `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token.
-    By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt.
+`n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token.
+By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt.
 
-    `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
+`stream`: Allows receiving each predicted token in real-time instead of waiting for the completion to finish (uses a different response format). To enable this, set to `true`.
 
-    `stop`: Specify a JSON array of stopping strings.
-    These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]`
+`stop`: Specify a JSON array of stopping strings.
+These words will not be included in the completion, so make sure to add them to the prompt for the next iteration. Default: `[]`
 
-    `typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled.
+`typical_p`: Enable locally typical sampling with parameter p. Default: `1.0`, which is disabled.
 
-    `repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1`
+`repeat_penalty`: Control the repetition of token sequences in the generated text. Default: `1.1`
 
-    `repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size.
+`repeat_last_n`: Last n tokens to consider for penalizing repetition. Default: `64`, where `0` is disabled and `-1` is ctx-size.
 
-    `penalize_nl`: Penalize newline tokens when applying the repeat penalty. Default: `true`
+`presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled.
 
-    `presence_penalty`: Repeat alpha presence penalty. Default: `0.0`, which is disabled.
+`frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled.
 
-    `frequency_penalty`: Repeat alpha frequency penalty. Default: `0.0`, which is disabled.
+`dry_multiplier`: Set the DRY (Don't Repeat Yourself) repetition penalty multiplier. Default: `0.0`, which is disabled.
 
-    `dry_multiplier`: Set the DRY (Don't Repeat Yourself) repetition penalty multiplier. Default: `0.0`, which is disabled.
+`dry_base`: Set the DRY repetition penalty base value. Default: `1.75`
 
-    `dry_base`: Set the DRY repetition penalty base value. Default: `1.75`
+`dry_allowed_length`: Tokens that extend repetition beyond this receive exponentially increasing penalty: multiplier * base ^ (length of repeating sequence before token - allowed length). Default: `2`
 
-    `dry_allowed_length`: Tokens that extend repetition beyond this receive exponentially increasing penalty: multiplier * base ^ (length of repeating sequence before token - allowed length). Default: `2`
+`dry_penalty_last_n`: How many tokens to scan for repetitions. Default: `-1`, where `0` is disabled and `-1` is context size.
 
-    `dry_penalty_last_n`: How many tokens to scan for repetitions. Default: `-1`, where `0` is disabled and `-1` is context size.
+`dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']`
 
-    `dry_sequence_breakers`: Specify an array of sequence breakers for DRY sampling. Only a JSON array of strings is accepted. Default: `['\n', ':', '"', '*']`
+`xtc_probability`: Set the chance for token removal via XTC sampler. Default: `0.0`, which is disabled.
 
-    `xtc_probability`: Set the chance for token removal via XTC sampler. Default: `0.0`, which is disabled.
+`xtc_threshold`: Set a minimum probability threshold for tokens to be removed via XTC sampler. Default: `0.1` (> `0.5` disables XTC)
 
-    `xtc_threshold`: Set a minimum probability threshold for tokens to be removed via XTC sampler. Default: `0.1` (> `0.5` disables XTC)
+`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0.
 
-    `mirostat`: Enable Mirostat sampling, controlling perplexity during text generation. Default: `0`, where `0` is disabled, `1` is Mirostat, and `2` is Mirostat 2.0.
+`mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0`
 
-    `mirostat_tau`: Set the Mirostat target entropy, parameter tau. Default: `5.0`
+`mirostat_eta`: Set the Mirostat learning rate, parameter eta.  Default: `0.1`
 
-    `mirostat_eta`: Set the Mirostat learning rate, parameter eta.  Default: `0.1`
+`grammar`: Set grammar for grammar-based sampling.  Default: no grammar
 
-    `grammar`: Set grammar for grammar-based sampling.  Default: no grammar
+`json_schema`: Set a JSON schema for grammar-based sampling (e.g. `{"items": {"type": "string"}, "minItems": 10, "maxItems": 100}` of a list of strings, or `{}` for any JSON). See [tests](../../tests/test-json-schema-to-grammar.cpp) for supported features.  Default: no JSON schema.
 
-    `json_schema`: Set a JSON schema for grammar-based sampling (e.g. `{"items": {"type": "string"}, "minItems": 10, "maxItems": 100}` of a list of strings, or `{}` for any JSON). See [tests](../../tests/test-json-schema-to-grammar.cpp) for supported features.  Default: no JSON schema.
+`seed`: Set the random number generator (RNG) seed.  Default: `-1`, which is a random seed.
 
-    `seed`: Set the random number generator (RNG) seed.  Default: `-1`, which is a random seed.
+`ignore_eos`: Ignore end of stream token and continue generating.  Default: `false`
 
-    `ignore_eos`: Ignore end of stream token and continue generating.  Default: `false`
+`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. Default: `[]`
 
-    `logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. Default: `[]`
+`n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token given the sampling settings. Note that for temperature < 0 the tokens are sampled greedily but token probabilities are still being calculated via a simple softmax of the logits without considering any other sampler settings. Default: `0`
 
-    `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token given the sampling settings. Note that for temperature < 0 the tokens are sampled greedily but token probabilities are still being calculated via a simple softmax of the logits without considering any other sampler settings. Default: `0`
+`min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0`
 
-    `min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0`
+`t_max_predict_ms`: Set a time limit in milliseconds for the prediction (a.k.a. text-generation) phase. The timeout will trigger if the generation takes more than the specified time (measured since the first token was generated) and if a new-line character has already been generated. Useful for FIM applications. Default: `0`, which is disabled.
 
-    `t_max_predict_ms`: Set a time limit in milliseconds for the prediction (a.k.a. text-generation) phase. The timeout will trigger if the generation takes more than the specified time (measured since the first token was generated) and if a new-line character has already been generated. Useful for FIM applications. Default: `0`, which is disabled.
+`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
 
-    `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:`. In this case, `[img-12]` will be replaced by the embeddings of the image with id `12` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
+`id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot.  Default: `-1`
 
-    `id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot.  Default: `-1`
+`cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `true`
 
-    `cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `true`
+`samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["dry", "top_k", "typ_p", "top_p", "min_p", "xtc", "temperature"]` - these are all the available values.
 
-    `samplers`: The order the samplers should be applied in. An array of strings representing sampler type names. If a sampler is not set, it will not be used. If a sampler is specified more than once, it will be applied multiple times. Default: `["dry", "top_k", "typ_p", "top_p", "min_p", "xtc", "temperature"]` - these are all the available values.
-
-    `timings_per_token`: Include prompt processing and text generation speed information in each response.  Default: `false`
+`timings_per_token`: Include prompt processing and text generation speed information in each response.  Default: `false`
 
 **Response format**
 
-- Note: When using streaming mode (`stream`), only `content` and `stop` will be returned until end of completion.
+- Note: In streaming mode (`stream`), only `content` and `stop` will be returned until end of completion. Responses are sent using the [Server-sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html) standard. Note: the browser's `EventSource` interface cannot be used due to its lack of `POST` request support.
 
 - `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure:
 
@@ -448,9 +472,11 @@ Notice that each `probs` is an array of length `n_probs`.
 - `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`. These options may differ from the original ones in some way (e.g. bad values filtered out, strings converted to tokens, etc.).
 - `model`: The path to the model loaded with `-m`
 - `prompt`: The provided `prompt`
-- `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token
-- `stopped_limit`: Indicating whether the completion stopped because `n_predict` tokens were generated before stop words or EOS was encountered
-- `stopped_word`: Indicating whether the completion stopped due to encountering a stopping word from `stop` JSON array provided
+- `stop_type`: Indicating whether the completion has stopped. Possible values are:
+  - `none`: Generating (not stopped)
+  - `eos`: Stopped because it encountered the EOS token
+  - `limit`: Stopped because `n_predict` tokens were generated before stop words or EOS was encountered
+  - `word`: Stopped due to encountering a stopping word from `stop` JSON array provided
 - `stopping_word`: The stopping word encountered which stopped the generation (or "" if not stopped due to a stopping word)
 - `timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second`
 - `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion (`n_past`)
@@ -459,13 +485,13 @@ Notice that each `probs` is an array of length `n_probs`.
 
 ### POST `/tokenize`: Tokenize a given text
 
-    *Options:*
+*Options:*
 
-    `content`: (Required) The text to tokenize.
+`content`: (Required) The text to tokenize.
 
-    `add_special`: (Optional) Boolean indicating if special tokens, i.e. `BOS`, should be inserted.  Default: `false`
+`add_special`: (Optional) Boolean indicating if special tokens, i.e. `BOS`, should be inserted.  Default: `false`
 
-    `with_pieces`: (Optional) Boolean indicating whether to return token pieces along with IDs.  Default: `false`
+`with_pieces`: (Optional) Boolean indicating whether to return token pieces along with IDs.  Default: `false`
 
 **Response:**
 
@@ -502,52 +528,52 @@ With input 'á' (utf8 hex: C3 A1) on tinyllama/stories260k
 
 ### POST `/detokenize`: Convert tokens to text
 
-    *Options:*
+*Options:*
 
-    `tokens`: Set the tokens to detokenize.
+`tokens`: Set the tokens to detokenize.
 
 ### POST `/embedding`: Generate embedding of a given text
 
 The same as [the embedding example](../embedding) does.
 
-    *Options:*
+*Options:*
 
-    `content`: Set the text to process.
+`content`: Set the text to process.
 
-    `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
+`image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
 
 ### POST `/reranking`: Rerank documents according to a given query
 
 Similar to https://jina.ai/reranker/ but might change in the future.
 Requires a reranker model (such as [bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3)) and the `--embedding --pooling rank` options.
 
-    *Options:*
+*Options:*
 
-    `query`: The query against which the documents will be ranked.
+`query`: The query against which the documents will be ranked.
 
-    `documents`: An array strings representing the documents to be ranked.
+`documents`: An array strings representing the documents to be ranked.
 
-    *Aliases:*
-      - `/rerank`
-      - `/v1/rerank`
-      - `/v1/reranking`
+*Aliases:*
+  - `/rerank`
+  - `/v1/rerank`
+  - `/v1/reranking`
 
-    *Examples:*
+*Examples:*
 
-    ```shell
-    curl http://127.0.0.1:8012/v1/rerank \
-        -H "Content-Type: application/json" \
-        -d '{
-            "model": "some-model",
-                "query": "What is panda?",
-                "top_n": 3,
-                "documents": [
-                    "hi",
-                "it is a bear",
-                "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China."
-                ]
-        }' | jq
-    ```
+```shell
+curl http://127.0.0.1:8012/v1/rerank \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "some-model",
+            "query": "What is panda?",
+            "top_n": 3,
+            "documents": [
+                "hi",
+            "it is a bear",
+            "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China."
+            ]
+    }' | jq
+```
 
 ### POST `/infill`: For code infilling.
 
@@ -591,14 +617,82 @@ This endpoint is public (no API key check). By default, it is read-only. To make
 
 ```json
 {
-  "default_generation_settings": { ... },
+  "default_generation_settings": {
+    "id": 0,
+    "id_task": -1,
+    "n_ctx": 1024,
+    "speculative": false,
+    "is_processing": false,
+    "params": {
+      "n_predict": -1,
+      "seed": 4294967295,
+      "temperature": 0.800000011920929,
+      "dynatemp_range": 0.0,
+      "dynatemp_exponent": 1.0,
+      "top_k": 40,
+      "top_p": 0.949999988079071,
+      "min_p": 0.05000000074505806,
+      "xtc_probability": 0.0,
+      "xtc_threshold": 0.10000000149011612,
+      "typical_p": 1.0,
+      "repeat_last_n": 64,
+      "repeat_penalty": 1.0,
+      "presence_penalty": 0.0,
+      "frequency_penalty": 0.0,
+      "dry_multiplier": 0.0,
+      "dry_base": 1.75,
+      "dry_allowed_length": 2,
+      "dry_penalty_last_n": -1,
+      "dry_sequence_breakers": [
+        "\n",
+        ":",
+        "\"",
+        "*"
+      ],
+      "mirostat": 0,
+      "mirostat_tau": 5.0,
+      "mirostat_eta": 0.10000000149011612,
+      "stop": [],
+      "max_tokens": -1,
+      "n_keep": 0,
+      "n_discard": 0,
+      "ignore_eos": false,
+      "stream": true,
+      "n_probs": 0,
+      "min_keep": 0,
+      "grammar": "",
+      "samplers": [
+        "dry",
+        "top_k",
+        "typ_p",
+        "top_p",
+        "min_p",
+        "xtc",
+        "temperature"
+      ],
+      "speculative.n_max": 16,
+      "speculative.n_min": 5,
+      "speculative.p_min": 0.8999999761581421,
+      "timings_per_token": false
+    },
+    "prompt": "",
+    "next_token": {
+      "has_next_token": true,
+      "has_new_line": false,
+      "n_remain": -1,
+      "n_decoded": 0,
+      "stopping_word": ""
+    }
+  },
   "total_slots": 1,
-  "chat_template": ""
+  "model_path": "../models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
+  "chat_template": "..."
 }
 ```
 
 - `default_generation_settings` - the default generation settings for the `/completion` endpoint, which has the same fields as the `generation_settings` response object from the `/completion` endpoint.
 - `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
+- `model_path` - the path to model file (same with `-m` argument)
 - `chat_template` - the model's original Jinja2 prompt template
 
 ### POST `/props`: Change server global properties.
@@ -613,89 +707,89 @@ To use this endpoint with POST method, you need to start server with `--props`
 
 Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only models with a [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, the ChatML template will be used.
 
-    *Options:*
+*Options:*
 
-    See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported.
+See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported.
 
-    The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name",  "type": "string" }, "date": { "title": "Date",  "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants",  "type": "string" } } } }`), similar to other OpenAI-inspired API providers.
+The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}` or `{"type": "json_schema", "schema": {"properties": { "name": { "title": "Name",  "type": "string" }, "date": { "title": "Date",  "type": "string" }, "participants": { "items": {"type: "string" }, "title": "Participants",  "type": "string" } } } }`), similar to other OpenAI-inspired API providers.
 
-    *Examples:*
+*Examples:*
 
-    You can use either Python `openai` library with appropriate checkpoints:
+You can use either Python `openai` library with appropriate checkpoints:
 
-    ```python
-    import openai
+```python
+import openai
 
-    client = openai.OpenAI(
-        base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
-        api_key = "sk-no-key-required"
-    )
+client = openai.OpenAI(
+    base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
+    api_key = "sk-no-key-required"
+)
 
-    completion = client.chat.completions.create(
-    model="gpt-3.5-turbo",
-    messages=[
-        {"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."},
-        {"role": "user", "content": "Write a limerick about python exceptions"}
-    ]
-    )
+completion = client.chat.completions.create(
+model="gpt-3.5-turbo",
+messages=[
+    {"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."},
+    {"role": "user", "content": "Write a limerick about python exceptions"}
+]
+)
 
-    print(completion.choices[0].message)
-    ```
+print(completion.choices[0].message)
+```
 
-    ... or raw HTTP requests:
+... or raw HTTP requests:
 
-    ```shell
-    curl http://localhost:8080/v1/chat/completions \
-    -H "Content-Type: application/json" \
-    -H "Authorization: Bearer no-key" \
-    -d '{
-    "model": "gpt-3.5-turbo",
-    "messages": [
-    {
-        "role": "system",
-        "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."
-    },
-    {
-        "role": "user",
-        "content": "Write a limerick about python exceptions"
-    }
-    ]
-    }'
-    ```
+```shell
+curl http://localhost:8080/v1/chat/completions \
+-H "Content-Type: application/json" \
+-H "Authorization: Bearer no-key" \
+-d '{
+"model": "gpt-3.5-turbo",
+"messages": [
+{
+    "role": "system",
+    "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."
+},
+{
+    "role": "user",
+    "content": "Write a limerick about python exceptions"
+}
+]
+}'
+```
 
 ### POST `/v1/embeddings`: OpenAI-compatible embeddings API
 
-    *Options:*
+*Options:*
 
-    See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings).
+See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings).
 
-    *Examples:*
+*Examples:*
 
-  - input as string
+- input as string
 
-    ```shell
-    curl http://localhost:8080/v1/embeddings \
-    -H "Content-Type: application/json" \
-    -H "Authorization: Bearer no-key" \
-    -d '{
-            "input": "hello",
-            "model":"GPT-4",
-            "encoding_format": "float"
-    }'
-    ```
+  ```shell
+  curl http://localhost:8080/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer no-key" \
+  -d '{
+          "input": "hello",
+          "model":"GPT-4",
+          "encoding_format": "float"
+  }'
+  ```
 
-  - `input` as string array
+- `input` as string array
 
-    ```shell
-    curl http://localhost:8080/v1/embeddings \
-    -H "Content-Type: application/json" \
-    -H "Authorization: Bearer no-key" \
-    -d '{
-            "input": ["hello", "world"],
-            "model":"GPT-4",
-            "encoding_format": "float"
-    }'
-    ```
+  ```shell
+  curl http://localhost:8080/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -H "Authorization: Bearer no-key" \
+  -d '{
+          "input": ["hello", "world"],
+          "model":"GPT-4",
+          "encoding_format": "float"
+  }'
+  ```
 
 ### GET `/slots`: Returns the current slots processing state
 
@@ -712,56 +806,73 @@ Example:
 
 ```json
 [
-    {
-        "dynatemp_exponent": 1.0,
-        "dynatemp_range": 0.0,
-        "frequency_penalty": 0.0,
-        "grammar": "",
-        "id": 0,
-        "ignore_eos": false,
-        "is_processing": false,
-        "logit_bias": [],
-        "min_p": 0.05000000074505806,
-        "mirostat": 0,
-        "mirostat_eta": 0.10000000149011612,
-        "mirostat_tau": 5.0,
-        "model": "llama-2-7b-32k-instruct.Q2_K.gguf",
-        "n_ctx": 2048,
-        "n_keep": 0,
-        "n_predict": 100000,
-        "n_probs": 0,
-        "next_token": {
-            "has_next_token": true,
-            "n_remain": -1,
-            "n_decoded": 0,
-            "stopped_eos": false,
-            "stopped_limit": false,
-            "stopped_word": false,
-            "stopping_word": ""
-        },
-        "penalize_nl": true,
-        "presence_penalty": 0.0,
-        "prompt": "Say hello to llama.cpp",
-        "repeat_last_n": 64,
-        "repeat_penalty": 1.100000023841858,
-        "samplers": [
-            "top_k",
-            "typical_p",
-            "top_p",
-            "min_p",
-            "temperature"
-        ],
-        "seed": 42,
-        "stop": [
-            "\n"
-        ],
-        "stream": false,
-        "task_id": 0,
-        "temperature": 0.0,
-        "top_k": 40,
-        "top_p": 0.949999988079071,
-        "typical_p": 1.0
+  {
+    "id": 0,
+    "id_task": -1,
+    "n_ctx": 1024,
+    "speculative": false,
+    "is_processing": false,
+    "params": {
+      "n_predict": -1,
+      "seed": 4294967295,
+      "temperature": 0.800000011920929,
+      "dynatemp_range": 0.0,
+      "dynatemp_exponent": 1.0,
+      "top_k": 40,
+      "top_p": 0.949999988079071,
+      "min_p": 0.05000000074505806,
+      "xtc_probability": 0.0,
+      "xtc_threshold": 0.10000000149011612,
+      "typical_p": 1.0,
+      "repeat_last_n": 64,
+      "repeat_penalty": 1.0,
+      "presence_penalty": 0.0,
+      "frequency_penalty": 0.0,
+      "dry_multiplier": 0.0,
+      "dry_base": 1.75,
+      "dry_allowed_length": 2,
+      "dry_penalty_last_n": -1,
+      "dry_sequence_breakers": [
+        "\n",
+        ":",
+        "\"",
+        "*"
+      ],
+      "mirostat": 0,
+      "mirostat_tau": 5.0,
+      "mirostat_eta": 0.10000000149011612,
+      "stop": [],
+      "max_tokens": -1,
+      "n_keep": 0,
+      "n_discard": 0,
+      "ignore_eos": false,
+      "stream": true,
+      "n_probs": 0,
+      "min_keep": 0,
+      "grammar": "",
+      "samplers": [
+        "dry",
+        "top_k",
+        "typ_p",
+        "top_p",
+        "min_p",
+        "xtc",
+        "temperature"
+      ],
+      "speculative.n_max": 16,
+      "speculative.n_min": 5,
+      "speculative.p_min": 0.8999999761581421,
+      "timings_per_token": false
+    },
+    "prompt": "",
+    "next_token": {
+      "has_next_token": true,
+      "has_new_line": false,
+      "n_remain": -1,
+      "n_decoded": 0,
+      "stopping_word": ""
     }
+  }
 ]
 ```
 
@@ -781,9 +892,9 @@ Available metrics:
 
 ### POST `/slots/{id_slot}?action=save`: Save the prompt cache of the specified slot to a file.
 
-    *Options:*
+*Options:*
 
-    `filename`: Name of the file to save the slot's prompt cache. The file will be saved in the directory specified by the `--slot-save-path` server parameter.
+`filename`: Name of the file to save the slot's prompt cache. The file will be saved in the directory specified by the `--slot-save-path` server parameter.
 
