llama : print a log of the total cache size

ggml-ci

.devops/full-cuda.Dockerfile

@@ -12,7 +12,7 @@ FROM ${BASE_CUDA_DEV_CONTAINER} as build
 ARG CUDA_DOCKER_ARCH=all
 
 RUN apt-get update && \
-    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1
+    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev
 
 COPY requirements.txt   requirements.txt
 COPY requirements       requirements
@@ -31,6 +31,6 @@ ENV LLAMA_CUDA=1
 # Enable cURL
 ENV LLAMA_CURL=1
 
-RUN make -j$(nproc)
+RUN make
 
 ENTRYPOINT ["/app/.devops/tools.sh"]

.devops/full-rocm.Dockerfile

@@ -45,6 +45,6 @@ ENV LLAMA_CURL=1
 RUN apt-get update && \
     apt-get install -y libcurl4-openssl-dev
 
-RUN make -j$(nproc)
+RUN make
 
 ENTRYPOINT ["/app/.devops/tools.sh"]

.devops/full.Dockerfile

@@ -3,7 +3,7 @@ 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
+    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev
 
 COPY requirements.txt   requirements.txt
 COPY requirements       requirements
@@ -18,7 +18,7 @@ COPY . .
 ENV LLAMA_CURL=1
 
 
-RUN make -j$(nproc)
+RUN make
 
 ENV LC_ALL=C.utf8
 

.devops/main-cuda.Dockerfile

@@ -23,13 +23,10 @@ ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
 # Enable CUDA
 ENV LLAMA_CUDA=1
 
-RUN make -j$(nproc) main
+RUN make
 
 FROM ${BASE_CUDA_RUN_CONTAINER} as runtime
 
-RUN apt-get update && \
-    apt-get install -y libgomp1
-
 COPY --from=build /app/main /main
 
 ENTRYPOINT [ "/main" ]

.devops/main-intel.Dockerfile

@@ -2,14 +2,6 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
 
 FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
 
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
-    echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
-    chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
-    rm /etc/apt/sources.list.d/intel-graphics.list && \
-    wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
-    echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
-    chmod 644 /usr/share/keyrings/intel-graphics.gpg
-
 ARG LLAMA_SYCL_F16=OFF
 RUN apt-get update && \
     apt-get install -y git

.devops/main-rocm.Dockerfile

@@ -40,6 +40,6 @@ ENV LLAMA_HIPBLAS=1
 ENV CC=/opt/rocm/llvm/bin/clang
 ENV CXX=/opt/rocm/llvm/bin/clang++
 
-RUN make -j$(nproc) main
+RUN make
 
 ENTRYPOINT [ "/app/main" ]

.devops/main-vulkan.Dockerfile

@@ -3,7 +3,7 @@ ARG UBUNTU_VERSION=jammy
 FROM ubuntu:$UBUNTU_VERSION as build
 
 # Install build tools
-RUN apt update && apt install -y git build-essential cmake wget libgomp1
+RUN apt update && apt install -y git build-essential cmake wget
 
 # Install Vulkan SDK
 RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \

.devops/main.Dockerfile

@@ -9,13 +9,10 @@ WORKDIR /app
 
 COPY . .
 
-RUN make -j$(nproc) main
+RUN make
 
 FROM ubuntu:$UBUNTU_VERSION as runtime
 
-RUN apt-get update && \
-    apt-get install -y libgomp1
-
 COPY --from=build /app/main /main
 
 ENV LC_ALL=C.utf8

.devops/server-cuda.Dockerfile

@@ -25,12 +25,12 @@ ENV LLAMA_CUDA=1
 # Enable cURL
 ENV LLAMA_CURL=1
 
-RUN make -j$(nproc) server
+RUN make
 
 FROM ${BASE_CUDA_RUN_CONTAINER} as runtime
 
 RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev libgomp1
+    apt-get install -y libcurl4-openssl-dev
 
 COPY --from=build /app/server /server
 

.devops/server-intel.Dockerfile

@@ -2,14 +2,6 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
 
 FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
 
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
-    echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
-    chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
-    rm /etc/apt/sources.list.d/intel-graphics.list && \
-    wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
-    echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
-    chmod 644 /usr/share/keyrings/intel-graphics.gpg
-
 ARG LLAMA_SYCL_F16=OFF
 RUN apt-get update && \
     apt-get install -y git libcurl4-openssl-dev
@@ -27,14 +19,6 @@ RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
 
 FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
 
-RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
-    echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
-    chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
-    rm /etc/apt/sources.list.d/intel-graphics.list && \
-    wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \
-    echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \
-    chmod 644 /usr/share/keyrings/intel-graphics.gpg
-
 RUN apt-get update && \
     apt-get install -y libcurl4-openssl-dev
 

.devops/server-rocm.Dockerfile

@@ -45,6 +45,6 @@ ENV LLAMA_CURL=1
 RUN apt-get update && \
     apt-get install -y libcurl4-openssl-dev
 
-RUN make -j$(nproc)
+RUN make
 
 ENTRYPOINT [ "/app/server" ]

.devops/server.Dockerfile

@@ -11,12 +11,12 @@ COPY . .
 
 ENV LLAMA_CURL=1
 
-RUN make -j$(nproc) server
+RUN make
 
 FROM ubuntu:$UBUNTU_VERSION as runtime
 
 RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev libgomp1
+    apt-get install -y libcurl4-openssl-dev
 
 COPY --from=build /app/server /server
 

.devops/tools.sh

@@ -8,7 +8,7 @@ arg1="$1"
 shift
 
 if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then
-    python3 ./convert-hf-to-gguf.py "$@"
+    python3 ./convert.py "$@"
 elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
     ./quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then

.github/ISSUE_TEMPLATE/06-question.yml

@@ -0,0 +1,38 @@
+name: Question
+description: Used to ask questions about llama.cpp
+title: "Question: "
+labels: ["question"]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        [Please search your question first in Discussion if you got a common general question.](https://github.com/ggerganov/llama.cpp/discussions/categories/q-a)
+
+  - type: checkboxes
+    id: prerequisites
+    attributes:
+      label: Prerequisites
+      description: Please confirm the following before submitting your question.
+      options:
+        - label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
+          required: true
+        - label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new useful question to share that cannot be answered within Discussions.
+          required: true
+
+  - type: textarea
+    id: background-description
+    attributes:
+      label: Background Description
+      description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an question.
+      placeholder: Detailed description of your question
+    validations:
+      required: true
+
+  - type: textarea
+    id: possible-answer
+    attributes:
+      label: Possible Answer
+      description: If you have some idea of possible answers you want to confirm, that would also be appreciated.
+      placeholder: Your idea of possible answers
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/06-research.yml

@@ -1,52 +0,0 @@
-name: Research
-description: Track new technical research area
-title: "Research: "
-labels: ["research 🔬"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Don't forget to check for any [duplicate research issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22)
-
-  - type: checkboxes
-    id: research-stage
-    attributes:
-      label: Research Stage
-      description: Track general state of this research ticket
-      options:
-        - label: Background Research (Let's try to avoid reinventing the wheel)
-        - label: Hypothesis Formed (How do you think this will work and it's effect?)
-        - label: Strategy / Implementation Forming
-        - label: Analysis of results
-        - label: Debrief / Documentation (So people in the future can learn from us)
-
-  - type: textarea
-    id: background
-    attributes:
-      label: Previous existing literature and research
-      description: Whats the current state of the art and whats the motivation for this research?
-
-  - type: textarea
-    id: hypothesis
-    attributes:
-      label: Hypothesis
-      description: How do you think this will work and it's effect?
-
-  - type: textarea
-    id: implementation
-    attributes:
-      label: Implementation
-      description: Got an approach? e.g. a PR ready to go?
-
-  - type: textarea
-    id: analysis
-    attributes:
-      label: Analysis
-      description: How does the proposed implementation behave?
-
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant log output
-      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
-      render: shell

.github/ISSUE_TEMPLATE/config.yml

@@ -1,13 +0,0 @@
-blank_issues_enabled: true
-contact_links:
-  - name: Got an idea?
-    url: https://github.com/ggerganov/llama.cpp/discussions/categories/ideas
-    about: Pop it there. It may then become an enhancement ticket.
-  - name: Got a question?
-    url: https://github.com/ggerganov/llama.cpp/discussions/categories/q-a
-    about: Ask a question there!
-  - name: Want to contribute?
-    url: https://github.com/ggerganov/llama.cpp/wiki/contribute
-    about: Head to the contribution guide page of the wiki for areas you can help with
-
-

.github/pull_request_template.md

@@ -1,5 +0,0 @@
-- Self Reported Review Complexity:
-    - [ ] Review Complexity : Low
-    - [ ] Review Complexity : Medium
-    - [ ] Review Complexity : High
-- [ ] I have read the [contributing guidelines](https://github.com/ggerganov/llama.cpp/blob/master/CONTRIBUTING.md)

.github/workflows/build.yml

@@ -13,7 +13,7 @@ on:
     paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m']
+    paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m']
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
@@ -294,22 +294,12 @@ jobs:
 
       - name: Build
         id: cmake_build
-        if: ${{ matrix.sanitizer != 'THREAD' }}
         run: |
           mkdir build
           cd build
           cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }}
           cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
 