 **Response format**
 
@@ -801,9 +912,9 @@ Available metrics:
 
 ### POST `/slots/{id_slot}?action=restore`: Restore the prompt cache of the specified slot from a file.
 
-    *Options:*
+*Options:*
 
-    `filename`: Name of the file to restore the slot's prompt cache from. The file should be located in the directory specified by the `--slot-save-path` server parameter.
+`filename`: Name of the file to restore the slot's prompt cache from. The file should be located in the directory specified by the `--slot-save-path` server parameter.
 
 **Response format**
 

examples/server/deps.sh

@@ -1,25 +0,0 @@
-#!/bin/bash
-# Download and update deps for binary
-
-# get the directory of this script file
-DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
-PUBLIC=$DIR/public
-
-echo "download js bundle files"
-
-# Note for contributors: Always pin to a specific version "maj.min.patch" to avoid breaking the CI
-
-curl -L https://cdn.tailwindcss.com/3.4.14 > $PUBLIC/deps_tailwindcss.js
-echo >> $PUBLIC/deps_tailwindcss.js # add newline
-
-curl -L https://cdnjs.cloudflare.com/ajax/libs/daisyui/4.12.14/styled.min.css > $PUBLIC/deps_daisyui.min.css
-curl -L https://cdnjs.cloudflare.com/ajax/libs/daisyui/4.12.14/themes.min.css >> $PUBLIC/deps_daisyui.min.css
-echo >> $PUBLIC/deps_daisyui.min.css # add newline
-
-curl -L https://unpkg.com/vue@3.5.12/dist/vue.esm-browser.js > $PUBLIC/deps_vue.esm-browser.js
-echo >> $PUBLIC/deps_vue.esm-browser.js # add newline
-
-curl -L https://cdnjs.cloudflare.com/ajax/libs/markdown-it/13.0.2/markdown-it.js > $PUBLIC/deps_markdown-it.js
-echo >> $PUBLIC/deps_markdown-it.js # add newline
-
-ls -lah $PUBLIC

examples/server/public/completion.js

@@ -1,225 +0,0 @@
-const paramDefaults = {
-  stream: true,
-  temperature: 0.2,
-};
-
-let generation_settings = null;
-
-export class CompletionError extends Error {
-  constructor(message, name, data) {
-    super(message);
-    this.name = name;
-  }
-};
-
-// Completes the prompt as a generator. Recommended for most use cases.
-//
-// Example:
-//
-//    import { llama } from '/completion.js'
-//
-//    const request = llama("Tell me a joke", {n_predict: 800})
-//    for await (const chunk of request) {
-//      document.write(chunk.data.content)
-//    }
-//
-export async function* llama(prompt, params = {}, config = {}) {
-  let controller = config.controller;
-  const api_url = config.api_url?.replace(/\/+$/, '') || "";
-
-  if (!controller) {
-    controller = new AbortController();
-  }
-
-  const completionParams = { ...paramDefaults, ...params, prompt };
-
-  const response = await fetch(`${api_url}${config.endpoint || '/completion'}`, {
-    method: 'POST',
-    body: JSON.stringify(completionParams),
-    headers: {
-      'Connection': 'keep-alive',
-      'Content-Type': 'application/json',
-      'Accept': 'text/event-stream',
-      ...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {})
-    },
-    signal: controller.signal,
-  });
-
-  const status = response.status;
-  if (status !== 200) {
-    try {
-      const body = await response.json();
-      if (body && body.error && body.error.message) {
-        throw new CompletionError(body.error.message, 'ServerError');
-      }
-    } catch (err) {
-      throw new CompletionError(err.message, 'ServerError');
-    }
-  }
-
-  const reader = response.body.getReader();
-  const decoder = new TextDecoder();
-
-  let content = "";
-  let leftover = ""; // Buffer for partially read lines
-
-  try {
-    let cont = true;
-
-    while (cont) {
-      const result = await reader.read();
-      if (result.done) {
-        break;
-      }
-
-      // Add any leftover data to the current chunk of data
-      const text = leftover + decoder.decode(result.value);
-
-      // Check if the last character is a line break
-      const endsWithLineBreak = text.endsWith('\n');
-
-      // Split the text into lines
-      let lines = text.split('\n');
-
-      // If the text doesn't end with a line break, then the last line is incomplete
-      // Store it in leftover to be added to the next chunk of data
-      if (!endsWithLineBreak) {
-        leftover = lines.pop();
-      } else {
-        leftover = ""; // Reset leftover if we have a line break at the end
-      }
-
-      // Parse all sse events and add them to result
-      const regex = /^(\S+):\s(.*)$/gm;
-      for (const line of lines) {
-        const match = regex.exec(line);
-        if (match) {
-          result[match[1]] = match[2];
-          if (result.data === '[DONE]') {
-            cont = false;
-            break;
-          }
-
-          // since we know this is llama.cpp, let's just decode the json in data
-          if (result.data) {
-            result.data = JSON.parse(result.data);
-            content += result.data.content;
-
-            // yield
-            yield result;
-
-            // if we got a stop token from server, we will break here
-            if (result.data.stop) {
-              if (result.data.generation_settings) {
-                generation_settings = result.data.generation_settings;
-              }
-              cont = false;
-              break;
-            }
-          }
-          if (result.error) {
-            try {
-              result.error = JSON.parse(result.error);
-              if (result.error.message.includes('slot unavailable')) {
-                // Throw an error to be caught by upstream callers
-                throw new Error('slot unavailable');
-              } else {
-                console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`);
-              }
-            } catch(e) {
-              console.error(`llama.cpp error ${result.error}`)
-            }
-          }
-        }
-      }
-    }
-  } catch (e) {
-    if (e.name !== 'AbortError') {
-      console.error("llama error: ", e);
-    }
-    throw e;
-  }
-  finally {
-    controller.abort();
-  }
-
-  return content;
-}
-
-// Call llama, return an event target that you can subscribe to
-//
-// Example:
-//
-//    import { llamaEventTarget } from '/completion.js'
-//
-//    const conn = llamaEventTarget(prompt)
-//    conn.addEventListener("message", (chunk) => {
-//      document.write(chunk.detail.content)
-//    })
-//
-export const llamaEventTarget = (prompt, params = {}, config = {}) => {
-  const eventTarget = new EventTarget();
-  (async () => {
-    let content = "";
-    for await (const chunk of llama(prompt, params, config)) {
-      if (chunk.data) {
-        content += chunk.data.content;
-        eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data }));
-      }
-      if (chunk.data.generation_settings) {
-        eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings }));
-      }
-      if (chunk.data.timings) {
-        eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings }));
-      }
-    }
-    eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } }));
-  })();
-  return eventTarget;
-}
-
-// Call llama, return a promise that resolves to the completed text. This does not support streaming
-//
-// Example:
-//
-//     llamaPromise(prompt).then((content) => {
-//       document.write(content)
-//     })
-//
-//     or
-//
-//     const content = await llamaPromise(prompt)
-//     document.write(content)
-//
-export const llamaPromise = (prompt, params = {}, config = {}) => {
-  return new Promise(async (resolve, reject) => {
-    let content = "";
-    try {
-      for await (const chunk of llama(prompt, params, config)) {
-        content += chunk.data.content;
-      }
-      resolve(content);
-    } catch (error) {
-      reject(error);
-    }
-  });
-};
-
-/**
- * (deprecated)
- */
-export const llamaComplete = async (params, controller, callback) => {
-  for await (const chunk of llama(params.prompt, params, { controller })) {
-    callback(chunk);
-  }
-}
-
-// Get the model info from the server. This is useful for getting the context window and so on.
-export const llamaModelInfo = async (config = {}) => {
-  if (!generation_settings) {
-    const api_url = config.api_url?.replace(/\/+$/, '') || "";
-    const props = await fetch(`${api_url}/props`).then(r => r.json());
-    generation_settings = props.default_generation_settings;
-  }
-  return generation_settings;
-}

examples/server/public/index.html

@@ -1,730 +0,0 @@
-<html>
-<head>
-  <meta charset="UTF-8">
-  <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
-  <meta name="color-scheme" content="light dark">
-  <title>🦙 llama.cpp - chat</title>
-
-  <!-- Note: dependencies can de updated using ./deps.sh script -->
-  <link href="./deps_daisyui.min.css" rel="stylesheet" type="text/css" />
-  <script src="./deps_tailwindcss.js"></script>
-  <style type="text/tailwindcss">
-    .markdown {
-      h1, h2, h3, h4, h5, h6, ul, ol, li { all: revert; }
-      pre {
-        @apply whitespace-pre-wrap rounded-lg p-2;
-        border: 1px solid currentColor;
-      }
-      /* TODO: fix markdown table */
-    }
-    /*
-      Note for daisyui: because we're using a subset of daisyui via CDN, many things won't be included
-      We can manually add the missing styles from https://cdnjs.cloudflare.com/ajax/libs/daisyui/4.12.14/full.css
-    */
-    .bg-base-100 {background-color: var(--fallback-b1,oklch(var(--b1)/1))}
-    .bg-base-200 {background-color: var(--fallback-b2,oklch(var(--b2)/1))}
-    .bg-base-300 {background-color: var(--fallback-b3,oklch(var(--b3)/1))}
-    .text-base-content {color: var(--fallback-bc,oklch(var(--bc)/1))}
-    .show-on-hover {
-      @apply opacity-0 group-hover:opacity-100;
-    }
-    .btn-mini {
-      @apply cursor-pointer hover:shadow-md;
-    }
-    .chat-screen { max-width: 900px; }
-    /* because the default bubble color is quite dark, we will make a custom one using bg-base-300 */
-    .chat-bubble-base-300 {
-      --tw-bg-opacity: 1;
-      --tw-text-opacity: 1;
-      @apply bg-base-300 text-base-content;
-    }
-  </style>
-</head>
-
-<body>
-  <div id="app" class="flex flex-row opacity-0"> <!-- opacity-0 will be removed on app mounted -->
-    <!-- sidebar -->
-    <div class="flex flex-col bg-black bg-opacity-5 w-64 py-8 px-4 h-screen overflow-y-auto">
-      <h2 class="font-bold mb-4 ml-4">Conversations</h2>
-
-      <!-- list of conversations -->
-      <div :class="{
-        'btn btn-ghost justify-start': true,
-        'btn-active': messages.length === 0,
-      }" @click="newConversation">
-        + New conversation
-      </div>
-      <div v-for="conv in conversations" :class="{
-        'btn btn-ghost justify-start font-normal': true,
-        'btn-active': conv.id === viewingConvId,
-      }" @click="setViewingConv(conv.id)">
-        <span class="truncate">{{ conv.messages[0].content }}</span>
-      </div>
-      <div class="text-center text-xs opacity-40 mt-auto mx-4">
-        Conversations are saved to browser's localStorage
-      </div>
-    </div>
-
-    <div class="chat-screen flex flex-col w-screen h-screen px-8 mx-auto">
-      <!-- header -->
-      <div class="flex flex-row items-center">
-        <div class="grow text-2xl font-bold mt-8 mb-6">
-          🦙 llama.cpp - chat
-        </div>
-
-        <!-- action buttons (top right) -->
-        <div class="flex items-center">
-          <button v-if="messages.length > 0" class="btn mr-1" @click="deleteConv(viewingConvId)" :disabled="isGenerating">
-            <!-- delete conversation button -->
-            <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-trash" viewBox="0 0 16 16">
-              <path d="M5.5 5.5A.5.5 0 0 1 6 6v6a.5.5 0 0 1-1 0V6a.5.5 0 0 1 .5-.5m2.5 0a.5.5 0 0 1 .5.5v6a.5.5 0 0 1-1 0V6a.5.5 0 0 1 .5-.5m3 .5a.5.5 0 0 0-1 0v6a.5.5 0 0 0 1 0z"/>
-              <path d="M14.5 3a1 1 0 0 1-1 1H13v9a2 2 0 0 1-2 2H5a2 2 0 0 1-2-2V4h-.5a1 1 0 0 1-1-1V2a1 1 0 0 1 1-1H6a1 1 0 0 1 1-1h2a1 1 0 0 1 1 1h3.5a1 1 0 0 1 1 1zM4.118 4 4 4.059V13a1 1 0 0 0 1 1h6a1 1 0 0 0 1-1V4.059L11.882 4zM2.5 3h11V2h-11z"/>
-            </svg>
-          </button>
-          <button v-if="messages.length > 0" class="btn mr-1" @click="downloadConv(viewingConvId)" :disabled="isGenerating">
-              <!-- download conversation button -->
-              <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-download" viewBox="0 0 16 16">
-                  <path d="M.5 9.9a.5.5 0 0 1 .5.5v2.5a1 1 0 0 0 1 1h12a1 1 0 0 0 1-1v-2.5a.5.5 0 0 1 1 0v2.5a2 2 0 0 1-2 2H2a2 2 0 0 1-2-2v-2.5a.5.5 0 0 1 .5-.5"/>
-                  <path d="M7.646 11.854a.5.5 0 0 0 .708 0l3-3a.5.5 0 0 0-.708-.708L8.5 10.293V1.5a.5.5 0 0 0-1 0v8.793L5.354 8.146a.5.5 0 1 0-.708.708z"/>
-            </svg>
-          </button>
-          <button class="btn" @click="showConfigDialog = true" :disabled="isGenerating">
-            <!-- edit config button -->
-            <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-gear" viewBox="0 0 16 16">
-              <path d="M8 4.754a3.246 3.246 0 1 0 0 6.492 3.246 3.246 0 0 0 0-6.492M5.754 8a2.246 2.246 0 1 1 4.492 0 2.246 2.246 0 0 1-4.492 0"/>
-              <path d="M9.796 1.343c-.527-1.79-3.065-1.79-3.592 0l-.094.319a.873.873 0 0 1-1.255.52l-.292-.16c-1.64-.892-3.433.902-2.54 2.541l.159.292a.873.873 0 0 1-.52 1.255l-.319.094c-1.79.527-1.79 3.065 0 3.592l.319.094a.873.873 0 0 1 .52 1.255l-.16.292c-.892 1.64.901 3.434 2.541 2.54l.292-.159a.873.873 0 0 1 1.255.52l.094.319c.527 1.79 3.065 1.79 3.592 0l.094-.319a.873.873 0 0 1 1.255-.52l.292.16c1.64.893 3.434-.902 2.54-2.541l-.159-.292a.873.873 0 0 1 .52-1.255l.319-.094c1.79-.527 1.79-3.065 0-3.592l-.319-.094a.873.873 0 0 1-.52-1.255l.16-.292c.893-1.64-.902-3.433-2.541-2.54l-.292.159a.873.873 0 0 1-1.255-.52zm-2.633.283c.246-.835 1.428-.835 1.674 0l.094.319a1.873 1.873 0 0 0 2.693 1.115l.291-.16c.764-.415 1.6.42 1.184 1.185l-.159.292a1.873 1.873 0 0 0 1.116 2.692l.318.094c.835.246.835 1.428 0 1.674l-.319.094a1.873 1.873 0 0 0-1.115 2.693l.16.291c.415.764-.42 1.6-1.185 1.184l-.291-.159a1.873 1.873 0 0 0-2.693 1.116l-.094.318c-.246.835-1.428.835-1.674 0l-.094-.319a1.873 1.873 0 0 0-2.692-1.115l-.292.16c-.764.415-1.6-.42-1.184-1.185l.159-.291A1.873 1.873 0 0 0 1.945 8.93l-.319-.094c-.835-.246-.835-1.428 0-1.674l.319-.094A1.873 1.873 0 0 0 3.06 4.377l-.16-.292c-.415-.764.42-1.6 1.185-1.184l.292.159a1.873 1.873 0 0 0 2.692-1.115z"/>
-            </svg>
-          </button>
-
-          <!-- theme controller is copied from https://daisyui.com/components/theme-controller/ -->
-          <div class="dropdown dropdown-end dropdown-bottom">
-            <div tabindex="0" role="button" class="btn m-1">
-              Theme
-              <svg width="12px" height="12px" class="inline-block h-2 w-2 fill-current opacity-60" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 2048 2048">
-                <path d="M1799 349l242 241-1017 1017L7 590l242-241 775 775 775-775z"></path>
-              </svg>
-            </div>
-            <ul tabindex="0" class="dropdown-content bg-base-300 rounded-box z-[1] w-52 p-2 shadow-2xl h-80 overflow-y-auto">
-              <li>
-                <button
-                  class="btn btn-sm btn-block w-full btn-ghost justify-start"
-                  :class="{ 'btn-active': selectedTheme === 'auto' }"
-                  @click="setSelectedTheme('auto')">
-                  auto
-                </button>
-              </li>
-              <li v-for="theme in themes">
-                <input
-                  type="radio"
-                  name="theme-dropdown"
-                  class="theme-controller btn btn-sm btn-block w-full btn-ghost justify-start"
-                  :aria-label="theme"
-                  :value="theme"
-                  :checked="selectedTheme === theme"
-                  @click="setSelectedTheme(theme)" />
-              </li>
-            </ul>
-          </div>
-        </div>
-      </div>
-
-      <!-- chat messages -->
-      <div id="messages-list" class="flex flex-col grow overflow-y-auto">
-        <div class="mt-auto flex justify-center">
-          <!-- placeholder to shift the message to the bottom -->
-           {{ messages.length === 0 ? 'Send a message to start' : '' }}
-        </div>
-        <div v-for="msg in messages" class="group">
-          <div :class="{
-            'chat': true,
-            'chat-start': msg.role !== 'user',
-            'chat-end': msg.role === 'user',
-          }">
-            <div :class="{
-              'chat-bubble markdown': true,
-              'chat-bubble-base-300': msg.role !== 'user',
-            }">
-              <!-- textarea for editing message -->
-              <template v-if="editingMsg && editingMsg.id === msg.id">
-                <textarea
-                  class="textarea textarea-bordered bg-base-100 text-base-content w-96"
-                  v-model="msg.content"></textarea>
-                <br/>
-                <button class="btn btn-ghost mt-2 mr-2" @click="editingMsg = null">Cancel</button>
-                <button class="btn mt-2" @click="editUserMsgAndRegenerate(msg)">Submit</button>
-              </template>
-              <!-- render message as markdown -->
-              <vue-markdown v-else :source="msg.content" />
-            </div>
-          </div>
-
-          <!-- actions for each message -->
-          <div :class="{'text-right': msg.role === 'user'}" class="mx-4 mt-2 mb-2">
-            <!-- user message -->
-            <button v-if="msg.role === 'user'" class="badge btn-minishow-on-hover " @click="editingMsg = msg" :disabled="isGenerating">
-              ✍️ Edit
-            </button>
-            <!-- assistant message -->
-            <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="regenerateMsg(msg)" :disabled="isGenerating">
-              🔄 Regenerate
-            </button>
-            <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="copyMsg(msg)" :disabled="isGenerating">
-              📋 Copy
-            </button>
-          </div>
-        </div>
-
-        <!-- pending (ongoing) assistant message -->
-        <div id="pending-msg" class="chat chat-start">
-          <div v-if="pendingMsg" class="chat-bubble markdown chat-bubble-base-300">
-            <span v-if="!pendingMsg.content" class="loading loading-dots loading-md"></span>
-            <vue-markdown v-else :source="pendingMsg.content" />
-          </div>
-        </div>
-      </div>
-
-      <!-- chat input -->
-      <div class="flex flex-row items-center mt-8 mb-6">
-        <textarea
-          class="textarea textarea-bordered w-full"
-          placeholder="Type a message (Shift+Enter to add a new line)"
-          v-model="inputMsg"
-          @keydown.enter.exact.prevent="sendMessage"
-          @keydown.enter.shift.exact.prevent="inputMsg += '\n'"
-          :disabled="isGenerating"
-          id="msg-input"
-        ></textarea>
-        <button v-if="!isGenerating" class="btn btn-primary ml-2" @click="sendMessage" :disabled="inputMsg.length === 0">Send</button>
-        <button v-else class="btn btn-neutral ml-2" @click="stopGeneration">Stop</button>
-      </div>
-    </div>
-
-    <!-- modal for editing config -->
-    <dialog class="modal" :class="{'modal-open': showConfigDialog}">
-      <div class="modal-box">
-        <h3 class="text-lg font-bold mb-6">Settings</h3>
-        <div class="h-[calc(90vh-12rem)] overflow-y-auto">
-          <p class="opacity-40 mb-6">Settings below are saved in browser's localStorage</p>
-          <settings-modal-short-input :config-key="'apiKey'" :config-default="configDefault" :config-info="configInfo" v-model="config.apiKey"></settings-modal-short-input>
-          <label class="form-control mb-2">
-            <div class="label">System Message</div>
-            <textarea class="textarea textarea-bordered h-24" :placeholder="'Default: ' + configDefault.systemMessage" v-model="config.systemMessage"></textarea>
-          </label>
-          <template v-for="configKey in ['temperature', 'top_k', 'top_p', 'min_p', 'max_tokens']">
-            <settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]" />
-          </template>
-          <!-- TODO: add more sampling-related configs, please regroup them into different "collapse" sections -->
-          <!-- Section: Other sampler settings -->
-          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary class="collapse-title font-bold">Other sampler settings</summary>
-            <div class="collapse-content">
-              <!-- Samplers queue -->
-              <settings-modal-short-input label="Samplers queue" :config-key="'samplers'" :config-default="configDefault" :config-info="configInfo" v-model="config.samplers"></settings-modal-short-input>
-              <!-- Samplers -->
-              <template v-for="configKey in ['dynatemp_range', 'dynatemp_exponent', 'typical_p', 'xtc_probability', 'xtc_threshold']">
-                <settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]" />
-              </template>
-            </div>
-          </details>
-          <!-- Section: Penalties settings -->
-          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary class="collapse-title font-bold">Penalties settings</summary>
-            <div class="collapse-content">
-              <template v-for="configKey in ['repeat_last_n', 'repeat_penalty', 'presence_penalty', 'frequency_penalty', 'dry_multiplier', 'dry_base', 'dry_allowed_length', 'dry_penalty_last_n']">
-                <settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]" />
-              </template>
-            </div>
-          </details>
-          <!-- Section: Advanced config -->
-          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
-            <summary class="collapse-title font-bold">Advanced config</summary>
-            <div class="collapse-content">
-              <label class="form-control mb-2">
-                <!-- Custom parameters input -->
-                <div class="label inline">Custom JSON config (For more info, refer to <a class="underline" href="https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md" target="_blank" rel="noopener noreferrer">server documentation</a>)</div>
-                <textarea class="textarea textarea-bordered h-24" placeholder="Example: { &quot;mirostat&quot;: 1, &quot;min_p&quot;: 0.1 }" v-model="config.custom"></textarea>
-              </label>
-            </div>
-          </details>
-        </div>
-
-        <!-- action buttons -->
-        <div class="modal-action">
-          <button class="btn" @click="resetConfigDialog">Reset to default</button>
-          <button class="btn" @click="closeAndDiscardConfigDialog">Close</button>
-          <button class="btn btn-primary" @click="closeAndSaveConfigDialog">Save and close</button>
-        </div>
-      </div>
-    </dialog>
-  </div>
-
-  <!-- Template to be used by settings modal -->
-  <template id="settings-modal-short-input">
-    <label class="input input-bordered join-item grow flex items-center gap-2 mb-2">
-      <!-- Show help message on hovering on the input label -->
-      <div class="dropdown dropdown-hover">
-        <div tabindex="0" role="button" class="font-bold">{{ label || configKey }}</div>
-        <div class="dropdown-content menu bg-base-100 rounded-box z-10 w-64 p-2 shadow mt-4">
-          {{ configInfo[configKey] || '(no help message available)' }}
-        </div>
-      </div>
-      <!-- Here we forward v-model from parent to child component, see: https://stackoverflow.com/questions/47311936/v-model-and-child-components -->
-      <input type="text" class="grow" :placeholder="'Default: ' + (configDefault[configKey] || 'none')" :value="modelValue" @input="$emit('update:modelValue', $event.target.value)" />
-    </label>
-  </template>
-
-  <script src="./deps_markdown-it.js"></script>
-  <script type="module">
-    import { createApp, defineComponent, shallowRef, computed, h } from './deps_vue.esm-browser.js';
-    import { llama } from './completion.js';
-
-    // utility functions
-    const isString = (x) => !!x.toLowerCase;
-    const isNumeric = (n) => !isString(n) && !isNaN(n);
-    const escapeAttr = (str) => str.replace(/>/g, '&gt;').replace(/"/g, '&quot;');
-    const copyStr = (str) => navigator.clipboard.writeText(str);
-
-    // constants
-    const BASE_URL = localStorage.getItem('base') // for debugging
-      || (new URL('.', document.baseURI).href).toString(); // for production
-    const CONFIG_DEFAULT = {
-      // Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value. Do not use null or undefined for default value.
-      apiKey: '',
-      systemMessage: 'You are a helpful assistant.',
-      // make sure these default values are in sync with `common.h`
-      samplers: 'dkypmxt',
-      temperature: 0.8,
-      dynatemp_range: 0.0,
-      dynatemp_exponent: 1.0,
-      top_k: 40,
-      top_p: 0.95,
-      min_p: 0.05,
-      xtc_probability: 0.0,
-      xtc_threshold: 0.1,
-      typical_p: 1.0,
-      repeat_last_n: 64,
-      repeat_penalty: 1.0,
-      presence_penalty: 0.0,
-      frequency_penalty: 0.0,
-      dry_multiplier: 0.0,
-      dry_base: 1.75,
-      dry_allowed_length: 2,
-      dry_penalty_last_n: -1,
-      max_tokens: -1,
-      custom: '', // custom json-stringified object
-    };
-    const CONFIG_INFO = {
-      apiKey: 'Set the API Key if you are using --api-key option for the server.',
-      systemMessage: 'The starting message that defines how model should behave.',
-      samplers: 'The order at which samplers are applied, in simplified way. Default is "dkypmxt": dry->top_k->typ_p->top_p->min_p->xtc->temperature',
-      temperature: 'Controls the randomness of the generated text by affecting the probability distribution of the output tokens. Higher = more random, lower = more focused.',
-      dynatemp_range: 'Addon for the temperature sampler. The added value to the range of dynamic temperature, which adjusts probabilities by entropy of tokens.',
-      dynatemp_exponent: 'Addon for the temperature sampler. Smoothes out the probability redistribution based on the most probable token.',
-      top_k: 'Keeps only k top tokens.',
-      top_p: 'Limits tokens to those that together have a cumulative probability of at least p',
-      min_p: 'Limits tokens based on the minimum probability for a token to be considered, relative to the probability of the most likely token.',
-      xtc_probability: 'XTC sampler cuts out top tokens; this parameter controls the chance of cutting tokens at all. 0 disables XTC.',
-      xtc_threshold: 'XTC sampler cuts out top tokens; this parameter controls the token probability that is required to cut that token.',
-      typical_p: 'Sorts and limits tokens based on the difference between log-probability and entropy.',
-      repeat_last_n: 'Last n tokens to consider for penalizing repetition',
-      repeat_penalty: 'Controls the repetition of token sequences in the generated text',
-      presence_penalty: 'Limits tokens based on whether they appear in the output or not.',
-      frequency_penalty: 'Limits tokens based on how often they appear in the output.',
-      dry_multiplier: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling multiplier.',
-      dry_base: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling base value.',
-      dry_allowed_length: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the allowed length for DRY sampling.',
-      dry_penalty_last_n: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets DRY penalty for the last n tokens.',
-      max_tokens: 'The maximum number of token per output.',
-      custom: '', // custom json-stringified object
-    };
-    // config keys having numeric value (i.e. temperature, top_k, top_p, etc)
-    const CONFIG_NUMERIC_KEYS = Object.entries(CONFIG_DEFAULT).filter(e => isNumeric(e[1])).map(e => e[0]);
-    // list of themes supported by daisyui
-    const THEMES = ['light', 'dark', 'cupcake', 'bumblebee', 'emerald', 'corporate', 'synthwave', 'retro', 'cyberpunk', 'valentine', 'halloween', 'garden', 'forest', 'aqua', 'lofi', 'pastel', 'fantasy', 'wireframe', 'black', 'luxury', 'dracula', 'cmyk', 'autumn', 'business', 'acid', 'lemonade', 'night', 'coffee', 'winter', 'dim', 'nord', 'sunset'];
-
-    // markdown support
-    const VueMarkdown = defineComponent(
-      (props) => {
-        const md = shallowRef(new markdownit({ breaks: true }));
-        const origFenchRenderer = md.value.renderer.rules.fence;
-        md.value.renderer.rules.fence = (tokens, idx, ...args) => {
-          const content = tokens[idx].content;
-          const origRendered = origFenchRenderer(tokens, idx, ...args);
-          return `<div class="relative my-4">
-            <div class="text-right sticky top-4 mb-2 mr-2 h-0">
-              <button class="badge btn-mini" onclick="copyStr(${escapeAttr(JSON.stringify(content))})">📋 Copy</button>
-            </div>
-            ${origRendered}
-          </div>`;
-        };
-        window.copyStr = copyStr;
-        const content = computed(() => md.value.render(props.source));
-        return () => h("div", { innerHTML: content.value });
-      },
-      { props: ["source"] }
-    );
-
-    // input field to be used by settings modal
-    const SettingsModalShortInput = defineComponent({
-      template: document.getElementById('settings-modal-short-input').innerHTML,
-      props: {
-        label: { type: String, required: false },
-        configKey: String,
-        configDefault: Object,
-        configInfo: Object,
-        modelValue: [Object, String, Number],
-      },
-    });
-
-    // coversations is stored in localStorage
-    // format: { [convId]: { id: string, lastModified: number, messages: [...] } }
-    // convId is a string prefixed with 'conv-'
-    const StorageUtils = {
-      // manage conversations
-      getAllConversations() {
-        const res = [];
-        for (const key in localStorage) {
-          if (key.startsWith('conv-')) {
-            res.push(JSON.parse(localStorage.getItem(key)));
-          }
-        }
-        res.sort((a, b) => b.lastModified - a.lastModified);
-        return res;
-      },
-      // can return null if convId does not exist
-      getOneConversation(convId) {
-        return JSON.parse(localStorage.getItem(convId) || 'null');
-      },
-      // if convId does not exist, create one
-      appendMsg(convId, msg) {
-        if (msg.content === null) return;
-        const conv = StorageUtils.getOneConversation(convId) || {
-          id: convId,
-          lastModified: Date.now(),
-          messages: [],
-        };
-        conv.messages.push(msg);
-        conv.lastModified = Date.now();
-        localStorage.setItem(convId, JSON.stringify(conv));
-      },
-      getNewConvId() {
-        return `conv-${Date.now()}`;
-      },
-      remove(convId) {
-        localStorage.removeItem(convId);
-      },
-      filterAndKeepMsgs(convId, predicate) {
-        const conv = StorageUtils.getOneConversation(convId);
-        if (!conv) return;
-        conv.messages = conv.messages.filter(predicate);
-        conv.lastModified = Date.now();
-        localStorage.setItem(convId, JSON.stringify(conv));
-      },
-      popMsg(convId) {
-        const conv = StorageUtils.getOneConversation(convId);
-        if (!conv) return;
-        const msg = conv.messages.pop();
-        conv.lastModified = Date.now();
-        if (conv.messages.length === 0) {
-          StorageUtils.remove(convId);
-        } else {
-          localStorage.setItem(convId, JSON.stringify(conv));
-        }
-        return msg;
-      },
-
-      // manage config
-      getConfig() {
-        const savedVal = JSON.parse(localStorage.getItem('config') || '{}');
-        // to prevent breaking changes in the future, we always provide default value for missing keys
-        return {
-          ...CONFIG_DEFAULT,
-          ...savedVal,
-        };
-      },
-      setConfig(config) {
-        localStorage.setItem('config', JSON.stringify(config));
-      },
-      getTheme() {
-        return localStorage.getItem('theme') || 'auto';
-      },
-      setTheme(theme) {
-        if (theme === 'auto') {
-          localStorage.removeItem('theme');
-        } else {
-          localStorage.setItem('theme', theme);
-        }
-      },
-    };
-
-    // scroll to bottom of chat messages
-    // if requiresNearBottom is true, only auto-scroll if user is near bottom
-    const chatScrollToBottom = (requiresNearBottom) => {
-      const msgListElem = document.getElementById('messages-list');
-      const spaceToBottom = msgListElem.scrollHeight - msgListElem.scrollTop - msgListElem.clientHeight;
-      if (!requiresNearBottom || (spaceToBottom < 100)) {
-        setTimeout(() => msgListElem.scrollTo({ top: msgListElem.scrollHeight }), 1);
-      }
-    };
-
-    const mainApp = createApp({
-      components: {
-        VueMarkdown,
-        SettingsModalShortInput,
-      },
-      data() {
-        return {
-          conversations: StorageUtils.getAllConversations(),
-          messages: [], // { id: number, role: 'user' | 'assistant', content: string }
-          viewingConvId: StorageUtils.getNewConvId(),
-          inputMsg: '',
-          isGenerating: false,
-          pendingMsg: null, // the on-going message from assistant
-          stopGeneration: () => {},
-          selectedTheme: StorageUtils.getTheme(),
-          config: StorageUtils.getConfig(),
-          showConfigDialog: false,
-          editingMsg: null,
-          // const
-          themes: THEMES,
-          configDefault: {...CONFIG_DEFAULT},
-          configInfo: {...CONFIG_INFO},
-        }
-      },
-      computed: {},
-      mounted() {
-        document.getElementById('app').classList.remove('opacity-0'); // show app
-        // scroll to the bottom when the pending message height is updated
-        const pendingMsgElem = document.getElementById('pending-msg');
-        const resizeObserver = new ResizeObserver(() => {
-          if (this.isGenerating) chatScrollToBottom(true);
-        });
-        resizeObserver.observe(pendingMsgElem);
-      },
-      methods: {
-        setSelectedTheme(theme) {
-          this.selectedTheme = theme;
-          StorageUtils.setTheme(theme);
-        },
-        newConversation() {
-          if (this.isGenerating) return;
-          this.viewingConvId = StorageUtils.getNewConvId();
-          this.editingMsg = null;
-          this.fetchMessages();
-          chatScrollToBottom();
-        },
-        setViewingConv(convId) {
-          if (this.isGenerating) return;
-          this.viewingConvId = convId;
-          this.editingMsg = null;
-          this.fetchMessages();
-          chatScrollToBottom();
-        },
-        deleteConv(convId) {
-          if (this.isGenerating) return;
-          if (window.confirm('Are you sure to delete this conversation?')) {
-            StorageUtils.remove(convId);
-            if (this.viewingConvId === convId) {
-              this.viewingConvId = StorageUtils.getNewConvId();
-              this.editingMsg = null;
-            }
-            this.fetchConversation();
-            this.fetchMessages();
-          }
-        },
-        downloadConv(convId) {
-          const conversation = StorageUtils.getOneConversation(convId);
-          if (!conversation) {
-            alert('Conversation not found.');
-            return;
-          }
-          const conversationJson = JSON.stringify(conversation, null, 2);
-          const blob = new Blob([conversationJson], { type: 'application/json' });
-          const url = URL.createObjectURL(blob);
-          const a = document.createElement('a');
-          a.href = url;
-          a.download = `conversation_${convId}.json`;
-          document.body.appendChild(a);
-          a.click();
-          document.body.removeChild(a);
-          URL.revokeObjectURL(url);
-        },
-        async sendMessage() {
-          if (!this.inputMsg) return;
-          const currConvId = this.viewingConvId;
-
-          StorageUtils.appendMsg(currConvId, {
-            id: Date.now(),
-            role: 'user',
-            content: this.inputMsg,
-          });
-          this.fetchConversation();
-          this.fetchMessages();
-          this.inputMsg = '';
-          this.editingMsg = null;
-          this.generateMessage(currConvId);
-          chatScrollToBottom();
-        },
-        async generateMessage(currConvId) {
-          if (this.isGenerating) return;
-          this.pendingMsg = { id: Date.now()+1, role: 'assistant', content: null };
-          this.isGenerating = true;
-          this.editingMsg = null;
-
-          try {
-            const abortController = new AbortController();
-            this.stopGeneration = () => abortController.abort();
-            const params = {
-              messages: [
-                { role: 'system', content: this.config.systemMessage },
-                ...this.messages,
-              ],
-              stream: true,
-              cache_prompt: true,
-              samplers: this.config.samplers,
-              temperature: this.config.temperature,
-              dynatemp_range: this.config.dynatemp_range,
-              dynatemp_exponent: this.config.dynatemp_exponent,
-              top_k: this.config.top_k,
-              top_p: this.config.top_p,
-              min_p: this.config.min_p,
-              typical_p: this.config.typical_p,
-              xtc_probability: this.config.xtc_probability,
-              xtc_threshold: this.config.xtc_threshold,
-              repeat_last_n: this.config.repeat_last_n,
-              repeat_penalty: this.config.repeat_penalty,
-              presence_penalty: this.config.presence_penalty,
-              frequency_penalty: this.config.frequency_penalty,
-              dry_multiplier: this.config.dry_multiplier,
-              dry_base: this.config.dry_base,
-              dry_allowed_length: this.config.dry_allowed_length,
-              dry_penalty_last_n: this.config.dry_penalty_last_n,
-              max_tokens: this.config.max_tokens,
-              ...(this.config.custom.length ? JSON.parse(this.config.custom) : {}),
-              ...(this.config.apiKey ? { api_key: this.config.apiKey } : {}),
-            };
-            const config = {
-              controller: abortController,
-              api_url: BASE_URL,
-              endpoint: '/chat/completions',
-            };
-            for await (const chunk of llama(prompt, params, config)) {
-              const stop = chunk.data.stop;
-              const addedContent = chunk.data.choices[0].delta.content;
-              const lastContent = this.pendingMsg.content || '';
-              if (addedContent) {
-                this.pendingMsg = {
-                  id: this.pendingMsg.id,
-                  role: 'assistant',
-                  content: lastContent + addedContent,
-                };
-              }
-            }
-
-            StorageUtils.appendMsg(currConvId, this.pendingMsg);
-            this.fetchConversation();
-            this.fetchMessages();
-            setTimeout(() => document.getElementById('msg-input').focus(), 1);
-          } catch (error) {
-            if (error.name === 'AbortError') {
-              // user stopped the generation via stopGeneration() function
-              StorageUtils.appendMsg(currConvId, this.pendingMsg);
-              this.fetchConversation();
-              this.fetchMessages();
-            } else {
-              console.error(error);
-              alert(error);
-              // pop last user message
-              const lastUserMsg = StorageUtils.popMsg(currConvId);
-              this.inputMsg = lastUserMsg ? lastUserMsg.content : '';
-            }
-          }
-
-          this.pendingMsg = null;
-          this.isGenerating = false;
-          this.stopGeneration = () => {};
-          this.fetchMessages();
-          chatScrollToBottom();
-        },
-
-        // message actions
-        regenerateMsg(msg) {
-          if (this.isGenerating) return;
-          // TODO: somehow keep old history (like how ChatGPT has different "tree"). This can be done by adding "sub-conversations" with "subconv-" prefix, and new message will have a list of subconvIds
-          const currConvId = this.viewingConvId;
-          StorageUtils.filterAndKeepMsgs(currConvId, (m) => m.id < msg.id);
-          this.fetchConversation();
-          this.fetchMessages();
-          this.generateMessage(currConvId);
-        },
-        copyMsg(msg) {
-          copyStr(msg.content);
-        },
-        editUserMsgAndRegenerate(msg) {
-          if (this.isGenerating) return;
-          const currConvId = this.viewingConvId;
-          const newContent = msg.content;
-          this.editingMsg = null;
-          StorageUtils.filterAndKeepMsgs(currConvId, (m) => m.id < msg.id);
-          StorageUtils.appendMsg(currConvId, {
-            id: Date.now(),
-            role: 'user',
-            content: newContent,
-          });
-          this.fetchConversation();
-          this.fetchMessages();
-          this.generateMessage(currConvId);
-        },
-
-        // settings dialog methods
-        closeAndSaveConfigDialog() {
-          try {
-            if (this.config.custom.length) JSON.parse(this.config.custom);
-          } catch (error) {
-            alert('Invalid JSON for custom config. Please either fix it or leave it empty.');
-            return;
-          }
-          for (const key of CONFIG_NUMERIC_KEYS) {
-            if (isNaN(this.config[key]) || this.config[key].toString().trim().length === 0) {
-              alert(`Invalid number for ${key} (expected an integer or a float)`);
-              return;
-            }
-            this.config[key] = parseFloat(this.config[key]);
-          }
-          this.showConfigDialog = false;
-          StorageUtils.setConfig(this.config);
-        },
-        closeAndDiscardConfigDialog() {
-          this.showConfigDialog = false;
-          this.config = StorageUtils.getConfig();
-        },
-        resetConfigDialog() {
-          if (window.confirm('Are you sure to reset all settings?')) {
-            this.config = {...CONFIG_DEFAULT};
-          }
-        },
-
-        // sync state functions
-        fetchConversation() {
-          this.conversations = StorageUtils.getAllConversations();
-        },
-        fetchMessages() {
-          this.messages = StorageUtils.getOneConversation(this.viewingConvId)?.messages ?? [];
-        },
-      },
-    });
-    mainApp.config.errorHandler = alert;
-    try {
-      mainApp.mount('#app');
-    } catch (err) {
-      console.error(err);
-      document.getElementById('app').innerHTML = `<div style="margin:2em auto">
-        Failed to start app. Please try clearing localStorage and try again.<br/>
-        <br/>
-        <button class="btn" onClick="localStorage.clear(); window.location.reload();">Clear localStorage</button>
-      </div>`;
-    }
-  </script>
-</body>
-
-</html>

examples/server/public_legacy/index-new.html

@@ -39,7 +39,6 @@
       temperature: 0.8, // adapt all following parameters to optimized min-p requierements. If for non-english, set to 0.6 or lower
       repeat_last_n: 0, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.0, // 1.0 = disabled
-      penalize_nl: false, // true only useful for infinite completion
       dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
       dry_base: 1.75,     // 0.0 = disabled
       dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well

examples/server/public_legacy/index.html

@@ -303,7 +303,6 @@
       temperature: 0.7,
       repeat_last_n: 256, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.18, // 1.0 = disabled
-      penalize_nl: false,
       dry_multiplier: 0.0, // 0.0 = disabled, 0.8 works well
       dry_base: 1.75,     // 0.0 = disabled
       dry_allowed_length: 2, // tokens extending repetitions beyond this receive penalty, 2 works well
@@ -1006,7 +1005,6 @@
             ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
             ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
             ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
-            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
             ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
             ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
             ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}