-      - name: Build (no OpenMP)
-        id: cmake_build_no_openmp
-        if: ${{ matrix.sanitizer == 'THREAD' }}
-        run: |
-          mkdir build
-          cd build
-          cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} -DLLAMA_OPENMP=OFF
-          cmake --build . --config ${{ matrix.build_type }} -j $(nproc)
-
       - name: Test
         id: cmake_test
         run: |
@@ -684,10 +674,12 @@ jobs:
             cmake --build build --config ${{ matrix.build }} -j $(nproc)
 
   windows-latest-cmake:
-    runs-on: windows-2019
+    runs-on: windows-latest
 
     env:
       OPENBLAS_VERSION: 0.3.23
+      OPENCL_VERSION: 2023.04.17
+      CLBLAST_VERSION: 1.6.0
       SDE_VERSION: 9.33.0-2024-01-07
       VULKAN_VERSION: 1.3.261.1
 
@@ -704,6 +696,8 @@ jobs:
             defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF -DBUILD_SHARED_LIBS=ON'
           - build: 'avx512-x64'
             defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON'
+          - build: 'clblast-x64'
+            defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/clblast"'
           - build: 'openblas-x64'
             defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"'
           - build: 'kompute-x64'
@@ -728,6 +722,27 @@ jobs:
         run: |
           git submodule update --init kompute
 
+      - name: Download OpenCL SDK
+        id: get_opencl
+        if: ${{ matrix.build == 'clblast-x64' }}
+        run: |
+          curl.exe -o $env:RUNNER_TEMP/opencl.zip -L "https://github.com/KhronosGroup/OpenCL-SDK/releases/download/v${env:OPENCL_VERSION}/OpenCL-SDK-v${env:OPENCL_VERSION}-Win-x64.zip"
+          mkdir $env:RUNNER_TEMP/opencl
+          tar.exe -xvf $env:RUNNER_TEMP/opencl.zip --strip-components=1 -C $env:RUNNER_TEMP/opencl
+
+      - name: Download CLBlast
+        id: get_clblast
+        if: ${{ matrix.build == 'clblast-x64' }}
+        run: |
+          curl.exe -o $env:RUNNER_TEMP/clblast.7z -L "https://github.com/CNugteren/CLBlast/releases/download/${env:CLBLAST_VERSION}/CLBlast-${env:CLBLAST_VERSION}-windows-x64.7z"
+          curl.exe -o $env:RUNNER_TEMP/CLBlast.LICENSE.txt -L "https://github.com/CNugteren/CLBlast/raw/${env:CLBLAST_VERSION}/LICENSE"
+          7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/clblast.7z
+          rename-item $env:RUNNER_TEMP/CLBlast-${env:CLBLAST_VERSION}-windows-x64 clblast
+          foreach ($f in (gci -Recurse -Path "$env:RUNNER_TEMP/clblast" -Filter '*.cmake')) {
+            $txt = Get-Content -Path $f -Raw
+            $txt.Replace('C:/vcpkg/packages/opencl_x64-windows/', "$($env:RUNNER_TEMP.Replace('\','/'))/opencl/") | Set-Content -Path $f -Encoding UTF8
+          }
+
       - name: Download OpenBLAS
         id: get_openblas
         if: ${{ matrix.build == 'openblas-x64' }}
@@ -761,6 +776,13 @@ jobs:
           cmake -S . -B build ${{ matrix.defines }}
           cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS}
 
+      - name: Add clblast.dll
+        id: add_clblast_dll
+        if: ${{ matrix.build == 'clblast-x64' }}
+        run: |
+          cp $env:RUNNER_TEMP/clblast/lib/clblast.dll ./build/bin/Release
+          cp $env:RUNNER_TEMP/CLBlast.LICENSE.txt ./build/bin/Release/CLBlast-${env:CLBLAST_VERSION}.txt
+
       - name: Add libopenblas.dll
         id: add_libopenblas_dll
         if: ${{ matrix.build == 'openblas-x64' }}
@@ -784,7 +806,7 @@ 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: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'clblast-x64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }}
         run: |
           cd build
           ctest -L main -C Release --verbose --timeout 900
@@ -829,7 +851,7 @@ jobs:
           name: llama-bin-win-${{ matrix.build }}.zip
 
   windows-latest-cmake-cuda:
-    runs-on: windows-2019
+    runs-on: windows-latest
 
     strategy:
       matrix:
@@ -843,9 +865,8 @@ jobs:
         with:
           fetch-depth: 0
 
-      - name: Install CUDA toolkit
+      - uses: Jimver/cuda-toolkit@v0.2.11
         id: cuda-toolkit
-        uses: Jimver/cuda-toolkit@v0.2.15
         with:
           cuda: ${{ matrix.cuda }}
           method: 'network'
@@ -1040,7 +1061,7 @@ jobs:
 #        hypervisor: 'qemu'
 #        run: |
 #            sudo pkg update
-#            sudo pkg install -y gmake automake autoconf pkgconf llvm15 openblas
+#            sudo pkg install -y gmake automake autoconf pkgconf llvm15 clinfo clover opencl clblast openblas
 #            gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j `sysctl -n hw.ncpu`
 
   release:

.github/workflows/docker.yml

@@ -42,8 +42,9 @@ jobs:
           - { tag: "light-rocm", dockerfile: ".devops/main-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
           - { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
           - { tag: "server-rocm", dockerfile: ".devops/server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          - { tag: "light-intel", dockerfile: ".devops/main-intel.Dockerfile", platforms: "linux/amd64" }
-          - { tag: "server-intel", dockerfile: ".devops/server-intel.Dockerfile", platforms: "linux/amd64" }
+          # TODO: Disabled due to build issues https://github.com/ggerganov/llama.cpp/issues/7507
+          #- { tag: "light-intel", dockerfile: ".devops/main-intel.Dockerfile", platforms: "linux/amd64" }
+          #- { tag: "server-intel", dockerfile: ".devops/server-intel.Dockerfile", platforms: "linux/amd64" }
     steps:
       - name: Check out the repo
         uses: actions/checkout@v4

.github/workflows/server.yml

@@ -16,9 +16,11 @@ on:
     branches:
       - master
     paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
-  pull_request:
+  pull_request_target:
     types: [opened, synchronize, reopened]
     paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
+  schedule:
+    -  cron: '2 4 * * *'
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
@@ -113,7 +115,7 @@ jobs:
 
 
   server-windows:
-    runs-on: windows-2019
+    runs-on: windows-latest
 
     steps:
       - name: Clone

.gitignore

@@ -34,11 +34,9 @@ ggml-metal-embed.metal
 lcov-report/
 gcovr-report/
 
-tags
 build*
 !build.zig
 cmake-build-*
-android-ndk-*
 out/
 tmp/
 
@@ -107,7 +105,6 @@ examples/jeopardy/results.txt
 examples/server/*.html.hpp
 examples/server/*.js.hpp
 examples/server/*.mjs.hpp
-examples/server/*.css.hpp
 
 poetry.lock
 poetry.toml

CMakeLists.txt

@@ -106,11 +106,11 @@ set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING
                                              "llama: max. batch size for using peer access")
 option(LLAMA_CUDA_NO_PEER_COPY               "llama: do not use peer to peer copies"            OFF)
 option(LLAMA_CUDA_NO_VMM                     "llama: do not try to use CUDA VMM"                OFF)
-option(LLAMA_CUDA_FA_ALL_QUANTS              "llama: compile all quants for FlashAttention"     OFF)
 
 option(LLAMA_CURL                            "llama: use libcurl to download model from an URL" OFF)
 option(LLAMA_HIPBLAS                         "llama: use hipBLAS"                               OFF)
 option(LLAMA_HIP_UMA                         "llama: use HIP unified memory architecture"       OFF)
+option(LLAMA_CLBLAST                         "llama: use CLBlast"                               OFF)
 option(LLAMA_VULKAN                          "llama: use Vulkan"                                OFF)
 option(LLAMA_VULKAN_CHECK_RESULTS            "llama: run Vulkan op checks"                      OFF)
 option(LLAMA_VULKAN_DEBUG                    "llama: enable Vulkan debug output"                OFF)
@@ -125,7 +125,6 @@ set(LLAMA_METAL_MACOSX_VERSION_MIN "" CACHE STRING
 set(LLAMA_METAL_STD "" CACHE STRING          "llama: metal standard version (-std flag)")
 option(LLAMA_KOMPUTE                         "llama: use Kompute"                               OFF)
 option(LLAMA_RPC                             "llama: use RPC"                                   OFF)
-option(LLAMA_OPENMP                          "llama: use OpenMP"                                ON)
 option(LLAMA_SYCL                            "llama: use SYCL"                                  OFF)
 option(LLAMA_SYCL_F16                        "llama: use 16 bit floats for sycl calculations"   OFF)
 set(LLAMA_SYCL_TARGET   "INTEL" CACHE STRING "llama: sycl target device")
@@ -296,17 +295,6 @@ if (LLAMA_METAL)
         )
 endif()
 
-if (LLAMA_OPENMP)
-    find_package(OpenMP)
-    if (OpenMP_FOUND)
-        message(STATUS "OpenMP found")
-        add_compile_definitions(GGML_USE_OPENMP)
-        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} OpenMP::OpenMP_C OpenMP::OpenMP_CXX)
-    else()
-        message(WARNING "OpenMP not found")
-    endif()
-endif()
-
 if (LLAMA_BLAS)
     if (LLAMA_STATIC)
         set(BLA_STATIC ON)
@@ -402,36 +390,18 @@ if (LLAMA_CUBLAS)
 endif()
 
 if (LLAMA_CUDA)
-    cmake_minimum_required(VERSION 3.18)  # for CMAKE_CUDA_ARCHITECTURES
+    cmake_minimum_required(VERSION 3.17)
 
     find_package(CUDAToolkit)
     if (CUDAToolkit_FOUND)
         message(STATUS "CUDA found")
 