examples/server/public_simplechat/simplechat.js

@@ -407,6 +407,9 @@ class SimpleChat {
                 if (curLine.startsWith("data:")) {
                     curLine = curLine.substring(5);
                 }
+                if (curLine.trim() === "[DONE]") {
+                    break;
+                }
                 let curJson = JSON.parse(curLine);
                 console.debug("DBUG:SC:PART:Json:", curJson);
                 this.append_response(this.response_extract_stream(curJson, apiEP));

examples/server/tests/README.md

@@ -44,4 +44,10 @@ To run with stdout/stderr display in real time (verbose output, but useful for d
 DEBUG=1 ./tests.sh -s -v -x
 ```
 
+Hint: You can compile and run test in single command, useful for local developement:
+
+```shell
+cmake --build build -j --target llama-server && ./examples/server/tests/tests.sh
+```
+
 To see all available arguments, please refer to [pytest documentation](https://docs.pytest.org/en/stable/how-to/usage.html)

examples/server/tests/tests.sh

@@ -1,5 +1,9 @@
 #!/bin/bash
 
+# make sure we are in the right directory
+SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
+cd $SCRIPT_DIR
+
 set -eu
 
 if [ $# -lt 1 ]

examples/server/tests/unit/test_basic.py

@@ -1,4 +1,5 @@
 import pytest
+import requests
 from utils import *
 
 server = ServerPreset.tinyllama2()
@@ -22,7 +23,12 @@ def test_server_props():
     server.start()
     res = server.make_request("GET", "/props")
     assert res.status_code == 200
+    assert ".gguf" in res.body["model_path"]
     assert res.body["total_slots"] == server.n_slots
+    default_val = res.body["default_generation_settings"]
+    assert server.n_ctx is not None and server.n_slots is not None
+    assert default_val["n_ctx"] == server.n_ctx / server.n_slots
+    assert default_val["params"]["seed"] == server.seed
 
 
 def test_server_models():
@@ -33,6 +39,31 @@ def test_server_models():
     assert len(res.body["data"]) == 1
     assert res.body["data"][0]["id"] == server.model_alias
 
+
+def test_server_slots():
+    global server
+
+    # without slots endpoint enabled, this should return error
+    server.server_slots = False
+    server.start()
+    res = server.make_request("GET", "/slots")
+    assert res.status_code == 501 # ERROR_TYPE_NOT_SUPPORTED
+    assert "error" in res.body
+    server.stop()
+
+    # with slots endpoint enabled, this should return slots info
+    server.server_slots = True
+    server.n_slots = 2
+    server.start()
+    res = server.make_request("GET", "/slots")
+    assert res.status_code == 200
+    assert len(res.body) == server.n_slots
+    assert server.n_ctx is not None and server.n_slots is not None
+    assert res.body[0]["n_ctx"] == server.n_ctx / server.n_slots
+    assert "params" in res.body[0]
+    assert res.body[0]["params"]["seed"] == server.seed
+
+
 def test_load_split_model():
     global server
     server.model_hf_repo = "ggml-org/models"
@@ -46,3 +77,20 @@ def test_load_split_model():
     })
     assert res.status_code == 200
     assert match_regex("(little|girl)+", res.body["content"])
+
+
+def test_no_webui():
+    global server
+    # default: webui enabled
+    server.start()
+    url = f"http://{server.server_host}:{server.server_port}"
+    res = requests.get(url)
+    assert res.status_code == 200
+    assert "<html>" in res.text
+    server.stop()
+
+    # with --no-webui
+    server.no_webui = True
+    server.start()
+    res = requests.get(url)
+    assert res.status_code == 404

examples/server/tests/unit/test_chat_completion.py

@@ -12,13 +12,13 @@ def create_server():
 
 
 @pytest.mark.parametrize(
-    "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,truncated",
+    "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason",
     [
-        ("llama-2", "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, False),
-        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, False),
+        (None, "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length"),
+        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length"),
     ]
 )
-def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, truncated):
+def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason):
     global server
     server.start()
     res = server.make_request("POST", "/chat/completions", data={
@@ -30,29 +30,28 @@ def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_conte
         ],
     })
     assert res.status_code == 200
+    assert "cmpl" in res.body["id"] # make sure the completion id has the expected format
+    assert res.body["model"] == model if model is not None else server.model_alias
     assert res.body["usage"]["prompt_tokens"] == n_prompt
     assert res.body["usage"]["completion_tokens"] == n_predicted
     choice = res.body["choices"][0]
     assert "assistant" == choice["message"]["role"]
     assert match_regex(re_content, choice["message"]["content"])
-    if truncated:
-        assert choice["finish_reason"] == "length"
-    else:
-        assert choice["finish_reason"] == "stop"
+    assert choice["finish_reason"] == finish_reason
 
 
 @pytest.mark.parametrize(
-    "model,system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,truncated",
+    "system_prompt,user_prompt,max_tokens,re_content,n_prompt,n_predicted,finish_reason",
     [
-        ("llama-2", "Book", "What is the best book", 8, "(Suddenly)+", 77, 8, False),
-        ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, False),
+        ("Book", "What is the best book", 8, "(Suddenly)+", 77, 8, "length"),
+        ("You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length"),
     ]
 )
-def test_chat_completion_stream(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, truncated):
+def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason):
     global server
+    server.model_alias = None # try using DEFAULT_OAICOMPAT_MODEL
     server.start()
     res = server.make_stream_request("POST", "/chat/completions", data={
-        "model": model,
         "max_tokens": max_tokens,
         "messages": [
             {"role": "system", "content": system_prompt},
@@ -61,18 +60,19 @@ def test_chat_completion_stream(model, system_prompt, user_prompt, max_tokens, r
         "stream": True,
     })
     content = ""
+    last_cmpl_id = None
     for data in res:
         choice = data["choices"][0]
+        assert "gpt-3.5" in data["model"] # DEFAULT_OAICOMPAT_MODEL, maybe changed in the future
+        if last_cmpl_id is None:
+            last_cmpl_id = data["id"]
+        assert last_cmpl_id == data["id"] # make sure the completion id is the same for all events in the stream
         if choice["finish_reason"] in ["stop", "length"]:
             assert data["usage"]["prompt_tokens"] == n_prompt
             assert data["usage"]["completion_tokens"] == n_predicted
             assert "content" not in choice["delta"]
             assert match_regex(re_content, content)
-            # FIXME: not sure why this is incorrect in stream mode
-            # if truncated:
-            #   assert choice["finish_reason"] == "length"
-            # else:
-            #   assert choice["finish_reason"] == "stop"
+            assert choice["finish_reason"] == finish_reason
         else:
             assert choice["finish_reason"] is None
             content += choice["delta"]["content"]
@@ -93,7 +93,7 @@ def test_chat_completion_with_openai_library():
         temperature=0.8,
     )
     print(res)
-    assert res.choices[0].finish_reason == "stop"
+    assert res.choices[0].finish_reason == "length"
     assert res.choices[0].message.content is not None
     assert match_regex("(Suddenly)+", res.choices[0].message.content)
 

examples/server/tests/unit/test_completion.py

@@ -25,6 +25,7 @@ def test_completion(prompt: str, n_predict: int, re_content: str, n_prompt: int,
     assert res.body["timings"]["prompt_n"] == n_prompt
     assert res.body["timings"]["predicted_n"] == n_predicted
     assert res.body["truncated"] == truncated
+    assert type(res.body["has_new_line"]) == bool
     assert match_regex(re_content, res.body["content"])
 
 
@@ -42,15 +43,40 @@ def test_completion_stream(prompt: str, n_predict: int, re_content: str, n_promp
     })
     content = ""
     for data in res:
+        assert "stop" in data and type(data["stop"]) == bool
         if data["stop"]:
             assert data["timings"]["prompt_n"] == n_prompt
             assert data["timings"]["predicted_n"] == n_predicted
             assert data["truncated"] == truncated
+            assert data["stop_type"] == "limit"
+            assert type(data["has_new_line"]) == bool
+            assert "generation_settings" in data
+            assert server.n_predict is not None
+            assert data["generation_settings"]["n_predict"] == min(n_predict, server.n_predict)
+            assert data["generation_settings"]["seed"] == server.seed
             assert match_regex(re_content, content)
         else:
             content += data["content"]
 
 
+def test_completion_stream_vs_non_stream():
+    global server
+    server.start()
+    res_stream = server.make_stream_request("POST", "/completion", data={
+        "n_predict": 8,
+        "prompt": "I believe the meaning of life is",
+        "stream": True,
+    })
+    res_non_stream = server.make_request("POST", "/completion", data={
+        "n_predict": 8,
+        "prompt": "I believe the meaning of life is",
+    })
+    content_stream = ""
+    for data in res_stream:
+        content_stream += data["content"]
+    assert content_stream == res_non_stream.body["content"]
+
+
 @pytest.mark.parametrize("n_slots", [1, 2])
 def test_consistent_result_same_seed(n_slots: int):
     global server
@@ -221,3 +247,24 @@ def test_completion_parallel_slots(n_slots: int, n_requests: int):
         assert len(res.body["content"]) > 10
         # FIXME: the result is not deterministic when using other slot than slot 0
         # assert match_regex(re_content, res.body["content"])
+
+
+def test_n_probs():
+    global server
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "I believe the meaning of life is",
+        "n_probs": 10,
+        "temperature": 0.0,
+        "n_predict": 5,
+    })
+    assert res.status_code == 200
+    assert "completion_probabilities" in res.body
+    assert len(res.body["completion_probabilities"]) == 5
+    for tok in res.body["completion_probabilities"]:
+        assert "probs" in tok
+        assert len(tok["probs"]) == 10
+        for prob in tok["probs"]:
+            assert "prob" in prob
+            assert "tok_str" in prob
+            assert 0.0 <= prob["prob"] <= 1.0

examples/server/tests/unit/test_embedding.py

@@ -97,3 +97,33 @@ def test_same_prompt_give_same_result():
         vi = res.body['data'][i]['embedding']
         for x, y in zip(v0, vi):
             assert abs(x - y) < EPSILON
+
+
+@pytest.mark.parametrize(
+    "content,n_tokens",
+    [
+        ("I believe the meaning of life is", 7),
+        ("This is a test", 4),
+    ]
+)
+def test_embedding_usage_single(content, n_tokens):
+    global server
+    server.start()
+    res = server.make_request("POST", "/embeddings", data={"input": content})
+    assert res.status_code == 200
+    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
+    assert res.body['usage']['prompt_tokens'] == n_tokens
+
+
+def test_embedding_usage_multiple():
+    global server
+    server.start()
+    res = server.make_request("POST", "/embeddings", data={
+        "input": [
+            "I believe the meaning of life is",
+            "I believe the meaning of life is",
+        ],
+    })
+    assert res.status_code == 200
+    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
+    assert res.body['usage']['prompt_tokens'] == 2 * 7

examples/server/tests/unit/test_infill.py

@@ -13,28 +13,28 @@ def test_infill_without_input_extra():
     global server
     server.start()
     res = server.make_request("POST", "/infill", data={
-        "prompt": "Complete this",
-        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n    int n_threads = llama_",
+        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
+        "prompt": "    int n_threads = llama_",
         "input_suffix": "}\n",
     })
     assert res.status_code == 200
-    assert match_regex("(One|day|she|saw|big|scary|bird)+", res.body["content"])
+    assert match_regex("(Ann|small|shiny)+", res.body["content"])
 
 
 def test_infill_with_input_extra():
     global server
     server.start()
     res = server.make_request("POST", "/infill", data={
-        "prompt": "Complete this",
         "input_extra": [{
             "filename": "llama.h",
             "text": "LLAMA_API int32_t llama_n_threads();\n"
         }],
-        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n    int n_threads = llama_",
+        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
+        "prompt": "    int n_threads = llama_",
         "input_suffix": "}\n",
     })
     assert res.status_code == 200
-    assert match_regex("(cuts|Jimmy|mom|came|into|the|room)+", res.body["content"])
+    assert match_regex("(Dad|excited|park)+", res.body["content"])
 
 
 @pytest.mark.parametrize("input_extra", [
@@ -48,10 +48,30 @@ def test_invalid_input_extra_req(input_extra):
     global server
     server.start()
     res = server.make_request("POST", "/infill", data={
-        "prompt": "Complete this",
         "input_extra": [input_extra],
-        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n    int n_threads = llama_",
+        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
+        "prompt": "    int n_threads = llama_",
         "input_suffix": "}\n",
     })
     assert res.status_code == 400
     assert "error" in res.body
+
+
+@pytest.mark.skipif(not is_slow_test_allowed(), reason="skipping slow test")
+def test_with_qwen_model():
+    global server
+    server.model_file = None
+    server.model_hf_repo = "ggml-org/Qwen2.5-Coder-1.5B-IQ3_XXS-GGUF"
+    server.model_hf_file = "qwen2.5-coder-1.5b-iq3_xxs-imat.gguf"
+    server.start(timeout_seconds=600)
+    res = server.make_request("POST", "/infill", data={
+        "input_extra": [{
+            "filename": "llama.h",
+            "text": "LLAMA_API int32_t llama_n_threads();\n"
+        }],
+        "input_prefix": "#include <cstdio>\n#include \"llama.h\"\n\nint main() {\n",
+        "prompt": "    int n_threads = llama_",
+        "input_suffix": "}\n",
+    })
+    assert res.status_code == 200
+    assert res.body["content"] == "n_threads();\n    printf(\"Number of threads: %d\\n\", n_threads);\n    return 0;\n"

examples/server/tests/unit/test_rerank.py

@@ -53,3 +53,26 @@ def test_invalid_rerank_req(documents):
     })
     assert res.status_code == 400
     assert "error" in res.body
+
+
+@pytest.mark.parametrize(
+    "query,doc1,doc2,n_tokens",
+    [
+        ("Machine learning is", "A machine", "Learning is", 19),
+        ("Which city?", "Machine learning is ", "Paris, capitale de la", 26),
+    ]
+)
+def test_rerank_usage(query, doc1, doc2, n_tokens):
+    global server
+    server.start()
+
+    res = server.make_request("POST", "/rerank", data={
+        "query": query,
+        "documents": [
+            doc1,
+            doc2,
+        ]
+    })
+    assert res.status_code == 200
+    assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
+    assert res.body['usage']['prompt_tokens'] == n_tokens

examples/server/tests/unit/test_speculative.py

@@ -82,6 +82,37 @@ def test_different_draft_min_draft_max():
         last_content = res.body["content"]
 
 
+def test_slot_ctx_not_exceeded():
+    global server
+    server.n_ctx = 64
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "Hello " * 56,
+        "temperature": 0.0,
+        "top_k": 1,
+        "speculative.p_min": 0.0,
+    })
+    assert res.status_code == 200
+    assert len(res.body["content"]) > 0
+
+
+def test_with_ctx_shift():
+    global server
+    server.n_ctx = 64
+    server.start()
+    res = server.make_request("POST", "/completion", data={
+        "prompt": "Hello " * 56,
+        "temperature": 0.0,
+        "top_k": 1,
+        "n_predict": 64,
+        "speculative.p_min": 0.0,
+    })
+    assert res.status_code == 200
+    assert len(res.body["content"]) > 0
+    assert res.body["tokens_predicted"] == 64
+    assert res.body["truncated"] == True
+
+
 @pytest.mark.parametrize("n_slots,n_requests", [
     (1, 2),
     (2, 2),

examples/server/tests/utils.py

@@ -64,6 +64,7 @@ class ServerProcess:
     server_embeddings: bool | None = False
     server_reranking: bool | None = False
     server_metrics: bool | None = False
+    server_slots: bool | None = False
     draft: int | None = None
     api_key: str | None = None
     response_format: str | None = None
@@ -71,6 +72,7 @@ class ServerProcess:
     disable_ctx_shift: int | None = False
     draft_min: int | None = None
     draft_max: int | None = None
+    no_webui: bool | None = None
 
     # session variables
     process: subprocess.Popen | None = None
@@ -91,7 +93,6 @@ class ServerProcess:
         else:
             server_path = "../../../build/bin/llama-server"
         server_args = [
-            "--slots",  # requires to get slot status via /slots endpoint
             "--host",
             self.server_host,
             "--port",
@@ -129,6 +130,8 @@ class ServerProcess:
             server_args.append("--reranking")
         if self.server_metrics:
             server_args.append("--metrics")
+        if self.server_slots:
+            server_args.append("--slots")
         if self.model_alias:
             server_args.extend(["--alias", self.model_alias])
         if self.n_ctx:
@@ -156,6 +159,8 @@ class ServerProcess:
             server_args.extend(["--draft-max", self.draft_max])
         if self.draft_min:
             server_args.extend(["--draft-min", self.draft_min])
+        if self.no_webui:
+            server_args.append("--no-webui")
 
         args = [str(arg) for arg in [server_path, *server_args]]
         print(f"bench: starting server with: {' '.join(args)}")
@@ -181,7 +186,7 @@ class ServerProcess:
         start_time = time.time()
         while time.time() - start_time < timeout_seconds:
             try:
-                response = self.make_request("GET", "/slots", headers={
+                response = self.make_request("GET", "/health", headers={
                     "Authorization": f"Bearer {self.api_key}" if self.api_key else None
                 })
                 if response.status_code == 200:
@@ -224,7 +229,7 @@ class ServerProcess:
         result.headers = dict(response.headers)
         result.status_code = response.status_code
         result.body = response.json() if parse_body else None
-        print("Response from server", result.body)
+        print("Response from server", json.dumps(result.body, indent=2))
         return result
 
     def make_stream_request(
@@ -245,7 +250,7 @@ class ServerProcess:
                 break
             elif line.startswith('data: '):
                 data = json.loads(line[6:])
-                print("Partial response from server", data)
+                print("Partial response from server", json.dumps(data, indent=2))
                 yield data
 
 
@@ -369,3 +374,6 @@ def match_regex(regex: str, text: str) -> bool:
         ).search(text)
         is not None
     )
+
+def is_slow_test_allowed():
+    return os.environ.get("SLOW_TESTS") == "1" or os.environ.get("SLOW_TESTS") == "ON"

examples/server/themes/buttons-top/index.html

@@ -222,7 +222,6 @@
       temperature: 0.7,
       repeat_last_n: 256, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.18, // 1.0 = disabled
-      penalize_nl: false,
       top_k: 40, // <= 0 to use vocab size
       top_p: 0.95, // 1.0 = disabled
       min_p: 0.05, // 0 = disabled
@@ -779,7 +778,6 @@
             ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
             ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
             ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
-            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
             ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
             ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
             ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}

examples/server/themes/wild/index.html

@@ -225,7 +225,6 @@
       temperature: 0.7,
       repeat_last_n: 256, // 0 = disable penalty, -1 = context size
       repeat_penalty: 1.18, // 1.0 = disabled
-      penalize_nl: false,
       top_k: 40, // <= 0 to use vocab size
       top_p: 0.95, // 1.0 = disabled
       min_p: 0.05, // 0 = disabled
@@ -782,7 +781,6 @@
             ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
             ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
             ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
-            ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })}
             ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
             ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
             ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })}

examples/server/utils.hpp

@@ -20,8 +20,9 @@
 #include <sstream>
 #include <string>
 #include <vector>
+#include <memory>
 
-#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
+#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo"
 
 using json = nlohmann::ordered_json;
 
@@ -40,17 +41,6 @@ using json = nlohmann::ordered_json;
 #define QUE_ERR(fmt, ...) LOG_ERR("que  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
 #define QUE_DBG(fmt, ...) LOG_DBG("que  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
 
-// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11
-enum error_type {
-    ERROR_TYPE_INVALID_REQUEST,
-    ERROR_TYPE_AUTHENTICATION,
-    ERROR_TYPE_SERVER,
-    ERROR_TYPE_NOT_FOUND,
-    ERROR_TYPE_PERMISSION,
-    ERROR_TYPE_UNAVAILABLE, // custom error
-    ERROR_TYPE_NOT_SUPPORTED, // custom error
-};
-
 template <typename T>
 static T json_value(const json & body, const std::string & key, const T & default_value) {
     // Fallback null to default value
@@ -174,6 +164,9 @@ static std::vector<llama_tokens> tokenize_input_prompts(llama_context * ctx, con
     } else {
         throw std::runtime_error("\"prompt\" must be a string, an list of tokens, a list of mixed strings & tokens, or a list of prompts");
     }
+    if (result.empty()) {
+        throw std::runtime_error("\"prompt\" must not be empty");
+    }
     return result;
 }
 
@@ -337,12 +330,12 @@ static std::string llama_get_chat_template(const struct llama_model * model) {
     std::string template_key = "tokenizer.chat_template";
     // call with NULL buffer to get the total size of the string
     int32_t res = llama_model_meta_val_str(model, template_key.c_str(), NULL, 0);
-    if (res < 0) {
+    if (res < 2) {
         return "";
     } else {
-        std::vector<char> model_template(res, 0);
+        std::vector<char> model_template(res + 1, 0);
         llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
-        return std::string(model_template.data(), model_template.size());
+        return std::string(model_template.data(), model_template.size() - 1);
     }
 }
 
@@ -485,48 +478,11 @@ static std::string tokens_to_output_formatted_string(const llama_context * ctx,
     return out;
 }
 
-struct completion_token_output {
-    llama_token tok;
-    std::string text_to_send;
-
-    struct token_prob {
-        llama_token tok;
-        float prob;
-    };
-
-    std::vector<token_prob> probs;
-};
-
-// convert a vector of completion_token_output to json
-static json probs_vector_to_json(const llama_context * ctx, const std::vector<completion_token_output> & probs) {
-    json out = json::array();
-
-    for (const auto & prob : probs) {
-        json probs_for_token = json::array();
-
-        for (const auto & p : prob.probs) {
-            const std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
-            probs_for_token.push_back(json {
-                {"tok_str", tok_str},
-                {"prob",    p.prob},
-            });
-        }
-
-        const std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
-        out.push_back(json {
-            {"content", tok_str},
-            {"probs",   probs_for_token},
-        });
-    }
-
-    return out;
-}
-
 static bool server_sent_event(httplib::DataSink & sink, const char * event, const json & data) {
     const std::string str =
         std::string(event) + ": " +
         data.dump(-1, ' ', false, json::error_handler_t::replace) +
-        "\n\n"; // note: these newlines are important (not sure why though, if you know, add a comment to explain)
+        "\n\n"; // required by RFC 8895 - A message is terminated by a blank line (two line terminators in a row).
 
     LOG_DBG("data stream, to_send: %s", str.c_str());
 
@@ -543,8 +499,6 @@ static json oaicompat_completion_params_parse(
     const std::string & chat_template) {
     json llama_params;
 
-    llama_params["__oaicompat"] = true;
-
     // Apply chat template to the list of messages
     llama_params["prompt"] = format_chat(model, chat_template, body.at("messages"));
 
@@ -604,166 +558,9 @@ static json oaicompat_completion_params_parse(
     return llama_params;
 }
 
-static json format_final_response_oaicompat(const json & request, const json & result, const std::string & completion_id, bool streaming = false, bool verbose = false) {
-    bool stopped_word        = result.count("stopped_word") != 0;
-    bool stopped_eos         = json_value(result, "stopped_eos", false);
-    int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
-    int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
-    std::string content      = json_value(result, "content", std::string(""));
-
-    std::string finish_reason = "length";
-    if (stopped_word || stopped_eos) {
-        finish_reason = "stop";
-    }
-
-    json choices =
-        streaming ? json::array({json{{"finish_reason", finish_reason},
-                                        {"index", 0},
-                                        {"delta", json::object()}}})
-                  : json::array({json{{"finish_reason", finish_reason},
-                                        {"index", 0},
-                                        {"message", json{{"content", content},
-                                                         {"role", "assistant"}}}}});
-
-    std::time_t t = std::time(0);
-
-    json res = json {
-        {"choices", choices},
-        {"created", t},
-        {"model",
-            json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
-        {"object", streaming ? "chat.completion.chunk" : "chat.completion"},
-        {"usage", json {
-            {"completion_tokens", num_tokens_predicted},
-            {"prompt_tokens",     num_prompt_tokens},
-            {"total_tokens",      num_tokens_predicted + num_prompt_tokens}
-        }},
-        {"id", completion_id}
-    };
-
-    // extra fields for debugging purposes
-    if (verbose) {
-        res["__verbose"] = result;
-    }
-
-    if (result.contains("completion_probabilities")) {
-        res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
-    }
-
-    if (result.contains("timings")) {
-        res.push_back({"timings", json_value(result, "timings", json::object())});
-    }
-
-    return res;
-}
-
-// return value is vector as there is one case where we might need to generate two responses
-static std::vector<json> format_partial_response_oaicompat(const json & result, const std::string & completion_id) {
-    if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
-        return std::vector<json>({result});
-    }
-
-    bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
-    std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
-
-    bool stopped_word   = json_value(result, "stopped_word",  false);
-    bool stopped_eos    = json_value(result, "stopped_eos",   false);
-    bool stopped_limit  = json_value(result, "stopped_limit", false);
-    std::string content = json_value(result, "content",       std::string(""));
-
-    std::string finish_reason;
-    if (stopped_word || stopped_eos) {
-        finish_reason = "stop";
-    }
-    if (stopped_limit) {
-        finish_reason = "length";
-    }
-
-    std::time_t t = std::time(0);
-
-    json choices;
-
-    if (!finish_reason.empty()) {
-        choices = json::array({json{{"finish_reason", finish_reason},
-                                    {"index", 0},
-                                    {"delta", json::object()}}});
-    } else {
-        if (first) {
-            if (content.empty()) {
-                choices = json::array({json{{"finish_reason", nullptr},
-                                            {"index", 0},
-                                            {"delta", json{{"role", "assistant"}}}}});
-            } else {
-                // We have to send this as two updates to conform to openai behavior
-                json initial_ret = json{{"choices", json::array({json{
-                                        {"finish_reason", nullptr},
-                                        {"index", 0},
-                                        {"delta", json{
-                                            {"role", "assistant"}
-                                        }}}})},
-                            {"created", t},
-                            {"id", completion_id},
-                            {"model", modelname},
-                            {"object", "chat.completion.chunk"}};
-
-                json second_ret = json{
-                            {"choices", json::array({json{{"finish_reason", nullptr},
-                                                            {"index", 0},
-                                                            {"delta", json{
-                                                            {"content", content}}}
-                                                            }})},
-                            {"created", t},
-                            {"id", completion_id},
-                            {"model", modelname},
-                            {"object", "chat.completion.chunk"}};
-
-                return std::vector<json>({initial_ret, second_ret});
-            }
-        } else {
-            // Some idiosyncrasy in task processing logic makes several trailing calls
-            // with empty content, we ignore these at the calee site.
-            if (content.empty()) {
-                return std::vector<json>({json::object()});
-            }
-
-            choices = json::array({json{
-                {"finish_reason", nullptr},
-                {"index", 0},
-                {"delta",
-                json{
-                    {"content", content},
-                }},
-            }});
-        }
-    }
-
-    json ret = json {
-        {"choices", choices},
-        {"created", t},
-        {"id",      completion_id},
-        {"model",   modelname},
-        {"object",  "chat.completion.chunk"}
-    };
-
-    if (result.contains("timings")) {
-        ret.push_back({"timings", json_value(result, "timings", json::object())});
-    }
-
-    if (!finish_reason.empty()) {
-        int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
-        int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
-        ret.push_back({"usage", json {
-            {"completion_tokens", num_tokens_predicted},
-            {"prompt_tokens",     num_prompt_tokens},
-            {"total_tokens",      num_tokens_predicted + num_prompt_tokens}
-        }});
-    }
-
-    return std::vector<json>({ret});
-}
-
 static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
     json data = json::array();
+    int32_t n_tokens = 0;
     int i = 0;
     for (const auto & elem : embeddings) {
         data.push_back(json{
@@ -771,14 +568,16 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
             {"index",     i++},
             {"object",    "embedding"}
         });
+
+        n_tokens += json_value(elem, "tokens_evaluated", 0);
     }
 
     json res = json {
         {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
         {"object", "list"},
-        {"usage", json { // TODO: fill
-            {"prompt_tokens", 0},
-            {"total_tokens", 0}
+        {"usage", json {
+            {"prompt_tokens", n_tokens},
+            {"total_tokens", n_tokens}
         }},
         {"data", data}
     };
@@ -788,20 +587,23 @@ static json format_embeddings_response_oaicompat(const json & request, const jso
 
 static json format_response_rerank(const json & request, const json & ranks) {
     json data = json::array();
+    int32_t n_tokens = 0;
     int i = 0;
     for (const auto & rank : ranks) {
         data.push_back(json{
             {"index",    i++},
             {"relevance_score", json_value(rank, "score", 0.0)},
         });
+
+        n_tokens += json_value(rank, "tokens_evaluated", 0);
     }
 
     json res = json {
         {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
         {"object", "list"},
-        {"usage", json { // TODO: fill
-            {"prompt_tokens", 0},
-            {"total_tokens", 0}
+        {"usage", json {
+            {"prompt_tokens", n_tokens},
+            {"total_tokens", n_tokens}
         }},
         {"results", data}
     };
@@ -854,42 +656,17 @@ static json format_detokenized_response(const std::string & content) {
     };
 }
 
-static json format_error_response(const std::string & message, const enum error_type type) {
-    std::string type_str;
-    int code = 500;
-    switch (type) {
-        case ERROR_TYPE_INVALID_REQUEST:
-            type_str = "invalid_request_error";
-            code = 400;
-            break;
-        case ERROR_TYPE_AUTHENTICATION:
-            type_str = "authentication_error";
-            code = 401;
-            break;
-        case ERROR_TYPE_NOT_FOUND:
-            type_str = "not_found_error";
-            code = 404;
-            break;
-        case ERROR_TYPE_SERVER:
-            type_str = "server_error";
-            code = 500;
-            break;
-        case ERROR_TYPE_PERMISSION:
-            type_str = "permission_error";
-            code = 403;
-            break;
-        case ERROR_TYPE_NOT_SUPPORTED:
-            type_str = "not_supported_error";
-            code = 501;
-            break;
-        case ERROR_TYPE_UNAVAILABLE:
-            type_str = "unavailable_error";
-            code = 503;
-            break;
+static json format_logit_bias(const std::vector<llama_logit_bias> & logit_bias) {
+    json data = json::array();
+    for (const auto & lb : logit_bias) {
+        data.push_back(json{
+            {"bias", lb.bias},
+            {"token", lb.token},
+        });
     }
-    return json {
-        {"code", code},
-        {"message", message},
-        {"type", type_str},
-    };
+    return data;
+}
+
+static std::string safe_json_to_str(json data) {
+    return data.dump(-1, ' ', false, json::error_handler_t::replace);
 }

examples/server/webui/index.html

@@ -0,0 +1,310 @@
+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
+  <meta name="color-scheme" content="light dark">
+  <title>🦙 llama.cpp - chat</title>
+</head>
+
+<body>
+  <div id="app" class="opacity-0"> <!-- opacity-0 will be removed on app mounted -->
+    <div class="flex flex-row drawer lg:drawer-open">
+      <input id="toggle-drawer" type="checkbox" class="drawer-toggle" checked />
+
+      <!-- sidebar -->
+      <div class="drawer-side h-screen lg:h-screen z-50 lg:max-w-64">
+        <label for="toggle-drawer" aria-label="close sidebar" class="drawer-overlay"></label>
+        <div class="flex flex-col bg-base-200 min-h-full max-w-64 py-4 px-4">
+          <div class="flex flex-row items-center justify-between mb-4 mt-4">
+            <h2 class="font-bold ml-4">Conversations</h2>
+
+            <!-- close sidebar button -->
+            <label for="toggle-drawer" class="btn btn-ghost lg:hidden">
+              <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-arrow-bar-left" viewBox="0 0 16 16">
+                <path fill-rule="evenodd" d="M12.5 15a.5.5 0 0 1-.5-.5v-13a.5.5 0 0 1 1 0v13a.5.5 0 0 1-.5.5M10 8a.5.5 0 0 1-.5.5H3.707l2.147 2.146a.5.5 0 0 1-.708.708l-3-3a.5.5 0 0 1 0-.708l3-3a.5.5 0 1 1 .708.708L3.707 7.5H9.5a.5.5 0 0 1 .5.5"/>
+              </svg>
+            </label>
+          </div>
+
+          <!-- list of conversations -->
+          <div :class="{
+            'btn btn-ghost justify-start': true,
+            'btn-active': messages.length === 0,
+          }" @click="newConversation">
+            + New conversation
+          </div>
+          <div v-for="conv in conversations" :class="{
+            'btn btn-ghost justify-start font-normal': true,
+            'btn-active': conv.id === viewingConvId,
+          }" @click="setViewingConv(conv.id)">
+            <span class="truncate">{{ conv.messages[0].content }}</span>
+          </div>
+          <div class="text-center text-xs opacity-40 mt-auto mx-4">
+            Conversations are saved to browser's localStorage
+          </div>
+        </div>
+      </div>
+
+      <!-- main view -->
+      <div class="chat-screen drawer-content grow flex flex-col h-screen w-screen mx-auto px-4">
+        <!-- header -->
+        <div class="flex flex-row items-center mt-6 mb-6">
+          <!-- open sidebar button -->
+          <label for="toggle-drawer" class="btn btn-ghost lg:hidden">
+            <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-list" viewBox="0 0 16 16">
+              <path fill-rule="evenodd" d="M2.5 12a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5m0-4a.5.5 0 0 1 .5-.5h10a.5.5 0 0 1 0 1H3a.5.5 0 0 1-.5-.5"/>
+            </svg>
+          </label>
+
+          <div class="grow text-2xl font-bold ml-2">llama.cpp</div>
+
+          <!-- action buttons (top right) -->
+          <div class="flex items-center">
+            <div v-if="messages.length > 0" class="dropdown dropdown-end">
+              <!-- "more" button -->
+              <button tabindex="0" role="button" class="btn m-1" :disabled="isGenerating">
+                <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-three-dots-vertical" viewBox="0 0 16 16">
+                  <path d="M9.5 13a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0m0-5a1.5 1.5 0 1 1-3 0 1.5 1.5 0 0 1 3 0"/>
+                </svg>
+              </button>
+              <!-- "more" dropdown menu -->
+              <ul tabindex="0" class="dropdown-content menu bg-base-100 rounded-box z-[1] w-52 p-2 shadow">
+                <li @click="downloadConv(viewingConvId)"><a>Download</a></li>
+                <li class="text-error" @click="deleteConv(viewingConvId)"><a>Delete</a></li>
+              </ul>
+            </div>
+            <button class="btn" @click="showConfigDialog = true" :disabled="isGenerating">
+              <!-- settings button -->
+              <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-gear" viewBox="0 0 16 16">
+                <path d="M8 4.754a3.246 3.246 0 1 0 0 6.492 3.246 3.246 0 0 0 0-6.492M5.754 8a2.246 2.246 0 1 1 4.492 0 2.246 2.246 0 0 1-4.492 0"/>
+                <path d="M9.796 1.343c-.527-1.79-3.065-1.79-3.592 0l-.094.319a.873.873 0 0 1-1.255.52l-.292-.16c-1.64-.892-3.433.902-2.54 2.541l.159.292a.873.873 0 0 1-.52 1.255l-.319.094c-1.79.527-1.79 3.065 0 3.592l.319.094a.873.873 0 0 1 .52 1.255l-.16.292c-.892 1.64.901 3.434 2.541 2.54l.292-.159a.873.873 0 0 1 1.255.52l.094.319c.527 1.79 3.065 1.79 3.592 0l.094-.319a.873.873 0 0 1 1.255-.52l.292.16c1.64.893 3.434-.902 2.54-2.541l-.159-.292a.873.873 0 0 1 .52-1.255l.319-.094c1.79-.527 1.79-3.065 0-3.592l-.319-.094a.873.873 0 0 1-.52-1.255l.16-.292c.893-1.64-.902-3.433-2.541-2.54l-.292.159a.873.873 0 0 1-1.255-.52zm-2.633.283c.246-.835 1.428-.835 1.674 0l.094.319a1.873 1.873 0 0 0 2.693 1.115l.291-.16c.764-.415 1.6.42 1.184 1.185l-.159.292a1.873 1.873 0 0 0 1.116 2.692l.318.094c.835.246.835 1.428 0 1.674l-.319.094a1.873 1.873 0 0 0-1.115 2.693l.16.291c.415.764-.42 1.6-1.185 1.184l-.291-.159a1.873 1.873 0 0 0-2.693 1.116l-.094.318c-.246.835-1.428.835-1.674 0l-.094-.319a1.873 1.873 0 0 0-2.692-1.115l-.292.16c-.764.415-1.6-.42-1.184-1.185l.159-.291A1.873 1.873 0 0 0 1.945 8.93l-.319-.094c-.835-.246-.835-1.428 0-1.674l.319-.094A1.873 1.873 0 0 0 3.06 4.377l-.16-.292c-.415-.764.42-1.6 1.185-1.184l.292.159a1.873 1.873 0 0 0 2.692-1.115z"/>
+              </svg>
+            </button>
+
+            <!-- theme controller is copied from https://daisyui.com/components/theme-controller/ -->
+            <div class="dropdown dropdown-end dropdown-bottom">
+              <div tabindex="0" role="button" class="btn m-1">
+                <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" fill="currentColor" class="bi bi-palette2" viewBox="0 0 16 16">
+                  <path d="M0 .5A.5.5 0 0 1 .5 0h5a.5.5 0 0 1 .5.5v5.277l4.147-4.131a.5.5 0 0 1 .707 0l3.535 3.536a.5.5 0 0 1 0 .708L10.261 10H15.5a.5.5 0 0 1 .5.5v5a.5.5 0 0 1-.5.5H3a3 3 0 0 1-2.121-.879A3 3 0 0 1 0 13.044m6-.21 7.328-7.3-2.829-2.828L6 7.188zM4.5 13a1.5 1.5 0 1 0-3 0 1.5 1.5 0 0 0 3 0M15 15v-4H9.258l-4.015 4zM0 .5v12.495zm0 12.495V13z"/>
+                </svg>
+              </div>
+              <ul tabindex="0" class="dropdown-content bg-base-300 rounded-box z-[1] w-52 p-2 shadow-2xl h-80 overflow-y-auto">
+                <li>
+                  <button
+                    class="btn btn-sm btn-block btn-ghost justify-start"
+                    :class="{ 'btn-active': selectedTheme === 'auto' }"
+                    @click="setSelectedTheme('auto')">
+                    auto
+                  </button>
+                </li>
+                <li v-for="theme in themes">
+                  <input
+                    type="radio"
+                    name="theme-dropdown"
+                    class="theme-controller btn btn-sm btn-block btn-ghost justify-start"
+                    :aria-label="theme"
+                    :value="theme"
+                    :checked="selectedTheme === theme"
+                    @click="setSelectedTheme(theme)" />
+                </li>
+              </ul>
+            </div>
+          </div>
+        </div>
+
+        <!-- chat messages -->
+        <div id="messages-list" class="flex flex-col grow overflow-y-auto">
+          <div class="mt-auto flex justify-center">
+            <!-- placeholder to shift the message to the bottom -->
+            {{ messages.length === 0 ? 'Send a message to start' : '' }}
+          </div>
+          <div v-for="msg in messages" class="group">
+            <message-bubble
+              :config="config"
+              :msg="msg"
+              :key="msg.id"
+              :is-generating="isGenerating"
+              :edit-user-msg-and-regenerate="editUserMsgAndRegenerate"