-        if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
-            # 52 == lowest CUDA 12 standard
-            # 60 == f16 CUDA intrinsics
-            # 61 == integer CUDA intrinsics
-            # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster
-            if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16)
-                set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics
-            else()
-                set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics
-                #set(CMAKE_CUDA_ARCHITECTURES "OFF") # use this to compile much faster, but only F16 models work
-            endif()
-        endif()
-        message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
-
         enable_language(CUDA)
 
         set(GGML_HEADERS_CUDA ggml-cuda.h)
 
         file(GLOB GGML_SOURCES_CUDA "ggml-cuda/*.cu")
         list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu")
-        file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu")
-        list(APPEND GGML_SOURCES_CUDA ${SRCS})
-        file(GLOB SRCS "ggml-cuda/template-instances/mmq*.cu")
-        list(APPEND GGML_SOURCES_CUDA ${SRCS})
 
         add_compile_definitions(GGML_USE_CUDA)
         add_compile_definitions(GGML_CUDA_USE_GRAPHS)
@@ -457,18 +427,6 @@ if (LLAMA_CUDA)
         if (LLAMA_CUDA_NO_PEER_COPY)
             add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
         endif()
-        if (LLAMA_CUDA_FA_ALL_QUANTS)
-            file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu")
-            list(APPEND GGML_SOURCES_CUDA ${SRCS})
-            add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS)
-        else()
-            file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu")
-            list(APPEND GGML_SOURCES_CUDA ${SRCS})
-            file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu")
-            list(APPEND GGML_SOURCES_CUDA ${SRCS})
-            file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu")
-            list(APPEND GGML_SOURCES_CUDA ${SRCS})
-        endif()
 
         if (LLAMA_STATIC)
             if (WIN32)
@@ -486,6 +444,21 @@ if (LLAMA_CUDA)
         else()
             set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver) # required by cuDeviceGetAttribute(), cuMemGetAllocationGranularity(...), ...
         endif()
+
+    if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
+        # 52 == lowest CUDA 12 standard
+        # 60 == f16 CUDA intrinsics
+        # 61 == integer CUDA intrinsics
+        # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster
+        if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16)
+            set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics
+        else()
+            set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics
+            #set(CMAKE_CUDA_ARCHITECTURES "") # use this to compile much faster, but only F16 models work
+        endif()
+    endif()
+    message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
+
     else()
         message(WARNING "CUDA not found")
     endif()
@@ -502,6 +475,22 @@ if (LLAMA_RPC)
     set(GGML_SOURCES_RPC ggml-rpc.cpp)
 endif()
 
+if (LLAMA_CLBLAST)
+    find_package(CLBlast)
+    if (CLBlast_FOUND)
+        message(STATUS "CLBlast found")
+
+        set(GGML_HEADERS_OPENCL ggml-opencl.h)
+        set(GGML_SOURCES_OPENCL ggml-opencl.cpp)
+
+        add_compile_definitions(GGML_USE_CLBLAST)
+
+        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} clblast)
+    else()
+        message(WARNING "CLBlast not found")
+    endif()
+endif()
+
 if (LLAMA_VULKAN)
     find_package(Vulkan)
     if (Vulkan_FOUND)
@@ -541,17 +530,12 @@ if (LLAMA_VULKAN)
 endif()
 
 if (LLAMA_HIPBLAS)
-    if (NOT EXISTS $ENV{ROCM_PATH})
-        if (NOT EXISTS /opt/rocm)
-            set(ROCM_PATH /usr)
-        else()
-            set(ROCM_PATH /opt/rocm)
-        endif()
-    else()
+    if ($ENV{ROCM_PATH})
         set(ROCM_PATH $ENV{ROCM_PATH})
+    else()
+        set(ROCM_PATH /opt/rocm)
     endif()
     list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH})
-    list(APPEND CMAKE_PREFIX_PATH "${ROCM_PATH}/lib64/cmake")
 
     # CMake on Windows doesn't support the HIP language yet
     if(WIN32)
@@ -587,10 +571,6 @@ if (LLAMA_HIPBLAS)
 
     file(GLOB GGML_SOURCES_ROCM "ggml-cuda/*.cu")
     list(APPEND GGML_SOURCES_ROCM "ggml-cuda.cu")
-    file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu")
-    list(APPEND GGML_SOURCES_ROCM ${SRCS})
-    file(GLOB SRCS "ggml-cuda/template-instances/mmq*.cu")
-    list(APPEND GGML_SOURCES_ROCM ${SRCS})
 
     add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUDA)
 
@@ -610,19 +590,6 @@ if (LLAMA_HIPBLAS)
         add_compile_definitions(GGML_CUDA_NO_PEER_COPY)
     endif()
 
-    if (LLAMA_CUDA_FA_ALL_QUANTS)
-        file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu")
-        list(APPEND GGML_SOURCES_ROCM ${SRCS})
-        add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS)
-    else()
-        file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu")
-        list(APPEND GGML_SOURCES_ROCM ${SRCS})
-        file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu")
-        list(APPEND GGML_SOURCES_ROCM ${SRCS})
-        file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu")
-        list(APPEND GGML_SOURCES_ROCM ${SRCS})
-    endif()
-
     add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
     add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
     add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
@@ -780,7 +747,6 @@ if (LLAMA_KOMPUTE)
             kompute-shaders/op_mul_mat_q4_0.comp
             kompute-shaders/op_mul_mat_q4_1.comp
             kompute-shaders/op_mul_mat_q6_k.comp
-            kompute-shaders/op_getrows_f32.comp
             kompute-shaders/op_getrows_f16.comp
             kompute-shaders/op_getrows_q4_0.comp
             kompute-shaders/op_getrows_q4_1.comp
@@ -813,7 +779,6 @@ if (LLAMA_KOMPUTE)
             shaderop_mul_mat_q4_0.h
             shaderop_mul_mat_q4_1.h
             shaderop_mul_mat_q6_k.h
-            shaderop_getrows_f32.h
             shaderop_getrows_f16.h
             shaderop_getrows_q4_0.h
             shaderop_getrows_q4_1.h
@@ -1251,6 +1216,7 @@ add_library(ggml OBJECT
             ggml-quants.c
             ggml-quants.h
             ${GGML_SOURCES_CUDA}      ${GGML_HEADERS_CUDA}
+            ${GGML_SOURCES_OPENCL}    ${GGML_HEADERS_OPENCL}
             ${GGML_SOURCES_METAL}     ${GGML_HEADERS_METAL}
             ${GGML_SOURCES_RPC}       ${GGML_HEADERS_RPC}
             ${GGML_SOURCES_EXTRA}     ${GGML_HEADERS_EXTRA}
@@ -1338,9 +1304,8 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake
         DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama)
 
 set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h"
-        "${GGML_HEADERS_CUDA}"
-        "${GGML_HEADERS_METAL}"
-        "${GGML_HEADERS_EXTRA}")
+        "${GGML_HEADERS_CUDA}"  "${GGML_HEADERS_OPENCL}"
+        "${GGML_HEADERS_METAL}" "${GGML_HEADERS_EXTRA}")
 
 set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")
 install(TARGETS ggml PUBLIC_HEADER)
@@ -1349,7 +1314,7 @@ set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}
 install(TARGETS llama LIBRARY PUBLIC_HEADER)
 
 install(
-    FILES convert-hf-to-gguf.py
+    FILES convert.py
     PERMISSIONS
         OWNER_READ
         OWNER_WRITE
@@ -1376,13 +1341,6 @@ if (LLAMA_METAL)
     endif()
 endif()
 
-configure_file(cmake/llama.pc.in
-        "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
-        @ONLY)
-
-install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
-        DESTINATION lib/pkgconfig)
-
 #
 # programs, examples and tests
 #

CONTRIBUTING.md

@@ -1,14 +0,0 @@
-# Contributing Guidelines
-
-## Checklist
-
-* Make sure your PR follows the [coding guidelines](https://github.com/ggerganov/llama.cpp/blob/master/README.md#coding-guidelines)
-* Test your changes using the commands in the [`tests`](tests) folder. For instance, running the `./tests/test-backend-ops` command tests different backend implementations of the GGML library
-* Execute [the full CI locally on your machine](ci/README.md) before publishing
-
-## PR formatting
-
-* Please rate the complexity of your PR (i.e. `Review Complexity : Low`, `Review Complexity : Medium`, `Review Complexity : High`). This makes it easier for maintainers to triage the PRs.
-    - The PR template has a series of review complexity checkboxes `[ ]` that you can mark as `[X]` for your conveience. Refer to [About task lists](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/about-task-lists) for more information.
-* If the pull request only contains documentation changes (e.g., updating READMEs, adding new wiki pages), please add `[no ci]` to the commit title. This will skip unnecessary CI checks and help reduce build times.
-* When squashing multiple commits on merge, use the following format for your commit title: `<module> : <commit title> (#<issue_number>)`. For example: `utils : Fix typo in utils.py (#1234)`

Makefile

@@ -1,7 +1,7 @@
 # Define the default target now so that it is always the first target
 BUILD_TARGETS = \
 	main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
-	simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama \
+	simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama beam-search  \
 	retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o
 
 # Binaries only useful for tests
@@ -57,8 +57,6 @@ ifeq ($(UNAME_S),Darwin)
 		LLAMA_METAL := 1
 	endif
 
-	LLAMA_NO_OPENMP := 1
-
 	ifneq ($(UNAME_P),arm)
 		SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null)
 		ifeq ($(SYSCTL_M),1)
@@ -69,10 +67,6 @@ ifeq ($(UNAME_S),Darwin)
 	endif
 endif
 