+              :regenerate-msg="regenerateMsg"></message-bubble>
+          </div>
+
+          <!-- pending (ongoing) assistant message -->
+          <div id="pending-msg" class="group">
+            <message-bubble
+              v-if="pendingMsg"
+              :config="config"
+              :msg="pendingMsg"
+              :key="pendingMsg.id"
+              :is-generating="isGenerating"
+              :edit-user-msg-and-regenerate="() => {}"
+              :regenerate-msg="() => {}"></message-bubble>
+          </div>
+        </div>
+
+        <!-- chat input -->
+        <div class="flex flex-row items-center mt-8 mb-6">
+          <textarea
+            class="textarea textarea-bordered w-full"
+            placeholder="Type a message (Shift+Enter to add a new line)"
+            v-model="inputMsg"
+            @keydown.enter.exact.prevent="sendMessage"
+            @keydown.enter.shift.exact.prevent="inputMsg += '\n'"
+            :disabled="isGenerating"
+            id="msg-input"
+          ></textarea>
+          <button v-if="!isGenerating" class="btn btn-primary ml-2" @click="sendMessage" :disabled="inputMsg.length === 0">Send</button>
+          <button v-else class="btn btn-neutral ml-2" @click="stopGeneration">Stop</button>
+        </div>
+      </div>
+
+    </div>
+
+
+    <!-- modal for editing config -->
+    <dialog class="modal" :class="{'modal-open': showConfigDialog}">
+      <div class="modal-box">
+        <h3 class="text-lg font-bold mb-6">Settings</h3>
+        <div class="h-[calc(90vh-12rem)] overflow-y-auto">
+          <p class="opacity-40 mb-6">Settings below are saved in browser's localStorage</p>
+          <settings-modal-short-input :config-key="'apiKey'" :config-default="configDefault" :config-info="configInfo" v-model="config.apiKey"></settings-modal-short-input>
+          <label class="form-control mb-2">
+            <div class="label">System Message</div>
+            <textarea class="textarea textarea-bordered h-24" :placeholder="'Default: ' + configDefault.systemMessage" v-model="config.systemMessage"></textarea>
+          </label>
+          <template v-for="configKey in ['temperature', 'top_k', 'top_p', 'min_p', 'max_tokens']">
+            <settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]"></settings-modal-short-input>
+          </template>
+          <!-- TODO: add more sampling-related configs, please regroup them into different "collapse" sections -->
+          <!-- Section: Other sampler settings -->
+          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
+            <summary class="collapse-title font-bold">Other sampler settings</summary>
+            <div class="collapse-content">
+              <!-- Samplers queue -->
+              <settings-modal-short-input label="Samplers queue" :config-key="'samplers'" :config-default="configDefault" :config-info="configInfo" v-model="config.samplers"></settings-modal-short-input>
+              <!-- Samplers -->
+              <template v-for="configKey in ['dynatemp_range', 'dynatemp_exponent', 'typical_p', 'xtc_probability', 'xtc_threshold']">
+                <settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]"></settings-modal-short-input>
+              </template>
+            </div>
+          </details>
+          <!-- Section: Penalties settings -->
+          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
+            <summary class="collapse-title font-bold">Penalties settings</summary>
+            <div class="collapse-content">
+              <template v-for="configKey in ['repeat_last_n', 'repeat_penalty', 'presence_penalty', 'frequency_penalty', 'dry_multiplier', 'dry_base', 'dry_allowed_length', 'dry_penalty_last_n']">
+                <settings-modal-short-input :config-key="configKey" :config-default="configDefault" :config-info="configInfo" v-model="config[configKey]"></settings-modal-short-input>
+              </template>
+            </div>
+          </details>
+          <!-- Section: Advanced config -->
+          <details class="collapse collapse-arrow bg-base-200 mb-2 overflow-visible">
+            <summary class="collapse-title font-bold">Advanced config</summary>
+            <div class="collapse-content">
+              <div class="flex flex-row items-center mb-2" v-if="isDev">
+                <!-- this button only shows in dev mode, used to import a demo conversation to test message rendering -->
+                <button class="btn" @click="debugImportDemoConv()">(debug) Import demo conversation</button>
+              </div>
+              <div class="flex flex-row items-center mb-2">
+                <input type="checkbox" class="checkbox" v-model="config.showTokensPerSecond" />
+                <span class="ml-4">Show tokens per second</span>
+              </div>
+              <label class="form-control mb-2">
+                <!-- Custom parameters input -->
+                <div class="label inline">Custom JSON config (For more info, refer to <a class="underline" href="https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md" target="_blank" rel="noopener noreferrer">server documentation</a>)</div>
+                <textarea class="textarea textarea-bordered h-24" placeholder="Example: { &quot;mirostat&quot;: 1, &quot;min_p&quot;: 0.1 }" v-model="config.custom"></textarea>
+              </label>
+            </div>
+          </details>
+        </div>
+
+        <!-- action buttons -->
+        <div class="modal-action">
+          <button class="btn" @click="resetConfigDialog">Reset to default</button>
+          <button class="btn" @click="closeAndDiscardConfigDialog">Close</button>
+          <button class="btn btn-primary" @click="closeAndSaveConfigDialog">Save</button>
+        </div>
+      </div>
+    </dialog>
+
+  </div>
+
+
+  <!-- Template to be used as message bubble -->
+  <template id="message-bubble">
+    <div :class="{
+      'chat': true,
+      'chat-start': msg.role !== 'user',
+      'chat-end': msg.role === 'user',
+    }">
+      <div :class="{
+        'chat-bubble markdown': true,
+        'chat-bubble-base-300': msg.role !== 'user',
+      }">
+        <!-- textarea for editing message -->
+        <template v-if="editingContent !== null">
+          <textarea
+            class="textarea textarea-bordered bg-base-100 text-base-content w-[calc(90vw-8em)] lg:w-96"
+            v-model="editingContent"></textarea>
+          <br/>
+          <button class="btn btn-ghost mt-2 mr-2" @click="editingContent = null">Cancel</button>
+          <button class="btn mt-2" @click="editMsg()">Submit</button>
+        </template>
+        <template v-else>
+          <!-- show loading dots for pending message -->
+          <span v-if="msg.content === null" class="loading loading-dots loading-md"></span>
+          <!-- render message as markdown -->
+          <vue-markdown v-else :source="msg.content"></vue-markdown>
+          <!-- render timings if enabled -->
+          <div class="dropdown dropdown-hover dropdown-top mt-2" v-if="timings && config.showTokensPerSecond">
+            <div tabindex="0" role="button" class="cursor-pointer font-semibold text-sm opacity-60">Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s</div>
+            <div class="dropdown-content bg-base-100 z-10 w-64 p-2 shadow mt-4">
+              <b>Prompt</b><br/>
+              - Tokens: {{ timings.prompt_n }}<br/>
+              - Time: {{ timings.prompt_ms }} ms<br/>
+              - Speed: {{ timings.prompt_per_second.toFixed(1) }} t/s<br/>
+              <b>Generation</b><br/>
+              - Tokens: {{ timings.predicted_n }}<br/>
+              - Time: {{ timings.predicted_ms }} ms<br/>
+              - Speed: {{ timings.predicted_per_second.toFixed(1) }} t/s<br/>
+            </div>
+          </div>
+        </template>
+      </div>
+    </div>
+    <!-- actions for each message -->
+    <div :class="{'text-right': msg.role === 'user', 'opacity-0': isGenerating}" class="mx-4 mt-2 mb-2">
+      <!-- user message -->
+      <button v-if="msg.role === 'user'" class="badge btn-mini show-on-hover" @click="editingContent = msg.content" :disabled="isGenerating">
+        ✍️ Edit
+      </button>
+      <!-- assistant message -->
+      <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="regenerateMsg(msg)" :disabled="isGenerating">
+        🔄 Regenerate
+      </button>
+      <button v-if="msg.role === 'assistant'" class="badge btn-mini show-on-hover mr-2" @click="copyMsg()" :disabled="isGenerating">
+        📋 Copy
+      </button>
+    </div>
+  </template>
+
+
+  <!-- Template to be used by settings modal -->
+  <template id="settings-modal-short-input">
+    <label class="input input-bordered join-item grow flex items-center gap-2 mb-2">
+      <!-- Show help message on hovering on the input label -->
+      <div class="dropdown dropdown-hover">
+        <div tabindex="0" role="button" class="font-bold">{{ label || configKey }}</div>
+        <div class="dropdown-content menu bg-base-100 rounded-box z-10 w-64 p-2 shadow mt-4">
+          {{ configInfo[configKey] || '(no help message available)' }}
+        </div>
+      </div>
+      <!-- Here we forward v-model from parent to child component, see: https://stackoverflow.com/questions/47311936/v-model-and-child-components -->
+      <input type="text" class="grow" :placeholder="'Default: ' + (configDefault[configKey] || 'none')" :value="modelValue" @input="$emit('update:modelValue', $event.target.value)" />
+    </label>
+  </template>
+
+  <script type="module" src="/src/main.js"></script>
+</body>
+
+</html>

examples/server/webui/package.json

@@ -0,0 +1,30 @@
+{
+  "name": "webui",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "preview": "vite preview",
+    "analyze": "ANALYZE=1 npx vite-bundle-visualizer"
+  },
+  "devDependencies": {
+    "sass-embedded": "^1.83.0",
+    "vite": "^5.4.10"
+  },
+  "dependencies": {
+    "@sec-ant/readable-stream": "^0.6.0",
+    "@vscode/markdown-it-katex": "^1.1.1",
+    "autoprefixer": "^10.4.20",
+    "daisyui": "^4.12.14",
+    "highlight.js": "^11.10.0",
+    "katex": "^0.16.15",
+    "markdown-it": "^14.1.0",
+    "postcss": "^8.4.49",
+    "tailwindcss": "^3.4.15",
+    "textlinestream": "^1.1.1",
+    "vite-plugin-singlefile": "^2.0.3",
+    "vue": "^3.5.13"
+  }
+}

examples/server/webui/postcss.config.js

@@ -0,0 +1,6 @@
+export default {
+  plugins: {
+    tailwindcss: {},
+    autoprefixer: {},
+  },
+}

examples/server/webui/public/demo-conversation.json

@@ -0,0 +1,33 @@
+{
+  "demo": true,
+  "id": "conv-1734086746930",
+  "lastModified": 1734087548943,
+  "messages": [
+    {
+      "id": 1734086764521,
+      "role": "user",
+      "content": "this is a demo conversation, used in dev mode"
+    },
+    {
+      "id": 1734087548327,
+      "role": "assistant",
+      "content": "This is the formula:\n\n$\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}$\n\nGiven an input vector \\(\\mathbf{x} = [x_1, x_2, \\ldots, x_n]\\)\n\n\\[\ny_i = \\frac{e^{x_i}}{\\sum_{j=1}^n e^{x_j}}\n\\]\n\nCode block latex:\n```latex\n\\frac{e^{x_i}}{\\sum_{j=1}^{n}e^{x_j}}\n```\n\nTest dollar sign: $1234 $4567\n\nInvalid latex syntax: $E = mc^$ and $$E = mc^$$",
+      "timings": {
+        "prompt_n": 1,
+        "prompt_ms": 28.923,
+        "predicted_n": 25,
+        "predicted_ms": 573.016
+      }
+    },
+    {
+      "id": 1734087548328,
+      "role": "user",
+      "content": "this is a demo conversation, used in dev mode"
+    },
+    {
+      "id": 1734087548329,
+      "role": "assistant",
+      "content": "Code block:\n```js\nconsole.log('hello world')\n```\n```sh\nls -la /dev\n```"
+    }
+  ]
+}

examples/server/webui/src/highlight-config.js

@@ -0,0 +1,60 @@
+import hljs from 'highlight.js/lib/core';
+
+// only import commonly used languages to reduce bundle size
+
+import python from 'highlight.js/lib/languages/python';
+import javascript from 'highlight.js/lib/languages/javascript';
+import json from 'highlight.js/lib/languages/json';
+import bash from 'highlight.js/lib/languages/bash';
+import yaml from 'highlight.js/lib/languages/yaml';
+import markdown from 'highlight.js/lib/languages/markdown';
+import scss from 'highlight.js/lib/languages/scss';
+import xml from 'highlight.js/lib/languages/xml';
+import ruby from 'highlight.js/lib/languages/ruby';
+import go from 'highlight.js/lib/languages/go';
+import java from 'highlight.js/lib/languages/java';
+import rust from 'highlight.js/lib/languages/rust';
+import scala from 'highlight.js/lib/languages/scala';
+import cpp from 'highlight.js/lib/languages/cpp';
+import csharp from 'highlight.js/lib/languages/csharp';
+import swift from 'highlight.js/lib/languages/swift';
+import dart from 'highlight.js/lib/languages/dart';
+import elixir from 'highlight.js/lib/languages/elixir';
+import kotlin from 'highlight.js/lib/languages/kotlin';
+import lua from 'highlight.js/lib/languages/lua';
+import php from 'highlight.js/lib/languages/php';
+import latex from 'highlight.js/lib/languages/latex';
+
+hljs.registerLanguage('python', python);
+hljs.registerLanguage('javascript', javascript);
+hljs.registerLanguage('json', json);
+hljs.registerLanguage('yaml', yaml);
+hljs.registerLanguage('markdown', markdown);
+hljs.registerLanguage('xml', xml);
+hljs.registerLanguage('ruby', ruby);
+hljs.registerLanguage('go', go);
+hljs.registerLanguage('java', java);
+hljs.registerLanguage('rust', rust);
+hljs.registerLanguage('scala', scala);
+hljs.registerLanguage('csharp', csharp);
+hljs.registerLanguage('swift', swift);
+hljs.registerLanguage('dart', dart);
+hljs.registerLanguage('elixir', elixir);
+hljs.registerLanguage('kotlin', kotlin);
+hljs.registerLanguage('lua', lua);
+hljs.registerLanguage('php', php);
+hljs.registerLanguage('latex', latex);
+
+// reuse some languages to further reduce bundle size
+
+hljs.registerLanguage('shell', bash);
+hljs.registerLanguage('bash', bash);
+hljs.registerLanguage('sh', bash);
+
+hljs.registerLanguage('css', scss);
+hljs.registerLanguage('scss', scss);
+
+hljs.registerLanguage('c', cpp);
+hljs.registerLanguage('cpp', cpp);
+
+export default hljs;

examples/server/webui/src/katex-gpt.js

@@ -0,0 +1,66 @@
+import katex from 'katex';
+
+// Adapted from https://github.com/SchneeHertz/markdown-it-katex-gpt
+// MIT license
+
+const defaultOptions = {
+  delimiters: [
+    { left: '\\[', right: '\\]', display: true },
+    { left: '\\(', right: '\\)', display: false },
+  ],
+};
+
+export function renderLatexHTML(content, display = false) {
+  return katex.renderToString(content, {
+    throwOnError: false,
+    output: 'mathml',
+    displayMode: display,
+  });
+}
+
+function escapedBracketRule(options) {
+  return (state, silent) => {
+    const max = state.posMax;
+    const start = state.pos;
+
+    for (const { left, right, display } of options.delimiters) {
+
+      // Check if it starts with the left delimiter
+      if (!state.src.slice(start).startsWith(left)) continue;
+
+      // Skip the length of the left delimiter
+      let pos = start + left.length;
+
+      // Find the matching right delimiter
+      while (pos < max) {
+        if (state.src.slice(pos).startsWith(right)) {
+          break;
+        }
+        pos++;
+      }
+
+      // No matching right delimiter found, skip to the next match
+      if (pos >= max) continue;
+
+      // If not in silent mode, convert LaTeX formula to MathML
+      if (!silent) {
+        const content = state.src.slice(start + left.length, pos);
+        try {
+          const renderedContent = renderLatexHTML(content, display);
+          const token = state.push('html_inline', '', 0);
+          token.content = renderedContent;
+        } catch (e) {
+          console.error(e);
+        }
+      }
+
+      // Update position, skip the length of the right delimiter
+      state.pos = pos + right.length;
+      return true;
+    }
+  }
+}
+
+export default function (md, options = defaultOptions) {
+  md.inline.ruler.after('text', 'escaped_bracket', escapedBracketRule(options));
+}

examples/server/webui/src/main.js

@@ -0,0 +1,585 @@
+import './styles.scss';
+import { createApp, defineComponent, shallowRef, computed, h } from 'vue/dist/vue.esm-bundler.js';
+import MarkdownIt from 'markdown-it';
+import TextLineStream from 'textlinestream';
+
+// math formula rendering
+import 'katex/dist/katex.min.css';
+import markdownItKatexGpt from './katex-gpt';
+import markdownItKatexNormal from '@vscode/markdown-it-katex';
+
+// code highlighting
+import hljs from './highlight-config';
+import daisyuiThemes from 'daisyui/src/theming/themes';
+
+// ponyfill for missing ReadableStream asyncIterator on Safari
+import { asyncIterator } from "@sec-ant/readable-stream/ponyfill/asyncIterator";
+
+const isDev = import.meta.env.MODE === 'development';
+
+// utility functions
+const isString = (x) => !!x.toLowerCase;
+const isBoolean = (x) => x === true || x === false;
+const isNumeric = (n) => !isString(n) && !isNaN(n) && !isBoolean(n);
+const escapeAttr = (str) => str.replace(/>/g, '>').replace(/"/g, '"');
+const copyStr = (str) => navigator.clipboard.writeText(str);
+
+// constants
+const BASE_URL = isDev
+  ? (localStorage.getItem('base') || 'https://localhost:8080') // for debugging
+  : (new URL('.', document.baseURI).href).toString().replace(/\/$/, ''); // for production
+console.log({ BASE_URL });
+
+const CONFIG_DEFAULT = {
+  // Note: in order not to introduce breaking changes, please keep the same data type (number, string, etc) if you want to change the default value. Do not use null or undefined for default value.
+  apiKey: '',
+  systemMessage: 'You are a helpful assistant.',
+  showTokensPerSecond: false,
+  // make sure these default values are in sync with `common.h`
+  samplers: 'edkypmxt',
+  temperature: 0.8,
+  dynatemp_range: 0.0,
+  dynatemp_exponent: 1.0,
+  top_k: 40,
+  top_p: 0.95,
+  min_p: 0.05,
+  xtc_probability: 0.0,
+  xtc_threshold: 0.1,
+  typical_p: 1.0,
+  repeat_last_n: 64,
+  repeat_penalty: 1.0,
+  presence_penalty: 0.0,
+  frequency_penalty: 0.0,
+  dry_multiplier: 0.0,
+  dry_base: 1.75,
+  dry_allowed_length: 2,
+  dry_penalty_last_n: -1,
+  max_tokens: -1,
+  custom: '', // custom json-stringified object
+};
+const CONFIG_INFO = {
+  apiKey: 'Set the API Key if you are using --api-key option for the server.',
+  systemMessage: 'The starting message that defines how model should behave.',
+  samplers: 'The order at which samplers are applied, in simplified way. Default is "dkypmxt": dry->top_k->typ_p->top_p->min_p->xtc->temperature',
+  temperature: 'Controls the randomness of the generated text by affecting the probability distribution of the output tokens. Higher = more random, lower = more focused.',
+  dynatemp_range: 'Addon for the temperature sampler. The added value to the range of dynamic temperature, which adjusts probabilities by entropy of tokens.',
+  dynatemp_exponent: 'Addon for the temperature sampler. Smoothes out the probability redistribution based on the most probable token.',
+  top_k: 'Keeps only k top tokens.',
+  top_p: 'Limits tokens to those that together have a cumulative probability of at least p',
+  min_p: 'Limits tokens based on the minimum probability for a token to be considered, relative to the probability of the most likely token.',
+  xtc_probability: 'XTC sampler cuts out top tokens; this parameter controls the chance of cutting tokens at all. 0 disables XTC.',
+  xtc_threshold: 'XTC sampler cuts out top tokens; this parameter controls the token probability that is required to cut that token.',
+  typical_p: 'Sorts and limits tokens based on the difference between log-probability and entropy.',
+  repeat_last_n: 'Last n tokens to consider for penalizing repetition',
+  repeat_penalty: 'Controls the repetition of token sequences in the generated text',
+  presence_penalty: 'Limits tokens based on whether they appear in the output or not.',
+  frequency_penalty: 'Limits tokens based on how often they appear in the output.',
+  dry_multiplier: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling multiplier.',
+  dry_base: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the DRY sampling base value.',
+  dry_allowed_length: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets the allowed length for DRY sampling.',
+  dry_penalty_last_n: 'DRY sampling reduces repetition in generated text even across long contexts. This parameter sets DRY penalty for the last n tokens.',
+  max_tokens: 'The maximum number of token per output.',
+  custom: '', // custom json-stringified object
+};
+// config keys having numeric value (i.e. temperature, top_k, top_p, etc)
+const CONFIG_NUMERIC_KEYS = Object.entries(CONFIG_DEFAULT).filter(e => isNumeric(e[1])).map(e => e[0]);
+// list of themes supported by daisyui
+const THEMES = ['light', 'dark']
+  // make sure light & dark are always at the beginning
+  .concat(Object.keys(daisyuiThemes).filter(t => t !== 'light' && t !== 'dark'));
+
+// markdown support
+const VueMarkdown = defineComponent(
+  (props) => {
+    const md = shallowRef(new MarkdownIt({
+      breaks: true,
+      highlight: function (str, lang) { // Add highlight.js
+        if (lang && hljs.getLanguage(lang)) {
+          try {
+            return '<pre><code class="hljs">' +
+                   hljs.highlight(str, { language: lang, ignoreIllegals: true }).value +
+                   '</code></pre>';
+          } catch (__) {}
+        }