-ifdef LLAMA_RPC
-	BUILD_TARGETS += rpc-server
-endif
-
 default: $(BUILD_TARGETS)
 
 test: $(TEST_TARGETS)
@@ -141,16 +135,12 @@ MK_NVCCFLAGS = -std=c++11
 ifdef LLAMA_FAST
 MK_CFLAGS     += -Ofast
 HOST_CXXFLAGS += -Ofast
-ifndef LLAMA_DEBUG
 MK_NVCCFLAGS  += -O3
-endif # LLAMA_DEBUG
 else
 MK_CFLAGS     += -O3
 MK_CXXFLAGS   += -O3
-ifndef LLAMA_DEBUG
 MK_NVCCFLAGS  += -O3
-endif # LLAMA_DEBUG
-endif # LLAMA_FAST
+endif
 
 ifndef LLAMA_NO_CCACHE
 CCACHE := $(shell which ccache)
@@ -211,10 +201,9 @@ ifdef LLAMA_SCHED_MAX_COPIES
 endif
 
 ifdef LLAMA_DEBUG
-	MK_CFLAGS    += -O0 -g
-	MK_CXXFLAGS  += -O0 -g
-	MK_LDFLAGS   += -g
-	MK_NVCCFLAGS += -O0 -g
+	MK_CFLAGS   += -O0 -g
+	MK_CXXFLAGS += -O0 -g
+	MK_LDFLAGS  += -g
 
 	ifeq ($(UNAME_S),Linux)
 		MK_CPPFLAGS += -D_GLIBCXX_ASSERTIONS
@@ -411,12 +400,6 @@ ifndef LLAMA_NO_ACCELERATE
 	endif
 endif # LLAMA_NO_ACCELERATE
 
-ifndef LLAMA_NO_OPENMP
-	MK_CPPFLAGS += -DGGML_USE_OPENMP
-	MK_CFLAGS   += -fopenmp
-	MK_CXXFLAGS += -fopenmp
-endif # LLAMA_NO_OPENMP
-
 ifdef LLAMA_OPENBLAS
 	MK_CPPFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags-only-I openblas)
 	MK_CFLAGS   += $(shell pkg-config --cflags-only-other openblas)
@@ -433,26 +416,11 @@ ifdef LLAMA_BLIS
 	MK_LDFLAGS  += -lblis -L/usr/local/lib
 endif # LLAMA_BLIS
 
-ifdef LLAMA_RPC
-	MK_CPPFLAGS   += -DGGML_USE_RPC
-	OBJS          += ggml-rpc.o
-endif # LLAMA_RPC
-
 ifdef LLAMA_CUBLAS
 # LLAMA_CUBLAS is deprecated and will be removed in the future
 	LLAMA_CUDA := 1
 endif
 
-OBJS_CUDA_TEMP_INST      = $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-wmma*.cu))
-OBJS_CUDA_TEMP_INST     += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/mmq*.cu))
-ifdef LLAMA_CUDA_FA_ALL_QUANTS
-	OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*.cu))
-else
-	OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu))
-	OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu))
-	OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*f16-f16.cu))
-endif # LLAMA_CUDA_FA_ALL_QUANTS
-
 ifdef LLAMA_CUDA
 	ifneq ('', '$(wildcard /opt/cuda)')
 		CUDA_PATH ?= /opt/cuda
@@ -463,7 +431,6 @@ ifdef LLAMA_CUDA
 	MK_LDFLAGS   += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib
 	OBJS         += ggml-cuda.o
 	OBJS         += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
-	OBJS         += $(OBJS_CUDA_TEMP_INST)
 	MK_NVCCFLAGS += -use_fast_math
 ifdef LLAMA_FATAL_WARNINGS
 	MK_NVCCFLAGS += -Werror all-warnings
@@ -526,10 +493,7 @@ ifdef LLAMA_CUDA_NO_PEER_COPY
 endif # LLAMA_CUDA_NO_PEER_COPY
 ifdef LLAMA_CUDA_CCBIN
 	MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
-endif # LLAMA_CUDA_CCBIN
-ifdef LLAMA_CUDA_FA_ALL_QUANTS
-	MK_NVCCFLAGS += -DGGML_CUDA_FA_ALL_QUANTS
-endif # LLAMA_CUDA_FA_ALL_QUANTS
+endif
 
 ifdef JETSON_EOL_MODULE_DETECT
 define NVCC_COMPILE
@@ -541,13 +505,30 @@ define NVCC_COMPILE
 endef # NVCC_COMPILE
 endif # JETSON_EOL_MODULE_DETECT
 
-ggml-cuda/%.o: ggml-cuda/%.cu ggml.h ggml-common.h ggml-cuda/common.cuh
+ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh
 	$(NVCC_COMPILE)
 
 ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
 	$(NVCC_COMPILE)
 endif # LLAMA_CUDA
 
+ifdef LLAMA_CLBLAST
+	MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL)
+	MK_CFLAGS   += $(shell pkg-config --cflags-only-other clblast OpenCL)
+	MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL)
+
+	# Mac provides OpenCL as a framework
+	ifeq ($(UNAME_S),Darwin)
+		MK_LDFLAGS += -lclblast -framework OpenCL
+	else
+		MK_LDFLAGS += $(shell pkg-config --libs clblast OpenCL)
+	endif
+	OBJS    += ggml-opencl.o
+
+ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+endif # LLAMA_CLBLAST
+
 ifdef LLAMA_VULKAN
 	MK_CPPFLAGS  += -DGGML_USE_VULKAN
 	MK_LDFLAGS += -lvulkan
@@ -590,7 +571,6 @@ ifdef LLAMA_HIP_UMA
 	MK_CPPFLAGS += -DGGML_HIP_UMA
 endif # LLAMA_HIP_UMA
 	MK_LDFLAGS  += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib
-	MK_LDFLAGS  += -L$(ROCM_PATH)/lib64 -Wl,-rpath=$(ROCM_PATH)/lib64
 	MK_LDFLAGS	+= -lhipblas -lamdhip64 -lrocblas
 	HIPFLAGS    += $(addprefix --offload-arch=,$(AMDGPU_TARGETS))
 	HIPFLAGS    += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
@@ -604,12 +584,11 @@ ifdef LLAMA_CUDA_NO_PEER_COPY
 endif # LLAMA_CUDA_NO_PEER_COPY
 	OBJS        += ggml-cuda.o
 	OBJS        += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu))
-	OBJS        += $(OBJS_CUDA_TEMP_INST)
 
 ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
 	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
 
-ggml-cuda/%.o: ggml-cuda/%.cu ggml.h ggml-common.h ggml-cuda/common.cuh
+ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh
 	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
 
 endif # LLAMA_HIPBLAS
@@ -647,26 +626,11 @@ ggml-metal-embed.o: ggml-metal.metal ggml-common.h
 endif
 endif # LLAMA_METAL
 
-OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o
-COMMON_H_DEPS = common/common.h common/sampling.h common/log.h llama.h
-COMMON_DEPS   = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o
-
 ifndef LLAMA_NO_LLAMAFILE
 sgemm.o: sgemm.cpp sgemm.h ggml.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 endif
 
-ifdef LLAMA_RPC
-ggml-rpc.o: ggml-rpc.cpp ggml-rpc.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
-
-rpc-server.o: examples/rpc/rpc-server.cpp ggml-rpc.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
-
-rpc-server: rpc-server.o ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-endif # LLAMA_RPC
-
 GF_CC := $(CC)
 include scripts/get-flags.mk
 
@@ -746,9 +710,14 @@ unicode.o: unicode.cpp unicode.h
 unicode-data.o: unicode-data.cpp unicode-data.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
+OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o
+
 llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
+COMMON_H_DEPS = common/common.h common/sampling.h common/log.h llama.h
+COMMON_DEPS   = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o
+
 common.o: common/common.cpp $(COMMON_H_DEPS)
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
@@ -779,7 +748,6 @@ libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS)
 clean:
 	rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult lookup-create lookup-merge lookup-stats common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS)
 	rm -vrf ggml-cuda/*.o
-	rm -vrf ggml-cuda/template-instances/*.o
 	find examples pocs -type f -name "*.o" -delete
 
 #
@@ -848,7 +816,7 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/colorthemes.css.hpp examples/server/style.css.hpp examples/server/theme-beeninorder.css.hpp examples/server/theme-ketivah.css.hpp examples/server/theme-mangotango.css.hpp examples/server/theme-playground.css.hpp examples/server/theme-polarnight.css.hpp examples/server/theme-snowstorm.css.hpp examples/server/index.html.hpp examples/server/index-new.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/system-prompts.js.hpp examples/server/prompt-formats.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
 
@@ -898,6 +866,10 @@ baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) tra
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
+beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
+
 finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

README-sycl.md

@@ -29,7 +29,7 @@ The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based o
 
 When targeting **Intel CPU**, it is recommended to use llama.cpp for [Intel oneMKL](README.md#intel-onemkl) backend.
 
-It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
+It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, CLBlast etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose.
 
 ## News
 

README.md

@@ -2,9 +2,7 @@
 
 ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)
 
-[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
-[![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
-[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp)
+[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml)
 
 [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
 
@@ -22,8 +20,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 
 ### Hot topics
 
-- **`convert.py` has been deprecated and moved to `examples/convert-legacy-llama.py`, please use `convert-hf-to-gguf.py`** https://github.com/ggerganov/llama.cpp/pull/7430
-- Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021
+- **Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021**
 - BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920
 - MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387
 - Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404
@@ -53,6 +50,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
         <li><a href="#quantization">Quantization</a></li>
         <li><a href="#interactive-mode">Interactive mode</a></li>
         <li><a href="#constrained-output-with-grammars">Constrained output with grammars</a></li>
+        <li><a href="#instruct-mode">Instruct mode</a></li>
         <li><a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a></li>
         <li><a href="#seminal-papers-and-background-on-the-models">Seminal papers and background on the models</a></li>
         <li><a href="#perplexity-measuring-model-quality">Perplexity (measuring model quality)</a></li>
@@ -76,7 +74,7 @@ variety of hardware - locally and in the cloud.
 - AVX, AVX2 and AVX512 support for x86 architectures
 - 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
 - Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP)
-- Vulkan and SYCL backend support
+- Vulkan, SYCL, and (partial) OpenCL backend support
 - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
 
 Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has
@@ -149,8 +147,6 @@ Typically finetunes of the base models below are supported as well.
 
 [llama.cpp web server](./examples/server) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients.
 
-[simplechat](./examples/server/public_simplechat) is a simple chat client, which can be used to chat with the model exposed using above web server (use --path to point to simplechat), from a local web browser.
-
 **Bindings:**
 
 - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
@@ -204,7 +200,6 @@ Unless otherwise noted these projects are open-source with permissive licensing:
 - [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
 - [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
 - [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
-- [AIKit](https://github.com/sozercan/aikit) (MIT)
 
 *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
 
@@ -320,6 +315,8 @@ In order to build llama.cpp you have four different options.
       make
       ```
 
+      **Note**: for `Debug` builds, run `make LLAMA_DEBUG=1`
+
   - On Windows:
 
     1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
@@ -331,38 +328,40 @@ In order to build llama.cpp you have four different options.
         make
         ```
 
-  - Notes:
-    - 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`
-
 - Using `CMake`:
 
-  ```bash
-  cmake -B build
-  cmake --build build --config Release
-  ```
+    ```bash
+    cmake -B build
+    cmake --build build --config Release
+    ```
 
-  **Notes**:
+    **Note**: for `Debug` builds, there are two cases:
 
-    - 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):
+    - Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
 
       ```bash
       cmake -B build -DCMAKE_BUILD_TYPE=Debug
       cmake --build build
       ```
 
-      2. Multi-config generators (`-G` param set to Visual Studio, XCode...):
+    - Multi-config generators (`-G` param set to Visual Studio, XCode...):
 
       ```bash
       cmake -B build -G "Xcode"
       cmake --build build --config Debug
       ```
 
+- Using `Zig` (version 0.11 or later):
+
+    Building for optimization levels and CPU features can be accomplished using standard build arguments, for example AVX2, FMA, F16C,
+    it's also possible to cross compile for other operating systems and architectures:
+
+    ```bash
+    zig build -Doptimize=ReleaseFast -Dtarget=x86_64-windows-gnu -Dcpu=x86_64+avx2+fma+f16c
+    ```
+
+    The `zig targets` command will give you valid options to use.
+
 -   Using `gmake` (FreeBSD):
 
     1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics)
@@ -370,18 +369,15 @@ In order to build llama.cpp you have four different options.
     3. Install compilation dependencies.
 
         ```bash
-        sudo pkg install gmake automake autoconf pkgconf llvm15 openblas
+        sudo pkg install gmake automake autoconf pkgconf llvm15 clinfo clover \
+            opencl clblast openblas
 
         gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4
         ```
 
-### Homebrew
-
-On Mac and Linux, the homebrew package manager can be used via
-```
-brew install llama.cpp
-```
-The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668
+    **Notes:** With this packages you can build llama.cpp with OPENBLAS and
+    CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read
+    the instructions for use and activate this options in this document below.
 
 ### Metal Build
 
@@ -393,7 +389,7 @@ 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). 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 and CLBlast. There are currently several different BLAS implementations available for build and use:
 
 - #### Accelerate Framework:
 
@@ -486,7 +482,6 @@ Building the program with BLAS support may lead to some performance improvements
   | LLAMA_CUDA_F16                 | Boolean                | false   | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs.                                                           |
   | LLAMA_CUDA_KQUANTS_ITER        | 1 or 2                 | 2       | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs.                                                                                                                     |
   | LLAMA_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.                                                                         |
-  | LLAMA_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.                                                                                                  |
 
 - #### hipBLAS
 
@@ -547,6 +542,111 @@ Building the program with BLAS support may lead to some performance improvements
   | LLAMA_CUDA_MMV_Y        | Positive integer       | 1       | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants.                                                                       |
   | LLAMA_CUDA_KQUANTS_ITER | 1 or 2                 | 2       | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs.                                                                             |
 
+- #### CLBlast
+
+  OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU.
+
+  You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK).
+    - For Ubuntu, Debian, and Fedora the packages `opencl-headers`, `ocl-icd` may be needed.
+
+    - For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page.
+
+    - <details>
+        <summary>Installing the OpenCL SDK from source</summary>
+
+        ```sh
+        git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git
+        cd OpenCL-SDK
+        cmake -B build -DBUILD_DOCS=OFF \
+          -DBUILD_EXAMPLES=OFF \
+          -DBUILD_TESTING=OFF \
+          -DOPENCL_SDK_BUILD_SAMPLES=OFF \
+          -DOPENCL_SDK_TEST_SAMPLES=OFF
+        cmake --build build
+        cmake --install build --prefix /some/path
+        ```
+      </details>
+
+  ##### Installing CLBlast
+
+  Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages.
+
+  Linux packaging:
+  Fedora Linux:
+  ```bash
+  sudo dnf install clblast
+  ```
+
+  Alternatively, they may be built from source.
+
+  - <details>
+    <summary>Windows:</summary>
+
+      ```cmd
+      set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64"
+      git clone https://github.com/CNugteren/CLBlast.git
+      cd CLBlast
+      cmake -B build -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64
+      cmake --build build --config Release
+      cmake --install build --prefix C:/CLBlast
+      ```
+
+      (note: `--config Release` at build time is the default and only relevant for Visual Studio builds - or multi-config Ninja builds)
+
+  - <details>
+    <summary>Unix:</summary>
+
+      ```sh
+      git clone https://github.com/CNugteren/CLBlast.git
+      cd CLBlast
+      cmake -B build -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
+      cmake --build build --config Release
+      cmake --install build --prefix /some/path
+      ```
+
+      Where `/some/path` is where the built library will be installed (default is `/usr/local`).
+    </details>
+
+  ##### Building Llama with CLBlast
+
+  - Build with make:
+    ```sh
+    make LLAMA_CLBLAST=1
+    ```
+  - CMake (Unix):
+    ```sh
+    cmake -B build -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
+    cmake --build build --config Release
+    ```
+  - CMake (Windows):
+    ```cmd
+    set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast"
+    git clone https://github.com/ggerganov/llama.cpp
+    cd llama.cpp
+    cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
+    cmake --build build --config Release
+    cmake --install build --prefix C:/LlamaCPP
+    ```
+
+  ##### Running Llama with CLBlast
+
+  The CLBlast build supports `--gpu-layers|-ngl` like the CUDA version does.
+
+  To select the correct platform (driver) and device (GPU), you can use the environment variables `GGML_OPENCL_PLATFORM` and `GGML_OPENCL_DEVICE`.
+  The selection can be a number (starting from 0) or a text string to search:
+
+  ```sh
+  GGML_OPENCL_PLATFORM=1 ./main ...
+  GGML_OPENCL_DEVICE=2 ./main ...
+  GGML_OPENCL_PLATFORM=Intel ./main ...
+  GGML_OPENCL_PLATFORM=AMD GGML_OPENCL_DEVICE=1 ./main ...
+  ```
+
+  The default behavior is to find the first GPU device, but when it is an integrated GPU on a laptop, for instance, the selectors are useful.
+  Using the variables it is possible to select a CPU-based driver as well, if so desired.
+
+  You can get a list of platforms and devices from the `clinfo -l` command, etc.
+
 - #### Vulkan
 
   **With docker**:
@@ -576,9 +676,7 @@ Building the program with BLAS support may lead to some performance improvements
   vulkaninfo
   ```
 
-  Alternatively your package manager might be able to provide the appropriate libraries.
-  For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
-  For Fedora 40, you can install `vulkan-devel`, `glslc` and `glslang` packages.
+  Alternatively your package manager might be able to provide the appropiate libraries. For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
 
   Then, build llama.cpp using the cmake command below:
 
@@ -599,8 +697,7 @@ Building the program with BLAS support may lead to some performance improvements
 
 To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
 
-Note: `convert.py` has been moved to `examples/convert-legacy-llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derivatives.
-It does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face.
+Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face.
 
 ```bash
 # obtain the official LLaMA model weights and place them in ./models
@@ -617,10 +714,10 @@ ls ./models
 python3 -m pip install -r requirements.txt
 
 # convert the model to ggml FP16 format
-python3 convert-hf-to-gguf.py models/mymodel/
+python3 convert.py models/mymodel/
 
 # [Optional] for models using BPE tokenizers
-python convert-hf-to-gguf.py models/mymodel/ --vocab-type bpe
+python convert.py models/mymodel/ --vocab-type bpe
 
 # quantize the model to 4-bits (using Q4_K_M method)
 ./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M
@@ -770,6 +867,34 @@ The `grammars/` folder contains a handful of sample grammars. To write your own,
 
 For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one.
 