+        return '<pre><code class="hljs">' + md.value.utils.escapeHtml(str) + '</code></pre>';
+      }
+    }));
+    // support latex with double dollar sign and square brackets
+    md.value.use(markdownItKatexGpt, {
+      delimiters: [
+        { left: '\\[', right: '\\]', display: true },
+        { left: '\\(', right: '\\)', display: false },
+        { left: '$$', right: '$$', display: false },
+        // do not add single dollar sign here, other wise it will confused with dollar used for money symbol
+      ],
+      throwOnError: false,
+    });
+    // support latex with single dollar sign
+    md.value.use(markdownItKatexNormal, { throwOnError: false });
+    // add copy button to code blocks
+    const origFenchRenderer = md.value.renderer.rules.fence;
+    md.value.renderer.rules.fence = (tokens, idx, ...args) => {
+      const content = tokens[idx].content;
+      const origRendered = origFenchRenderer(tokens, idx, ...args);
+      return `<div class="relative my-4">
+        <div class="text-right sticky top-4 mb-2 mr-2 h-0">
+          <button class="badge btn-mini" onclick="copyStr(${escapeAttr(JSON.stringify(content))})">📋 Copy</button>
+        </div>
+        ${origRendered}
+      </div>`;
+    };
+    window.copyStr = copyStr;
+    const content = computed(() => md.value.render(props.source));
+    return () => h("div", { innerHTML: content.value });
+  },
+  { props: ["source"] }
+);
+
+// input field to be used by settings modal
+const SettingsModalShortInput = defineComponent({
+  template: document.getElementById('settings-modal-short-input').innerHTML,
+  props: {
+    label: { type: String, required: false },
+    configKey: String,
+    configDefault: Object,
+    configInfo: Object,
+    modelValue: [Object, String, Number],
+  },
+});
+
+// message bubble component
+const MessageBubble = defineComponent({
+  components: {
+    VueMarkdown
+  },
+  template: document.getElementById('message-bubble').innerHTML,
+  props: {
+    config: Object,
+    msg: Object,
+    isGenerating: Boolean,
+    editUserMsgAndRegenerate: Function,
+    regenerateMsg: Function,
+  },
+  data() {
+    return {
+      editingContent: null,
+    };
+  },
+  computed: {
+    timings() {
+      if (!this.msg.timings) return null;
+      return {
+        ...this.msg.timings,
+        prompt_per_second: this.msg.timings.prompt_n / (this.msg.timings.prompt_ms / 1000),
+        predicted_per_second: this.msg.timings.predicted_n / (this.msg.timings.predicted_ms / 1000),
+      };
+    }
+  },
+  methods: {
+    copyMsg() {
+      copyStr(this.msg.content);
+    },
+    editMsg() {
+      this.editUserMsgAndRegenerate({
+        ...this.msg,
+        content: this.editingContent,
+      });
+      this.editingContent = null;
+    },
+  },
+});
+
+// coversations is stored in localStorage
+// format: { [convId]: { id: string, lastModified: number, messages: [...] } }
+// convId is a string prefixed with 'conv-'
+const StorageUtils = {
+  // manage conversations
+  getAllConversations() {
+    const res = [];
+    for (const key in localStorage) {
+      if (key.startsWith('conv-')) {
+        res.push(JSON.parse(localStorage.getItem(key)));
+      }
+    }
+    res.sort((a, b) => b.lastModified - a.lastModified);
+    return res;
+  },
+  // can return null if convId does not exist
+  getOneConversation(convId) {
+    return JSON.parse(localStorage.getItem(convId) || 'null');
+  },
+  // if convId does not exist, create one
+  appendMsg(convId, msg) {
+    if (msg.content === null) return;
+    const conv = StorageUtils.getOneConversation(convId) || {
+      id: convId,
+      lastModified: Date.now(),
+      messages: [],
+    };
+    conv.messages.push(msg);
+    conv.lastModified = Date.now();
+    localStorage.setItem(convId, JSON.stringify(conv));
+  },
+  getNewConvId() {
+    return `conv-${Date.now()}`;
+  },
+  remove(convId) {
+    localStorage.removeItem(convId);
+  },
+  filterAndKeepMsgs(convId, predicate) {
+    const conv = StorageUtils.getOneConversation(convId);
+    if (!conv) return;
+    conv.messages = conv.messages.filter(predicate);
+    conv.lastModified = Date.now();
+    localStorage.setItem(convId, JSON.stringify(conv));
+  },
+  popMsg(convId) {
+    const conv = StorageUtils.getOneConversation(convId);
+    if (!conv) return;
+    const msg = conv.messages.pop();
+    conv.lastModified = Date.now();
+    if (conv.messages.length === 0) {
+      StorageUtils.remove(convId);
+    } else {
+      localStorage.setItem(convId, JSON.stringify(conv));
+    }
+    return msg;
+  },
+
+  // manage config
+  getConfig() {
+    const savedVal = JSON.parse(localStorage.getItem('config') || '{}');
+    // to prevent breaking changes in the future, we always provide default value for missing keys
+    return {
+      ...CONFIG_DEFAULT,
+      ...savedVal,
+    };
+  },
+  setConfig(config) {
+    localStorage.setItem('config', JSON.stringify(config));
+  },
+  getTheme() {
+    return localStorage.getItem('theme') || 'auto';
+  },
+  setTheme(theme) {
+    if (theme === 'auto') {
+      localStorage.removeItem('theme');
+    } else {
+      localStorage.setItem('theme', theme);
+    }
+  },
+};
+
+// scroll to bottom of chat messages
+// if requiresNearBottom is true, only auto-scroll if user is near bottom
+const chatScrollToBottom = (requiresNearBottom) => {
+  const msgListElem = document.getElementById('messages-list');
+  const spaceToBottom = msgListElem.scrollHeight - msgListElem.scrollTop - msgListElem.clientHeight;
+  if (!requiresNearBottom || (spaceToBottom < 100)) {
+    setTimeout(() => msgListElem.scrollTo({ top: msgListElem.scrollHeight }), 1);
+  }
+};
+
+// wrapper for SSE
+async function* sendSSEPostRequest(url, fetchOptions) {
+  const res = await fetch(url, fetchOptions);
+  const lines = res.body
+    .pipeThrough(new TextDecoderStream())
+    .pipeThrough(new TextLineStream());
+  for await (const line of asyncIterator(lines)) {
+    if (isDev) console.log({line});
+    if (line.startsWith('data:') && !line.endsWith('[DONE]')) {
+      const data = JSON.parse(line.slice(5));
+      yield data;
+    } else if (line.startsWith('error:')) {
+      const data = JSON.parse(line.slice(6));
+      throw new Error(data.message || 'Unknown error');
+    }
+  }
+};
+
+const mainApp = createApp({
+  components: {
+    VueMarkdown,
+    SettingsModalShortInput,
+    MessageBubble,
+  },
+  data() {
+    return {
+      conversations: StorageUtils.getAllConversations(),
+      messages: [], // { id: number, role: 'user' | 'assistant', content: string }
+      viewingConvId: StorageUtils.getNewConvId(),
+      inputMsg: '',
+      isGenerating: false,
+      pendingMsg: null, // the on-going message from assistant
+      stopGeneration: () => {},
+      selectedTheme: StorageUtils.getTheme(),
+      config: StorageUtils.getConfig(),
+      showConfigDialog: false,
+      // const
+      themes: THEMES,
+      configDefault: {...CONFIG_DEFAULT},
+      configInfo: {...CONFIG_INFO},
+      isDev,
+    }
+  },
+  computed: {},
+  mounted() {
+    document.getElementById('app').classList.remove('opacity-0'); // show app
+    // scroll to the bottom when the pending message height is updated
+    const pendingMsgElem = document.getElementById('pending-msg');
+    const resizeObserver = new ResizeObserver(() => {
+      if (this.isGenerating) chatScrollToBottom(true);
+    });
+    resizeObserver.observe(pendingMsgElem);
+    this.setSelectedTheme(this.selectedTheme);
+  },
+  watch: {
+    viewingConvId: function(val, oldVal) {
+      if (val != oldVal) {
+        this.fetchMessages();
+        chatScrollToBottom();
+        this.hideSidebar();
+      }
+    }
+  },
+  methods: {
+    hideSidebar() {
+      document.getElementById('toggle-drawer').checked = false;
+    },
+    setSelectedTheme(theme) {
+      this.selectedTheme = theme;
+      document.body.setAttribute('data-theme', theme);
+      document.body.setAttribute('data-color-scheme', daisyuiThemes[theme]?.['color-scheme'] ?? 'auto');
+      StorageUtils.setTheme(theme);
+    },
+    newConversation() {
+      if (this.isGenerating) return;
+      this.viewingConvId = StorageUtils.getNewConvId();
+    },
+    setViewingConv(convId) {
+      if (this.isGenerating) return;
+      this.viewingConvId = convId;
+    },
+    deleteConv(convId) {
+      if (this.isGenerating) return;
+      if (window.confirm('Are you sure to delete this conversation?')) {
+        StorageUtils.remove(convId);
+        if (this.viewingConvId === convId) {
+          this.viewingConvId = StorageUtils.getNewConvId();
+        }
+        this.fetchConversation();
+        this.fetchMessages();
+      }
+    },
+    downloadConv(convId) {
+      const conversation = StorageUtils.getOneConversation(convId);
+      if (!conversation) {
+        alert('Conversation not found.');
+        return;
+      }
+      const conversationJson = JSON.stringify(conversation, null, 2);
+      const blob = new Blob([conversationJson], { type: 'application/json' });
+      const url = URL.createObjectURL(blob);
+      const a = document.createElement('a');
+      a.href = url;
+      a.download = `conversation_${convId}.json`;
+      document.body.appendChild(a);
+      a.click();
+      document.body.removeChild(a);
+      URL.revokeObjectURL(url);
+    },
+    async sendMessage() {
+      if (!this.inputMsg) return;
+      const currConvId = this.viewingConvId;
+
+      StorageUtils.appendMsg(currConvId, {
+        id: Date.now(),
+        role: 'user',
+        content: this.inputMsg,
+      });
+      this.fetchConversation();
+      this.fetchMessages();
+      this.inputMsg = '';
+      this.generateMessage(currConvId);
+      chatScrollToBottom();
+    },
+    async generateMessage(currConvId) {
+      if (this.isGenerating) return;
+      this.pendingMsg = { id: Date.now()+1, role: 'assistant', content: null };
+      this.isGenerating = true;
+
+      try {
+        const abortController = new AbortController();
+        this.stopGeneration = () => abortController.abort();
+        const params = {
+          messages: [
+            { role: 'system', content: this.config.systemMessage },
+            ...this.messages,
+          ],
+          stream: true,
+          cache_prompt: true,
+          samplers: this.config.samplers,
+          temperature: this.config.temperature,
+          dynatemp_range: this.config.dynatemp_range,
+          dynatemp_exponent: this.config.dynatemp_exponent,
+          top_k: this.config.top_k,
+          top_p: this.config.top_p,
+          min_p: this.config.min_p,
+          typical_p: this.config.typical_p,
+          xtc_probability: this.config.xtc_probability,
+          xtc_threshold: this.config.xtc_threshold,
+          repeat_last_n: this.config.repeat_last_n,
+          repeat_penalty: this.config.repeat_penalty,
+          presence_penalty: this.config.presence_penalty,
+          frequency_penalty: this.config.frequency_penalty,
+          dry_multiplier: this.config.dry_multiplier,
+          dry_base: this.config.dry_base,
+          dry_allowed_length: this.config.dry_allowed_length,
+          dry_penalty_last_n: this.config.dry_penalty_last_n,
+          max_tokens: this.config.max_tokens,
+          timings_per_token: !!this.config.showTokensPerSecond,
+          ...(this.config.custom.length ? JSON.parse(this.config.custom) : {}),
+        };
+        const chunks = sendSSEPostRequest(`${BASE_URL}/v1/chat/completions`, {
+          method: 'POST',
+          headers: {
+            'Content-Type': 'application/json',
+            'Authorization': this.config.apiKey ? `Bearer ${this.config.apiKey}` : undefined,
+          },
+          body: JSON.stringify(params),
+          signal: abortController.signal,
+        });
+        for await (const chunk of chunks) {
+          const stop = chunk.stop;
+          const addedContent = chunk.choices[0].delta.content;
+          const lastContent = this.pendingMsg.content || '';
+          if (addedContent) {
+            this.pendingMsg = {
+              id: this.pendingMsg.id,
+              role: 'assistant',
+              content: lastContent + addedContent,
+            };
+          }
+          const timings = chunk.timings;
+          if (timings && this.config.showTokensPerSecond) {
+            // only extract what's really needed, to save some space
+            this.pendingMsg.timings = {
+              prompt_n: timings.prompt_n,
+              prompt_ms: timings.prompt_ms,
+              predicted_n: timings.predicted_n,
+              predicted_ms: timings.predicted_ms,
+            };
+          }
+        }
+
+        StorageUtils.appendMsg(currConvId, this.pendingMsg);
+        this.fetchConversation();
+        this.fetchMessages();
+        setTimeout(() => document.getElementById('msg-input').focus(), 1);
+      } catch (error) {
+        if (error.name === 'AbortError') {
+          // user stopped the generation via stopGeneration() function
+          StorageUtils.appendMsg(currConvId, this.pendingMsg);
+          this.fetchConversation();
+          this.fetchMessages();
+        } else {
+          console.error(error);
+          alert(error);
+          // pop last user message
+          const lastUserMsg = StorageUtils.popMsg(currConvId);
+          this.inputMsg = lastUserMsg ? lastUserMsg.content : '';
+        }
+      }
+
+      this.pendingMsg = null;
+      this.isGenerating = false;
+      this.stopGeneration = () => {};
+      this.fetchMessages();
+      chatScrollToBottom();
+    },
+
+    // message actions
+    regenerateMsg(msg) {
+      if (this.isGenerating) return;
+      // TODO: somehow keep old history (like how ChatGPT has different "tree"). This can be done by adding "sub-conversations" with "subconv-" prefix, and new message will have a list of subconvIds
+      const currConvId = this.viewingConvId;
+      StorageUtils.filterAndKeepMsgs(currConvId, (m) => m.id < msg.id);
+      this.fetchConversation();
+      this.fetchMessages();
+      this.generateMessage(currConvId);
+    },
+    editUserMsgAndRegenerate(msg) {
+      if (this.isGenerating) return;
+      const currConvId = this.viewingConvId;
+      const newContent = msg.content;
+      StorageUtils.filterAndKeepMsgs(currConvId, (m) => m.id < msg.id);
+      StorageUtils.appendMsg(currConvId, {
+        id: Date.now(),
+        role: 'user',
+        content: newContent,
+      });
+      this.fetchConversation();
+      this.fetchMessages();
+      this.generateMessage(currConvId);
+    },
+
+    // settings dialog methods
+    closeAndSaveConfigDialog() {
+      try {
+        if (this.config.custom.length) JSON.parse(this.config.custom);
+      } catch (error) {
+        alert('Invalid JSON for custom config. Please either fix it or leave it empty.');
+        return;
+      }
+      for (const key of CONFIG_NUMERIC_KEYS) {
+        if (isNaN(this.config[key]) || this.config[key].toString().trim().length === 0) {
+          alert(`Invalid number for ${key} (expected an integer or a float)`);
+          return;
+        }
+        this.config[key] = parseFloat(this.config[key]);
+      }
+      this.showConfigDialog = false;
+      StorageUtils.setConfig(this.config);
+    },
+    closeAndDiscardConfigDialog() {
+      this.showConfigDialog = false;
+      this.config = StorageUtils.getConfig();
+    },
+    resetConfigDialog() {
+      if (window.confirm('Are you sure to reset all settings?')) {
+        this.config = {...CONFIG_DEFAULT};
+      }
+    },
+
+    // sync state functions
+    fetchConversation() {
+      this.conversations = StorageUtils.getAllConversations();
+    },
+    fetchMessages() {
+      this.messages = StorageUtils.getOneConversation(this.viewingConvId)?.messages ?? [];
+    },
+
+    // debug functions
+    async debugImportDemoConv() {
+      const res = await fetch('/demo-conversation.json');
+      const demoConv = await res.json();
+      StorageUtils.remove(demoConv.id);
+      for (const msg of demoConv.messages) {
+        StorageUtils.appendMsg(demoConv.id, msg);
+      }
+      this.fetchConversation();
+    }
+  },
+});
+mainApp.config.errorHandler = alert;
+try {
+  mainApp.mount('#app');
+} catch (err) {
+  console.error(err);
+  document.getElementById('app').innerHTML = `<div style="margin:2em auto">
+    Failed to start app. Please try clearing localStorage and try again.<br/>
+    <br/>
+    <button class="btn" onClick="localStorage.clear(); window.location.reload();">Clear localStorage</button>
+  </div>`;
+}

examples/server/webui/src/styles.scss

@@ -0,0 +1,48 @@
+@use "sass:meta";
+
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+.markdown {
+  h1, h2, h3, h4, h5, h6, ul, ol, li { all: revert; }
+  pre {
+    @apply whitespace-pre-wrap rounded-lg p-2;
+    border: 1px solid currentColor;
+  }
+  /* TODO: fix markdown table */
+}
+
+.show-on-hover {
+  @apply md:opacity-0 md:group-hover:opacity-100;
+}
+.btn-mini {
+  @apply cursor-pointer hover:shadow-md;
+}
+.chat-screen { max-width: 900px; }
+
+.chat-bubble-base-300 {
+  --tw-bg-opacity: 1;
+  --tw-text-opacity: 1;
+  @apply bg-base-300 text-base-content;
+}
+
+/* Highlight.js */
+[data-color-scheme='light'] {
+  @include meta.load-css('highlight.js/styles/stackoverflow-light');
+}
+[data-color-scheme='dark'] {
+  @include meta.load-css('highlight.js/styles/stackoverflow-dark');
+}
+[data-color-scheme='auto'] {
+  @media (prefers-color-scheme: light) {
+    @include meta.load-css('highlight.js/styles/stackoverflow-light');
+  }
+  @media (prefers-color-scheme: dark) {
+    @include meta.load-css('highlight.js/styles/stackoverflow-dark');
+  }
+}
+.hljs {
+  background: transparent !important;
+  padding: 0.5em !important;
+}

examples/server/webui/tailwind.config.js

@@ -0,0 +1,16 @@
+/** @type {import('tailwindcss').Config} */
+export default {
+  content: [
+    "./index.html",
+    "./src/**/*.{js,ts,jsx,tsx}",
+  ],
+  theme: {
+    extend: {},
+  },
+  plugins: [
+    require('daisyui'),
+  ],
+  daisyui: {
+    themes: ['light', 'dark', 'cupcake', 'bumblebee', 'emerald', 'corporate', 'synthwave', 'retro', 'cyberpunk', 'valentine', 'halloween', 'garden', 'forest', 'aqua', 'lofi', 'pastel', 'fantasy', 'wireframe', 'black', 'luxury', 'dracula', 'cmyk', 'autumn', 'business', 'acid', 'lemonade', 'night', 'coffee', 'winter', 'dim', 'nord', 'sunset'],
+  }
+}

examples/server/webui/vite.config.js

@@ -0,0 +1,59 @@
+
+import { viteSingleFile } from 'vite-plugin-singlefile';
+import path from 'path';
+import fs from 'fs';
+import zlib from 'zlib';
+
+const MAX_BUNDLE_SIZE = 1.5 * 1024 * 1024; // only increase when absolutely necessary
+
+const GUIDE_FOR_FRONTEND = `
+<!--
+  This is a single file build of the frontend.
+  It is automatically generated by the build process.
+  Do not edit this file directly.
+  To make changes, refer to the "Web UI" section in the README.
+-->
+`.trim();
+
+const BUILD_PLUGINS = [
+  viteSingleFile(),
+  (function llamaCppPlugin() {
+    let config;
+    return {
+      name: 'llamacpp:build',
+      apply: 'build',
+      async configResolved(_config) {
+        config = _config;
+      },
+      writeBundle() {
+        const outputIndexHtml = path.join(config.build.outDir, 'index.html');
+        const content = GUIDE_FOR_FRONTEND + '\n' + fs.readFileSync(outputIndexHtml, 'utf-8');
+        const compressed = zlib.gzipSync(Buffer.from(content, 'utf-8'), { level: 9 });
+
+        // because gzip header contains machine-specific info, we must remove these data from the header
+        // timestamp
+        compressed[0x4] = 0;
+        compressed[0x5] = 0;
+        compressed[0x6] = 0;
+        compressed[0x7] = 0;
+        // OS
+        compressed[0x9] = 0;
+
+        if (compressed.byteLength > MAX_BUNDLE_SIZE) {
+          throw new Error(
+            `Bundle size is too large (${Math.ceil(compressed.byteLength / 1024)} KB).\n` +
+            `Please reduce the size of the frontend or increase MAX_BUNDLE_SIZE in vite.config.js.\n`,
+          );
+        }
+
+        const targetOutputFile = path.join(config.build.outDir, '../../public/index.html.gz');
+        fs.writeFileSync(targetOutputFile, compressed);
+      }
+    }
+  })(),
+];
+
+/** @type {import('vite').UserConfig} */
+export default {
+  plugins: process.env.ANALYZE ? [] : BUILD_PLUGINS,
+};

examples/tokenize/tokenize.cpp

@@ -394,7 +394,7 @@ int main(int raw_argc, char ** raw_argv) {
     }
 
     if (show_token_count) {
-        printf("Total number of tokens: %ld\n", tokens.size());
+        printf("Total number of tokens: %zu\n", tokens.size());
     }
     // silence valgrind
     llama_free(ctx);

ggml/CMakeLists.txt

@@ -32,6 +32,13 @@ else()
     endif()
 endif()
 
+# remove the lib prefix on win32 mingw
+if (WIN32)
+    set(CMAKE_STATIC_LIBRARY_PREFIX "")
+    set(CMAKE_SHARED_LIBRARY_PREFIX "")
+    set(CMAKE_SHARED_MODULE_PREFIX  "")
+endif()
+
 option(BUILD_SHARED_LIBS "ggml: build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
 option(GGML_BACKEND_DL   "ggml: build backends as dynamic libraries (requires BUILD_SHARED_LIBS)" OFF)
 
@@ -92,30 +99,33 @@ else()
     set(INS_ENB ON)
 endif()
 
-option(GGML_CPU_HBM     "ggml: use memkind for CPU HBM" OFF)
-option(GGML_CPU_AARCH64 "ggml: use runtime weight conversion of Q4_0 to Q4_X_X" ON)
-
-option(GGML_AVX         "ggml: enable AVX"              ${INS_ENB})
-option(GGML_AVX_VNNI    "ggml: enable AVX-VNNI"         OFF)