+### Instruct mode
+
+1. First, download and place the `ggml` model into the `./models` folder
+2. Run the `main` tool like this:
+
+```
+./examples/alpaca.sh
+```
+
+Sample run:
+
+```
+== Running in interactive mode. ==
+ - Press Ctrl+C to interject at any time.
+ - Press Return to return control to LLaMA.
+ - If you want to submit another line, end your input in '\'.
+
+ Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+> How many letters are there in the English alphabet?
+There 26 letters in the English Alphabet
+> What is the most common way of transportation in Amsterdam?
+The majority (54%) are using public transit. This includes buses, trams and metros with over 100 lines throughout the city which make it very accessible for tourists to navigate around town as well as locals who commute by tram or metro on a daily basis
+> List 5 words that start with "ca".
+cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
+>
+```
+
 ### Obtaining and using the Facebook LLaMA 2 model
 
 - Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data.

ci/run.sh

@@ -287,7 +287,7 @@ function gg_run_open_llama_7b_v2 {
     (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
     (time make -j                                                           ) 2>&1 | tee -a $OUT/${ci}-make.log
 
-    python3 ../examples/convert-legacy-llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
+    python3 ../convert.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
 
     model_f16="${path_models}/ggml-model-f16.gguf"
     model_q8_0="${path_models}/ggml-model-q8_0.gguf"

cmake/arm64-windows-llvm.cmake

@@ -9,7 +9,7 @@ set( CMAKE_CXX_COMPILER  clang++ )
 set( CMAKE_C_COMPILER_TARGET   ${target} )
 set( CMAKE_CXX_COMPILER_TARGET ${target} )
 
-set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast -fno-finite-math-only" )
+set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast" )
 set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" )
 
 set( CMAKE_C_FLAGS_INIT   "${arch_c_flags} ${warn_c_flags}" )

cmake/llama.pc.in

@@ -1,10 +0,0 @@
-prefix=@CMAKE_INSTALL_PREFIX@
-exec_prefix=${prefix}
-libdir=${exec_prefix}/lib
-includedir=${prefix}/include
-
-Name: llama
-Description: Port of Facebook's LLaMA model in C/C++
-Version: @PROJECT_VERSION@
-Libs: -L${libdir} -lllama
-Cflags: -I${includedir}

common/CMakeLists.txt

@@ -84,4 +84,4 @@ endif ()
 
 target_include_directories(${TARGET} PUBLIC .)
 target_compile_features(${TARGET} PUBLIC cxx_std_11)
-target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)
+target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama)

common/common.h

@@ -56,67 +56,66 @@ struct gpt_params {
     uint32_t seed                 = LLAMA_DEFAULT_SEED; // RNG seed
 
     int32_t n_threads             = cpu_get_num_math();
-    int32_t n_threads_draft       =    -1;
-    int32_t n_threads_batch       =    -1; // number of threads to use for batch processing (-1 = use n_threads)
-    int32_t n_threads_batch_draft =    -1;
-    int32_t n_predict             =    -1; // new tokens to predict
-    int32_t n_ctx                 =     0; // context size
-    int32_t n_batch               =  2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
-    int32_t n_ubatch              =   512; // physical batch size for prompt processing (must be >=32 to use BLAS)
-    int32_t n_keep                =     0; // number of tokens to keep from initial prompt
-    int32_t n_draft               =     5; // number of tokens to draft during speculative decoding
-    int32_t n_chunks              =    -1; // max number of chunks to process (-1 = unlimited)
-    int32_t n_parallel            =     1; // number of parallel sequences to decode
-    int32_t n_sequences           =     1; // number of sequences to decode
-    float   p_split               =  0.1f; // speculative decoding split probability
-    int32_t n_gpu_layers          =    -1; // number of layers to store in VRAM (-1 - use default)
-    int32_t n_gpu_layers_draft    =    -1; // number of layers to store in VRAM for the draft model (-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
-    int32_t n_beams               =     0; // if non-zero then use beam search of given width.
-    int32_t grp_attn_n            =     1; // group-attention factor
-    int32_t grp_attn_w            =   512; // group-attention width
-    int32_t n_print               =    -1; // print token count every n tokens (-1 = disabled)
-    float   rope_freq_base        =  0.0f; // RoPE base frequency
-    float   rope_freq_scale       =  0.0f; // RoPE frequency scaling factor
+    int32_t n_threads_draft       = -1;
+    int32_t n_threads_batch       = -1;    // number of threads to use for batch processing (-1 = use n_threads)
+    int32_t n_threads_batch_draft = -1;
+    int32_t n_predict             = -1;    // new tokens to predict
+    int32_t n_ctx                 = 512;   // context size
+    int32_t n_batch               = 2048;  // logical batch size for prompt processing (must be >=32 to use BLAS)
+    int32_t n_ubatch              = 512;   // physical batch size for prompt processing (must be >=32 to use BLAS)
+    int32_t n_keep                = 0;     // number of tokens to keep from initial prompt
+    int32_t n_draft               = 5;     // number of tokens to draft during speculative decoding
+    int32_t n_chunks              = -1;    // max number of chunks to process (-1 = unlimited)
+    int32_t n_parallel            = 1;     // number of parallel sequences to decode
+    int32_t n_sequences           = 1;     // number of sequences to decode
+    float   p_split               = 0.1f;  // speculative decoding split probability
+    int32_t n_gpu_layers          = -1;    // number of layers to store in VRAM (-1 - use default)
+    int32_t n_gpu_layers_draft    = -1;    // number of layers to store in VRAM for the draft model (-1 - use default)
+    llama_split_mode split_mode   = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
+    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
+    int32_t n_beams               = 0;     // if non-zero then use beam search of given width.
+    int32_t grp_attn_n            = 1;     // group-attention factor
+    int32_t grp_attn_w            = 512;   // group-attention width
+    int32_t n_print               = -1;    // print token count every n tokens (-1 = disabled)
+    float   rope_freq_base        = 0.0f;  // RoPE base frequency
+    float   rope_freq_scale       = 0.0f;  // RoPE frequency scaling factor
     float   yarn_ext_factor       = -1.0f; // YaRN extrapolation mix factor
-    float   yarn_attn_factor      =  1.0f; // YaRN magnitude scaling factor
+    float   yarn_attn_factor      = 1.0f;  // YaRN magnitude scaling factor
     float   yarn_beta_fast        = 32.0f; // YaRN low correction dim
-    float   yarn_beta_slow        =  1.0f; // YaRN high correction dim
-    int32_t yarn_orig_ctx         =     0; // YaRN original context length
+    float   yarn_beta_slow        = 1.0f;  // YaRN high correction dim
+    int32_t yarn_orig_ctx         = 0;     // YaRN original context length
     float   defrag_thold          = -1.0f; // KV cache defragmentation threshold
+    std::string rpc_servers       = "";    // comma separated list of RPC servers
 
     ggml_backend_sched_eval_callback cb_eval = nullptr;
     void * cb_eval_user_data                 = nullptr;
 
     ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
 
-    enum llama_split_mode        split_mode        = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
     enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
     enum llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
 
     // // sampling parameters
     struct llama_sampling_params sparams;
 
-    std::string model                = ""; // model path
-    std::string model_draft          = ""; // draft model for speculative decoding
+    std::string model                = "";  // model path
+    std::string model_draft          = "";  // draft model for speculative decoding
     std::string model_alias          = "unknown"; // model alias
-    std::string model_url            = ""; // model url to download
-    std::string hf_repo              = ""; // HF repo
-    std::string hf_file              = ""; // HF file
+    std::string model_url            = "";  // model url to download
+    std::string hf_repo              = "";  // HF repo
+    std::string hf_file              = "";  // HF file
     std::string prompt               = "";
-    std::string prompt_file          = ""; // store the external prompt file name
-    std::string path_prompt_cache    = ""; // path to file for saving/loading prompt eval state
-    std::string input_prefix         = ""; // string to prefix user inputs with
-    std::string input_suffix         = ""; // string to suffix user inputs with
-    std::string logdir               = ""; // directory in which to save YAML log files
+    std::string prompt_file          = "";  // store the external prompt file name
+    std::string path_prompt_cache    = "";  // path to file for saving/loading prompt eval state
+    std::string input_prefix         = "";  // string to prefix user inputs with
+    std::string input_suffix         = "";  // string to suffix user inputs with
+    std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
+    std::string logdir               = "";  // directory in which to save YAML log files
     std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding
     std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding
-    std::string logits_file          = ""; // file for saving *all* logits
-    std::string rpc_servers          = ""; // comma separated list of RPC servers
+    std::string logits_file          = "";  // file for saving *all* logits
 
-    std::vector<std::string> in_files;   // all input files
-    std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
     std::vector<llama_model_kv_override> kv_overrides;
 
     // TODO: avoid tuple, use struct
@@ -125,36 +124,37 @@ struct gpt_params {
 
     std::vector<llama_control_vector_load_info> control_vectors; // control vector with user defined scale
 
-    int32_t verbosity                  = 0;
     int32_t control_vector_layer_start = -1; // layer range for control vector
     int32_t control_vector_layer_end   = -1; // layer range for control vector
 
-    int32_t ppl_stride      = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
-    int32_t ppl_output_type = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
-                                     //                                       (which is more convenient to use for plotting)
-                                     //
-    bool   hellaswag        = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
-    size_t hellaswag_tasks  = 400;   // number of tasks to use when computing the HellaSwag score
+    int  ppl_stride        = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
+    int  ppl_output_type   = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
+                                    //                                       (which is more convenient to use for plotting)
+                                    //
+    bool   hellaswag       = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
+    size_t hellaswag_tasks = 400;   // number of tasks to use when computing the HellaSwag score
 
-    bool   winogrande       = false; // compute Winogrande score over random tasks from datafile supplied in prompt
-    size_t winogrande_tasks = 0;     // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
+    bool   winogrande      = false; // compute Winogrande score over random tasks from datafile supplied in prompt
+    size_t winogrande_tasks= 0;     // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
 
-    bool   multiple_choice  = false;  // compute TruthfulQA score over random tasks from datafile supplied in prompt
-    size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
+    bool   multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
+    size_t multiple_choice_tasks = 0;     // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
 
-    bool   kl_divergence    = false; // compute KL divergence
+    bool   kl_divergence   = false; // compute KL divergence
 
-    bool usage             = false; // print usage
+    bool random_prompt     = false; // do not randomize prompt if none provided
     bool use_color         = false; // use color to distinguish generations and inputs
-    bool special           = false; // enable special token output
     bool interactive       = false; // interactive mode
-    bool interactive_first = false; // wait for user input immediately
+    bool interactive_specials = false; // whether to allow special tokens from user, during interactive mode
+    bool special           = false; // enable special token output
     bool conversation      = false; // conversation mode (does not print special tokens and suffix/prefix)
+    bool chatml            = false; // chatml mode (used for models trained on chatml syntax)
     bool prompt_cache_all  = false; // save user input and generations to prompt cache
     bool prompt_cache_ro   = false; // open the prompt cache read-only and do not update it
 
     bool embedding         = false; // get only sentence embedding
-    bool escape            = true;  // escape "\n", "\r", "\t", "\'", "\"", and "\\"
+    bool escape            = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\"
+    bool interactive_first = false; // wait for user input immediately
     bool multiline_input   = false; // reverse the usage of `\`
     bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
     bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
@@ -162,6 +162,7 @@ struct gpt_params {
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
     bool ignore_eos        = false; // ignore generated EOS tokens
+    bool instruct          = false; // instruction mode (used for Alpaca models)
     bool logits_all        = false; // return logits for all tokens in the batch
     bool use_mmap          = true;  // use mmap for faster loads
     bool use_mlock         = false; // use mlock to keep model in memory
@@ -179,59 +180,6 @@ struct gpt_params {
     // multimodal models (see examples/llava)
     std::string mmproj = "";        // path to multimodal projector
     std::vector<std::string> image; // path to image file(s)
-
-    // server params
-    int32_t port           = 8080;         // server listens on this network port
-    int32_t timeout_read   = 600;          // http read timeout in seconds
-    int32_t timeout_write  = timeout_read; // http write timeout in seconds
-    int32_t n_threads_http = -1;           // number of threads to process HTTP requests
-
-    std::string hostname      = "127.0.0.1";
-    std::string public_path   = "";
-    std::string chat_template = "";
-    std::string system_prompt = "";
-
-    std::vector<std::string> api_keys;
-
-    std::string ssl_file_key  = "";
-    std::string ssl_file_cert = "";
-
-    bool endpoint_slots   = true;
-    bool endpoint_metrics = false;
-
-    bool log_json = false;
-
-    std::string slot_save_path;
-
-    float slot_prompt_similarity = 0.5f;
-
-    // batched-bench params
-    bool is_pp_shared = false;
-
-    std::vector<int32_t> n_pp;
-    std::vector<int32_t> n_tg;
-    std::vector<int32_t> n_pl;
-
-    // retrieval params
-    std::vector<std::string> context_files; // context files to embed
-
-    int32_t chunk_size = 64; // chunk size for context embedding
-
-    std::string chunk_separator = "\n"; // chunk separator for context embedding
-
-    // passkey params
-    int32_t n_junk = 250; // number of times to repeat the junk text
-    int32_t i_pos  = -1;  // position of the passkey in the junk text
-
-    // imatrix params
-    std::string out_file = "imatrix.dat"; // save the resulting imatrix to this file
-
-    int32_t n_out_freq  = 10; // output the imatrix every n_out_freq iterations
-    int32_t n_save_freq =  0; // save the imatrix every n_save_freq iterations
-    int32_t i_chunk     =  0; // start processing from this chunk
-
-    bool process_output = false; // collect data for the output tensor
-    bool compute_ppl    = true;  // whether to compute perplexity
 };
 
 void gpt_params_handle_model_default(gpt_params & params);
@@ -251,20 +199,7 @@ std::vector<std::string> string_split(std::string input, char separator);
 
 std::string string_strip(const std::string & str);
 std::string string_get_sortable_timestamp();
-
-template<class T>
-static std::vector<T> string_split(const std::string & str, char delim) {
-    std::vector<T> values;
-    std::istringstream str_stream(str);
-    std::string token;
-    while (std::getline(str_stream, token, delim)) {
-        T value;
-        std::istringstream token_stream(token);
-        token_stream >> value;
-        values.push_back(value);
-    }
-    return values;
-}
+std::string string_random_prompt(std::mt19937 & rng);
 
 bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
 void string_process_escapes(std::string & input);
@@ -277,7 +212,6 @@ bool fs_validate_filename(const std::string & filename);
 bool fs_create_directory_with_parents(const std::string & path);
 
 std::string fs_get_cache_directory();
-std::string fs_get_cache_file(const std::string & filename);
 
 //
 // Model utils
@@ -348,13 +282,6 @@ std::string llama_detokenize_bpe(
 // defaults to true when model type is SPM, otherwise false.
 bool llama_should_add_bos_token(const llama_model * model);
 
-//
-// Chat template utils
-//
-
-// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
-bool llama_chat_verify_template(const std::string & tmpl);
-
 //
 // KV cache utils
 //

common/grammar-parser.cpp

@@ -46,12 +46,8 @@ namespace grammar_parser {
         state.rules[rule_id] = rule;
     }
 
-    static bool is_digit_char(char c) {
-        return '0' <= c && c <= '9';
-    }
-
     static bool is_word_char(char c) {
-        return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || is_digit_char(c);
+        return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || ('0' <= c && c <= '9');
     }
 
     static std::pair<uint32_t, const char *> parse_hex(const char * src, int size) {
@@ -103,17 +99,6 @@ namespace grammar_parser {
         return pos;
     }
 
-    static const char * parse_int(const char * src) {
-        const char * pos = src;
-        while (is_digit_char(*pos)) {
-            pos++;
-        }
-        if (pos == src) {
-            throw std::runtime_error(std::string("expecting integer at ") + src);
-        }
-        return pos;
-    }
-
     static std::pair<uint32_t, const char *> parse_char(const char * src) {
         if (*src == '\\') {
             switch (src[1]) {
@@ -152,60 +137,6 @@ namespace grammar_parser {
             bool                                 is_nested) {
         size_t last_sym_start = out_elements.size();
         const char * pos = src;
-
-        auto handle_repetitions = [&](int min_times, int max_times) {
-
-            if (last_sym_start == out_elements.size()) {
-                throw std::runtime_error(std::string("expecting preceding item to */+/?/{ at ") + pos);
-            }
-
-            // apply transformation to previous symbol (last_sym_start to end) according to
-            // the following rewrite rules:
-            // S{m,n} --> S S S (m times) S'(n-m)
-            //            S'(x)   ::= S S'(x-1) |
-            //            (... n-m definitions of these S' rules ...)
-            //            S'(1)   ::= S |
-            // S{m,} -->  S S S (m times) S'
-            //            S'     ::= S S' |
-            // S*     --> S{0,}
-            //        --> S'     ::= S S' |
-            // S+     --> S{1,}
-            //        --> S S'
-            //            S'     ::= S S' |
-            // S?     --> S{0,1}
-            //        --> S'
-            //            S'     ::= S |
-
-            std::vector<llama_grammar_element> previous_elements(out_elements.begin() + last_sym_start, out_elements.end());
-            if (min_times == 0) {
-                out_elements.resize(last_sym_start);
-            } else {
-                // Repeat the previous elements (min_times - 1) times
-                for (int i = 1; i < min_times; i++) {
-                    out_elements.insert(out_elements.end(), previous_elements.begin(), previous_elements.end());
-                }
-            }
-
-            uint32_t last_rec_rule_id = 0;
-            auto n_opt = max_times < 0 ? 1 : max_times - min_times;
-
-            std::vector<llama_grammar_element> rec_rule(previous_elements);
-            for (int i = 0; i < n_opt; i++) {
-                rec_rule.resize(previous_elements.size());
-                uint32_t rec_rule_id = generate_symbol_id(state, rule_name);
-                if (i > 0 || max_times < 0) {
-                    rec_rule.push_back({LLAMA_GRETYPE_RULE_REF, max_times < 0 ? rec_rule_id : last_rec_rule_id});
-                }
-                rec_rule.push_back({LLAMA_GRETYPE_ALT, 0});
-                rec_rule.push_back({LLAMA_GRETYPE_END, 0});
-                add_rule(state, rec_rule_id, rec_rule);
-                last_rec_rule_id = rec_rule_id;
-            }
-            if (n_opt > 0) {
-                out_elements.push_back({LLAMA_GRETYPE_RULE_REF, last_rec_rule_id});
-            }
-        };
-
         while (*pos) {
             if (*pos == '"') { // literal string
                 pos++;
@@ -266,51 +197,40 @@ namespace grammar_parser {
                     throw std::runtime_error(std::string("expecting ')' at ") + pos);
                 }
                 pos = parse_space(pos + 1, is_nested);
-            } else if (*pos == '.') { // any char
-                last_sym_start = out_elements.size();
-                out_elements.push_back({LLAMA_GRETYPE_CHAR_ANY, 0});
-                pos = parse_space(pos + 1, is_nested);
-            } else if (*pos == '*') {
-                pos = parse_space(pos + 1, is_nested);
-                handle_repetitions(0, -1);
-            } else if (*pos == '+') {
-                pos = parse_space(pos + 1, is_nested);
-                handle_repetitions(1, -1);
-            } else if (*pos == '?') {
-                pos = parse_space(pos + 1, is_nested);
-                handle_repetitions(0, 1);
-            } else if (*pos == '{') {
-                pos = parse_space(pos + 1, is_nested);
-
-                if (!is_digit_char(*pos)) {
-                    throw std::runtime_error(std::string("expecting an int at ") + pos);
+            } else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator
+                if (last_sym_start == out_elements.size()) {
+                    throw std::runtime_error(std::string("expecting preceding item to */+/? at ") + pos);
                 }
-                const char * int_end = parse_int(pos);
-                int min_times = std::stoul(std::string(pos, int_end - pos));
-                pos = parse_space(int_end, is_nested);
 