-option(GGML_AVX2        "ggml: enable AVX2"             ${INS_ENB})
-option(GGML_AVX512      "ggml: enable AVX512"           OFF)
-option(GGML_AVX512_VBMI "ggml: enable AVX512-VBMI"      OFF)
-option(GGML_AVX512_VNNI "ggml: enable AVX512-VNNI"      OFF)
-option(GGML_AVX512_BF16 "ggml: enable AVX512-BF16"      OFF)
-option(GGML_AMX_TILE    "ggml: enable AMX-TILE"         OFF)
-option(GGML_AMX_INT8    "ggml: enable AMX-INT8"         OFF)
-option(GGML_AMX_BF16    "ggml: enable AMX-BF16"         OFF)
-option(GGML_FMA         "ggml: enable FMA"              ${INS_ENB})
+option(GGML_CPU_HBM          "ggml: use memkind for CPU HBM" OFF)
+option(GGML_CPU_AARCH64      "ggml: use runtime weight conversion of Q4_0 to Q4_X_X" ON)
+option(GGML_AVX              "ggml: enable AVX"              ${INS_ENB})
+option(GGML_AVX_VNNI         "ggml: enable AVX-VNNI"         OFF)
+option(GGML_AVX2             "ggml: enable AVX2"             ${INS_ENB})
+option(GGML_AVX512           "ggml: enable AVX512F"          OFF)
+option(GGML_AVX512_VBMI      "ggml: enable AVX512-VBMI"      OFF)
+option(GGML_AVX512_VNNI      "ggml: enable AVX512-VNNI"      OFF)
+option(GGML_AVX512_BF16      "ggml: enable AVX512-BF16"      OFF)
 if (NOT MSVC)
-    option(GGML_F16C    "ggml: enable F16C"             ${INS_ENB}) # in MSVC F16C is implied with AVX2/AVX512
+    # in MSVC F16C and FMA is implied with AVX2/AVX512
+    option(GGML_FMA          "ggml: enable FMA"              ${INS_ENB})
+    option(GGML_F16C         "ggml: enable F16C"             ${INS_ENB})
+    # MSVC does not seem to support AMX
+    option(GGML_AMX_TILE     "ggml: enable AMX-TILE"         OFF)
+    option(GGML_AMX_INT8     "ggml: enable AMX-INT8"         OFF)
+    option(GGML_AMX_BF16     "ggml: enable AMX-BF16"         OFF)
 endif()
-option(GGML_LASX        "ggml: enable lasx"             ON)
-option(GGML_LSX         "ggml: enable lsx"              ON)
-option(GGML_RVV         "ggml: enable rvv"              ON)
-option(GGML_SVE         "ggml: enable SVE"              OFF)
+option(GGML_LASX             "ggml: enable lasx"             ON)
+option(GGML_LSX              "ggml: enable lsx"              ON)
+option(GGML_RVV              "ggml: enable rvv"              ON)
+option(GGML_SVE              "ggml: enable SVE"              OFF)
+option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF)
+
 
 if (WIN32)
-    set(GGML_WIN_VER "0x602" CACHE STRING "ggml: Windows Version")
+    set(GGML_WIN_VER "0x602" CACHE STRING   "ggml: Windows version")
 endif()
 
 # ggml core
@@ -169,6 +179,11 @@ set   (GGML_SYCL_TARGET "INTEL" CACHE STRING
 set   (GGML_SYCL_DEVICE_ARCH "" CACHE STRING
                                             "ggml: sycl device architecture")
 
+option(GGML_OPENCL                          "ggml: use OpenCL"                                OFF)
+option(GGML_OPENCL_PROFILING                "ggml: use OpenCL profiling (increases overhead)" OFF)
+option(GGML_OPENCL_EMBED_KERNELS            "ggml: embed kernels"                             ON)
+option(GGML_OPENCL_USE_ADRENO_KERNELS       "ggml: use optimized kernels for Adreno"          ON)
+
 # extra artifacts
 option(GGML_BUILD_TESTS    "ggml: build tests"    ${GGML_STANDALONE})
 option(GGML_BUILD_EXAMPLES "ggml: build examples" ${GGML_STANDALONE})
@@ -180,11 +195,7 @@ option(GGML_BUILD_EXAMPLES "ggml: build examples" ${GGML_STANDALONE})
 set(CMAKE_C_STANDARD 11)
 set(CMAKE_C_STANDARD_REQUIRED true)
 
-if (GGML_SYCL)
-    set(CMAKE_CXX_STANDARD 17)
-else()
-    set(CMAKE_CXX_STANDARD 11)
-endif()
+set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_STANDARD_REQUIRED true)
 
 set(THREADS_PREFER_PTHREAD_FLAG ON)

ggml/include/ggml-backend.h

@@ -228,6 +228,7 @@ extern "C" {
     GGML_API void               ggml_backend_unload(ggml_backend_reg_t reg);
     // Load all known backends from dynamic libraries
     GGML_API void               ggml_backend_load_all(void);
+    GGML_API void               ggml_backend_load_all_from_path(const char * dir_path);
 
     //
     // Backend scheduler

ggml/include/ggml-cpu.h

@@ -103,24 +103,14 @@ extern "C" {
 
     // Internal types and functions exposed for tests and benchmarks
 
-    typedef void (*ggml_from_float_to_mat_t)
-                                     (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t nr, int64_t k, int64_t bs);
     typedef void (*ggml_vec_dot_t)  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x, size_t bx,
                                        const void * GGML_RESTRICT y, size_t by, int nrc);
-    typedef void (*ggml_gemv_t)     (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
-                                       const void * GGML_RESTRICT y, int nr, int nc);
-    typedef void (*ggml_gemm_t)     (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT x,
-                                       const void * GGML_RESTRICT y, int nr, int nc);
 
     struct ggml_type_traits_cpu {
         ggml_from_float_t        from_float;
-        ggml_from_float_to_mat_t from_float_to_mat;
         ggml_vec_dot_t           vec_dot;
         enum ggml_type           vec_dot_type;
         int64_t                  nrows; // number of rows to process simultaneously
-        int64_t                  ncols; // number of columns to process simultaneously
-        ggml_gemv_t              gemv;
-        ggml_gemm_t              gemm;
     };
 
     GGML_BACKEND_API const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type);
@@ -140,13 +130,6 @@ extern "C" {
 
     GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void);
 
-#ifdef GGML_USE_CPU_HBM
-    GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);
-#endif
-
-    GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cpu_aarch64_buffer_type(void);
-    GGML_BACKEND_API bool ggml_backend_cpu_buft_is_aarch64(ggml_backend_buffer_type_t buft);
-
 #ifdef __cplusplus
 }
 #endif

ggml/include/ggml-opencl.h

@@ -0,0 +1,26 @@
+#ifndef GGML_OPENCL_H
+#define GGML_OPENCL_H
+
+#include "ggml.h"
+#include "ggml-backend.h"
+
+#ifdef  __cplusplus
+extern "C" {
+#endif
+
+//
+// backend API
+//
+GGML_BACKEND_API ggml_backend_t ggml_backend_opencl_init(void);
+GGML_BACKEND_API bool ggml_backend_is_opencl(ggml_backend_t backend);
+
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type(void);
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type(void);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_opencl_reg(void);
+
+#ifdef  __cplusplus
+}
+#endif
+
+#endif // GGML_OPENCL_H

ggml/include/ggml.h

@@ -237,7 +237,9 @@
 #define GGML_EXIT_SUCCESS 0
 #define GGML_EXIT_ABORTED 1
 
-#define GGML_ROPE_TYPE_NEOX 2
+#define GGML_ROPE_TYPE_NEOX   2
+#define GGML_ROPE_TYPE_MROPE  8
+#define GGML_ROPE_TYPE_VISION 24
 
 #define GGUF_MAGIC "GGUF"
 
@@ -384,15 +386,15 @@ extern "C" {
         GGML_TYPE_F64     = 28,
         GGML_TYPE_IQ1_M   = 29,
         GGML_TYPE_BF16    = 30,
-        GGML_TYPE_Q4_0_4_4 = 31,
-        GGML_TYPE_Q4_0_4_8 = 32,
-        GGML_TYPE_Q4_0_8_8 = 33,
+        // GGML_TYPE_Q4_0_4_4 = 31, support has been removed from gguf files
+        // GGML_TYPE_Q4_0_4_8 = 32,
+        // GGML_TYPE_Q4_0_8_8 = 33,
         GGML_TYPE_TQ1_0   = 34,
         GGML_TYPE_TQ2_0   = 35,
-        GGML_TYPE_IQ4_NL_4_4 = 36,
+        // GGML_TYPE_IQ4_NL_4_4 = 36,
         // GGML_TYPE_IQ4_NL_4_8 = 37,
         // GGML_TYPE_IQ4_NL_8_8 = 38,
-        GGML_TYPE_COUNT,
+        GGML_TYPE_COUNT   = 39,
     };
 
     // precision
@@ -433,9 +435,6 @@ extern "C" {
         GGML_FTYPE_MOSTLY_IQ4_XS  = 22, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ1_M   = 23, // except 1d tensors
         GGML_FTYPE_MOSTLY_BF16    = 24, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_0_4_4 = 25, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_0_4_8 = 26, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_0_8_8 = 27, // except 1d tensors
     };
 
     // available tensor operations:
@@ -499,6 +498,7 @@ extern "C" {
         GGML_OP_POOL_2D_BACK,
         GGML_OP_UPSCALE, // nearest interpolate
         GGML_OP_PAD,
+        GGML_OP_PAD_REFLECT_1D,
         GGML_OP_ARANGE,
         GGML_OP_TIMESTEP_EMBEDDING,
         GGML_OP_ARGSORT,
@@ -1445,6 +1445,22 @@ extern "C" {
             float                 beta_fast,
             float                 beta_slow);
 
+    GGML_API struct ggml_tensor * ggml_rope_multi(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * c,
+            int                   n_dims,
+            int                   sections[4],
+            int                   mode,
+            int                   n_ctx_orig,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 ext_factor,
+            float                 attn_factor,
+            float                 beta_fast,
+            float                 beta_slow);
+
     // in-place, returns view(a)
     GGML_API struct ggml_tensor * ggml_rope_ext_inplace(
             struct ggml_context * ctx,
@@ -1695,6 +1711,13 @@ extern "C" {
             int                  p2,
             int                  p3);
 
+    // pad each dimension with reflection: [a, b, c, d] -> [b, a, b, c, d, c]
+    GGML_API struct ggml_tensor * ggml_pad_reflect_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   p0,
+            int                   p1);
+
     // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
     // timesteps: [N,]
     // return: [N, dim]
@@ -2197,11 +2220,19 @@ extern "C" {
     GGML_API size_t gguf_get_meta_size(const struct gguf_context * ctx);
     GGML_API void   gguf_get_meta_data(const struct gguf_context * ctx, void * data);
 
-#ifdef  __cplusplus
-// restrict not standard in C++
-#define GGML_RESTRICT
+#ifdef __cplusplus
+    // restrict not standard in C++
+#    if defined(__GNUC__)
+#        define GGML_RESTRICT __restrict__
+#    elif defined(__clang__)
+#        define GGML_RESTRICT __restrict
+#    elif defined(_MSC_VER)
+#        define GGML_RESTRICT __restrict
+#    else
+#        define GGML_RESTRICT
+#    endif
 #else
-#define GGML_RESTRICT restrict
+#    define GGML_RESTRICT restrict
 #endif
     typedef void (*ggml_to_float_t)  (const void  * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
     typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void  * GGML_RESTRICT y, int64_t k);

ggml/src/CMakeLists.txt

@@ -194,11 +194,6 @@ endif()
 
 if (WIN32)
     add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
-
-    if (BUILD_SHARED_LIBS)
-        # TODO: should not use this
-        set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
-    endif()
 endif()
 
 # ggml
@@ -220,9 +215,7 @@ add_library(ggml-base
             ggml-threading.cpp
             ggml-threading.h
             ggml-quants.c
-            ggml-quants.h
-            ggml-aarch64.c
-            ggml-aarch64.h)
+            ggml-quants.h)
 
 target_include_directories(ggml-base PRIVATE .)
 
@@ -269,7 +262,42 @@ function(ggml_add_backend backend)
     endif()
 endfunction()
 
+function(ggml_add_cpu_backend_variant tag_name)
+    set(GGML_CPU_TAG_NAME ${tag_name})
+    # other: OPENMP LLAMAFILE CPU_HBM
+    foreach (feat NATIVE
+                  AVX AVX2 AVX_VNNI FMA F16C
+                  AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16
+                  AMX_TILE AMX_INT8 AMX_BF16)
+        set(GGML_${feat} OFF)
+    endforeach()
+
+    foreach (feat ${ARGN})
+        set(GGML_${feat} ON)
+    endforeach()
+
+    ggml_add_cpu_backend_variant_impl(${tag_name})
+endfunction()
+
 ggml_add_backend(CPU)
+
+if (GGML_CPU_ALL_VARIANTS)
+    if (NOT GGML_BACKEND_DL)
+        message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS requires GGML_BACKEND_DL")
+    endif()
+    ggml_add_cpu_backend_variant(sandybridge    AVX)
+    ggml_add_cpu_backend_variant(haswell        AVX F16C AVX2 FMA)
+    ggml_add_cpu_backend_variant(skylakex       AVX F16C AVX2 FMA AVX512)
+    ggml_add_cpu_backend_variant(icelake        AVX F16C AVX2 FMA AVX512 AVX512_VBMI AVX512_VNNI)
+    if (NOT MSVC)
+        # MSVC doesn't support AVX-VNNI or AMX
+        ggml_add_cpu_backend_variant(alderlake      AVX F16C AVX2 FMA AVX_VNNI)
+        ggml_add_cpu_backend_variant(sapphirerapids AVX F16C AVX2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
+    endif()
+else ()
+    ggml_add_cpu_backend_variant_impl("")
+endif()
+
 ggml_add_backend(BLAS)
 ggml_add_backend(CANN)
 ggml_add_backend(CUDA)
@@ -280,6 +308,7 @@ ggml_add_backend(MUSA)
 ggml_add_backend(RPC)
 ggml_add_backend(SYCL)
 ggml_add_backend(Vulkan)
+ggml_add_backend(OpenCL)
 
 foreach (target ggml-base ggml)
     target_include_directories(${target} PUBLIC    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../include> $<INSTALL_INTERFACE:include>)

ggml/src/ggml-aarch64.c

@@ -1,129 +0,0 @@
-#define GGML_COMMON_DECL_C
-#include "ggml-common.h"
-
-#include "ggml-aarch64.h"
-#include "ggml-impl.h"
-#include "ggml-quants.h"
-#include <assert.h>
-
-#define UNUSED GGML_UNUSED
-
-static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave) {
-    block_q4_0x4 out;
-
-    for (int i = 0; i < 4; i++) {
-        out.d[i] = in[i].d;
-    }
-
-    const int end = QK4_0 * 2 / blck_size_interleave;
-
-    if (blck_size_interleave == 8) {
-        const uint64_t xor_mask = 0x8888888888888888ULL;
-        for (int i = 0; i < end; ++i) {
-            int src_id = i % 4;
-            int src_offset = (i / 4) * blck_size_interleave;
-            int dst_offset = i * blck_size_interleave;
-
-            uint64_t elems;
-            // Using memcpy to avoid unaligned memory accesses
-            memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t));
-            elems ^= xor_mask;
-            memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t));
-        }
-    } else if (blck_size_interleave == 4) {
-        const uint32_t xor_mask = 0x88888888;
-        for (int i = 0; i < end; ++i) {
-            int src_id = i % 4;
-            int src_offset = (i / 4) * blck_size_interleave;
-            int dst_offset = i * blck_size_interleave;
-
-            uint32_t elems;
-            memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint32_t));
-            elems ^= xor_mask;
-            memcpy(&out.qs[dst_offset], &elems, sizeof(uint32_t));
-        }
-    } else {
-        GGML_ASSERT(false);
-    }
-
-    return out;
-}
-
-// interleave 8 block_q4_0s in blocks of blck_size_interleave
-// returns an interleaved block_q4_0x8
-// in the interleaved block_q4_0x8, place deltas for 8 block_q4_0 blocks
-// first, then interleave quants from 8 block_q4_0s in blocks of blck_size_interleave
-static block_q4_0x8 make_block_q4_0x8(block_q4_0 * in, unsigned int blck_size_interleave) {
-    block_q4_0x8 out;
-
-    for (int i = 0; i < 8; i++) {
-        out.d[i] = in[i].d;
-    }
-
-    const int end = QK4_0 * 4 / blck_size_interleave;
-    const uint64_t xor_mask = 0x8888888888888888ULL;
-
-    for (int i = 0; i < end; ++i) {
-        int src_id = i % 8;
-        int src_offset = (i / 8) * blck_size_interleave;
-        int dst_offset = i * blck_size_interleave;
-
-        uint64_t elems;
-        memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t));
-        elems ^= xor_mask;
-        memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t));
-    }
-
-    return out;
-}
-
-static size_t quantize_q4_0_nr_bl(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, int nrows_interleaved, int blck_size_interleave) {
-    assert(n_per_row % QK4_0 == 0);
-    const int nb = n_per_row / QK4_0;
-
-    void * out_ptr = NULL;
-    if (nrows_interleaved == 8) {
-        out_ptr = (block_q4_0x8 *) dst;
-    }
-    else if (nrows_interleaved == 4) {
-        out_ptr = (block_q4_0x4 *) dst;
-    }
-    assert(nrows_interleaved <= 8);
-    block_q4_0 dst_tmp[8];
-
-    for (int b = 0; b < (nrow * n_per_row); b += nrows_interleaved * n_per_row) {
-
-        for (int64_t x = 0; x < nb; x++) {
-
-            for (int i  = 0; i < nrows_interleaved; i++ ) {
-                quantize_row_q4_0_ref(src + b + i * n_per_row + x * QK4_0, (block_q4_0 *) dst_tmp + i, QK4_0);
-            }
-
-            if (nrows_interleaved == 8) {
-                *(block_q4_0x8 *) out_ptr = make_block_q4_0x8(dst_tmp, blck_size_interleave);
-                out_ptr = (block_q4_0x8 *) out_ptr + 1;
-            }
-            else if (nrows_interleaved == 4) {
-                *(block_q4_0x4 *) out_ptr = make_block_q4_0x4(dst_tmp, blck_size_interleave);
-                out_ptr = (block_q4_0x4 *) out_ptr + 1;
-            }
-        }
-    }
-
-    return ((nrow * n_per_row) / QK4_0 * sizeof(block_q4_0));
-}
-
-size_t quantize_q4_0_4x4(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
-    UNUSED(quant_weights);
-    return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 4, 4);
-}
-
-size_t quantize_q4_0_4x8(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
-    UNUSED(quant_weights);
-    return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 4, 8);
-}
-
-size_t quantize_q4_0_8x8(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
-    UNUSED(quant_weights);
-    return quantize_q4_0_nr_bl(src, dst, nrow, n_per_row, 8, 8);
-}

ggml/src/ggml-aarch64.h

@@ -1,19 +0,0 @@
-#pragma once
-
-#include "ggml.h"
-
-// GGML internal header
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
-size_t quantize_q4_0_4x4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
-size_t quantize_q4_0_4x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
-size_t quantize_q4_0_8x8(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
-
-#ifdef __cplusplus
-}
-#endif
-

ggml/src/ggml-backend-reg.cpp

@@ -46,6 +46,10 @@
 #include "ggml-vulkan.h"
 #endif
 
+#ifdef GGML_USE_OPENCL
+#include "ggml-opencl.h"
+#endif
+
 #ifdef GGML_USE_BLAS
 #include "ggml-blas.h"
 #endif
@@ -146,6 +150,9 @@ struct ggml_backend_registry {
 #ifdef GGML_USE_VULKAN
         register_backend(ggml_backend_vk_reg());
 #endif
+#ifdef GGML_USE_OPENCL
+        register_backend(ggml_backend_opencl_reg());
+#endif
 #ifdef GGML_USE_CANN
         register_backend(ggml_backend_cann_reg());
 #endif
@@ -449,11 +456,21 @@ static std::string backend_filename_suffix() {
 #endif
 }
 
-static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent) {
+static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent, const char * user_search_path) {
     // enumerate all the files that match [lib]ggml-name-*.[so|dll] in the search paths
      // TODO: search system paths
-    std::vector<std::string> search_paths = { "./", get_executable_path() };
     std::string file_prefix = backend_filename_prefix() + name + "-";
+    std::vector<std::string> search_paths;
+    if (user_search_path == nullptr) {
+        search_paths.push_back("./");
+        search_paths.push_back(get_executable_path());
+    } else {
+#if defined(_WIN32)
+        search_paths.push_back(std::string(user_search_path) + "\\");
+#else
+        search_paths.push_back(std::string(user_search_path) + "/");
+#endif
+    }
 
     int best_score = 0;
     std::string best_path;
@@ -463,7 +480,8 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent)
         if (!fs::exists(search_path)) {
             continue;
         }
-        for (const auto & entry : fs::directory_iterator(search_path)) {
+        fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
+        for (const auto & entry : dir_it) {
             if (entry.is_regular_file()) {
                 std::string filename = entry.path().filename().string();
                 std::string ext = entry.path().extension().string();
@@ -483,6 +501,10 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent)
                                 best_score = s;
                                 best_path = entry.path().string();
                             }
+                        } else {
+                            if (!silent) {
+                                GGML_LOG_INFO("%s: failed to find ggml_backend_score in %s\n", __func__, entry.path().string().c_str());
+                            }
                         }
                     }
                 }
@@ -505,15 +527,26 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent)
 }
 
 void ggml_backend_load_all() {
-    ggml_backend_load_best("blas", true);
-    ggml_backend_load_best("cann", true);
-    ggml_backend_load_best("cuda", true);
-    ggml_backend_load_best("hip", true);
-    ggml_backend_load_best("kompute", true);
-    ggml_backend_load_best("metal", true);
-    ggml_backend_load_best("rpc", true);
-    ggml_backend_load_best("sycl", true);
-    ggml_backend_load_best("vulkan", true);
-    ggml_backend_load_best("musa", true);
-    ggml_backend_load_best("cpu", true);
+    ggml_backend_load_all_from_path(nullptr);
+}
+
+void ggml_backend_load_all_from_path(const char * dir_path) {
+#ifdef NDEBUG
+    bool silent = true;
+#else
+    bool silent = false;
+#endif
+
+    ggml_backend_load_best("blas", silent, dir_path);
+    ggml_backend_load_best("cann", silent, dir_path);
+    ggml_backend_load_best("cuda", silent, dir_path);
+    ggml_backend_load_best("hip", silent, dir_path);
+    ggml_backend_load_best("kompute", silent, dir_path);
+    ggml_backend_load_best("metal", silent, dir_path);
+    ggml_backend_load_best("rpc", silent, dir_path);
+    ggml_backend_load_best("sycl", silent, dir_path);
+    ggml_backend_load_best("vulkan", silent, dir_path);
+    ggml_backend_load_best("opencl", silent, dir_path);
+    ggml_backend_load_best("musa", silent, dir_path);
+    ggml_backend_load_best("cpu", silent, dir_path);
 }