-                int max_times = -1;
-
-                if (*pos == '}') {
-                    max_times = min_times;
-                    pos = parse_space(pos + 1, is_nested);
-                } else if (*pos == ',') {
-                    pos = parse_space(pos + 1, is_nested);
-
-                    if (is_digit_char(*pos)) {
-                        const char * int_end = parse_int(pos);
-                        max_times = std::stoul(std::string(pos, int_end - pos));
-                        pos = parse_space(int_end, is_nested);
-                    }
-
-                    if (*pos != '}') {
-                        throw std::runtime_error(std::string("expecting '}' at ") + pos);
-                    }
-                    pos = parse_space(pos + 1, is_nested);
-                } else {
-                    throw std::runtime_error(std::string("expecting ',' at ") + pos);
+                // apply transformation to previous symbol (last_sym_start to end) according to
+                // rewrite rules:
+                // S* --> S' ::= S S' |
+                // S+ --> S' ::= S S' | S
+                // S? --> S' ::= S |
+                uint32_t sub_rule_id = generate_symbol_id(state, rule_name);
+                std::vector<llama_grammar_element> sub_rule;
+                // add preceding symbol to generated rule
+                sub_rule.insert(
+                    sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end());
+                if (*pos == '*' || *pos == '+') {
+                    // cause generated rule to recurse
+                    sub_rule.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id});
                 }
-                handle_repetitions(min_times, max_times);
+                // mark start of alternate def
+                sub_rule.push_back({LLAMA_GRETYPE_ALT, 0});
+                if (*pos == '+') {
+                    // add preceding symbol as alternate only for '+' (otherwise empty)
+                    sub_rule.insert(
+                        sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end());
+                }
+                sub_rule.push_back({LLAMA_GRETYPE_END, 0});
+                add_rule(state, sub_rule_id, sub_rule);
+
+                // in original rule, replace previous symbol with reference to generated rule
+                out_elements.resize(last_sym_start);
+                out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id});
+
+                pos = parse_space(pos + 1, is_nested);
             } else {
                 break;
             }
@@ -405,7 +325,6 @@ namespace grammar_parser {
             case LLAMA_GRETYPE_CHAR_NOT:       return true;
             case LLAMA_GRETYPE_CHAR_ALT:       return true;
             case LLAMA_GRETYPE_CHAR_RNG_UPPER: return true;
-            case LLAMA_GRETYPE_CHAR_ANY:       return true;
             default:                           return false;
         }
     }
@@ -420,7 +339,6 @@ namespace grammar_parser {
                 case LLAMA_GRETYPE_CHAR_NOT:       fprintf(file, "CHAR_NOT");       break;
                 case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break;
                 case LLAMA_GRETYPE_CHAR_ALT:       fprintf(file, "CHAR_ALT");       break;
-                case LLAMA_GRETYPE_CHAR_ANY:       fprintf(file, "CHAR_ANY");       break;
             }
             switch (elem.type) {
                 case LLAMA_GRETYPE_END:
@@ -432,7 +350,6 @@ namespace grammar_parser {
                 case LLAMA_GRETYPE_CHAR_NOT:
                 case LLAMA_GRETYPE_CHAR_RNG_UPPER:
                 case LLAMA_GRETYPE_CHAR_ALT:
-                case LLAMA_GRETYPE_CHAR_ANY:
                     fprintf(file, "(\"");
                     print_grammar_char(file, elem.value);
                     fprintf(file, "\") ");
@@ -490,15 +407,11 @@ namespace grammar_parser {
                     }
                     print_grammar_char(file, elem.value);
                     break;
-                case LLAMA_GRETYPE_CHAR_ANY:
-                    fprintf(file, ".");
-                    break;
             }
             if (is_char_element(elem)) {
                 switch (rule[i + 1].type) {
                     case LLAMA_GRETYPE_CHAR_ALT:
                     case LLAMA_GRETYPE_CHAR_RNG_UPPER:
-                    case LLAMA_GRETYPE_CHAR_ANY:
                         break;
                     default:
                         fprintf(file, "] ");

common/json-schema-to-grammar.cpp

@@ -16,55 +16,92 @@ static std::string join(Iterator begin, Iterator end, const std::string & separa
 
 static std::string repeat(const std::string & str, size_t n);
 
-static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") {
-    auto has_max = max_items != std::numeric_limits<int>::max();
-
-    if (min_items == 0 && max_items == 1) {
-        return item_rule + "?";
-    }
-
+static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "", bool item_rule_is_literal = false) {
     if (separator_rule.empty()) {
-        if (min_items == 1 && !has_max) {
+        if (min_items == 0 && max_items == 1) {
+            return item_rule + "?";
+        } else if (min_items == 1 && max_items == std::numeric_limits<int>::max()) {
             return item_rule + "+";
-        } else if (min_items == 0 && !has_max) {
-            return item_rule + "*";
-        } else {
-            return item_rule + "{" + std::to_string(min_items) + "," + (has_max ? std::to_string(max_items) : "") + "}";
         }
     }
 
-    auto result = item_rule + " " + build_repetition("(" + separator_rule + " " + item_rule + ")", min_items == 0 ? 0 : min_items - 1, has_max ? max_items - 1 : max_items);
-    if (min_items == 0) {
-        result = "(" + result + ")?";
+    std::string result;
+    if (min_items > 0) {
+        if (item_rule_is_literal && separator_rule.empty()) {
+            result = "\"" + repeat(std::string(item_rule.begin() + 1, item_rule.end() - 1), min_items) + "\"";
+        } else {
+            std::vector<std::string> items(min_items, item_rule);
+            result = join(items.begin(), items.end(), separator_rule.empty() ? " " : " " + separator_rule + " ");
+        }
     }
+
+    std::function<std::string(int, bool)> opt_repetitions = [&](int up_to_n, bool prefix_with_sep) -> std::string {
+        auto content = prefix_with_sep && !separator_rule.empty() ? separator_rule + " " + item_rule : item_rule;
+
+        if (up_to_n == 0) {
+            return "";
+        } else if (up_to_n == 1) {
+            return "(" + content + ")?";
+        } else if (!separator_rule.empty() && !prefix_with_sep) {
+            return "(" + content + " " + opt_repetitions(up_to_n - 1, true) + ")?";
+        } else {
+            std::string res = repeat("(" + content + " ", up_to_n);
+            // strip trailing space
+            res = res.substr(0, res.length() - 1);
+            res += repeat(")?", up_to_n);
+            return res;
+        }
+    };
+
+    if (min_items > 0 && max_items != min_items) {
+        result += " ";
+    }
+
+    if (max_items != std::numeric_limits<int>::max()) {
+        result += opt_repetitions(max_items - min_items, min_items > 0);
+    } else {
+        std::string item_operator = "(" + (separator_rule.empty() ? "" : separator_rule + " ") + item_rule + ")";
+        if (min_items == 0 && !separator_rule.empty()) {
+            result = "(" + item_rule + " " + item_operator + "*)?";
+        } else {
+            result += item_operator + "*";
+        }
+    }
+
     return result;
 }
 
-const std::string SPACE_RULE = "| \" \" | \"\\n\" [ \\t]{0,20}";
+const std::string SPACE_RULE = "\" \"?";
 
 struct BuiltinRule {
     std::string content;
     std::vector<std::string> deps;
 };
 
+const std::string _up_to_15_digits = build_repetition("[0-9]", 0, 15);
+
 std::unordered_map<std::string, BuiltinRule> PRIMITIVE_RULES = {
     {"boolean", {"(\"true\" | \"false\") space", {}}},
-    {"decimal-part", {"[0-9]{1,16}", {}}},
-    {"integral-part", {"[0] | [1-9] [0-9]{0,15}", {}}},
+    {"decimal-part", {"[0-9] " + _up_to_15_digits, {}}},
+    {"integral-part", {"[0-9] | [1-9] " + _up_to_15_digits, {}}},
     {"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)? space", {"integral-part", "decimal-part"}}},
     {"integer", {"(\"-\"? integral-part) space", {"integral-part"}}},
     {"value", {"object | array | string | number | boolean | null", {"object", "array", "string", "number", "boolean", "null"}}},
     {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}},
     {"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}},
-    {"uuid", {"\"\\\"\" [0-9a-fA-F]{8} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{12} \"\\\"\" space", {}}},
-    {"char",   {"[^\"\\\\\\x7F\\x00-\\x1F] | [\\\\] ([\"\\\\bfnrt] | \"u\" [0-9a-fA-F]{4})", {}}},
+    {"uuid", {"\"\\\"\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
+                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
+                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
+                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
+                "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] \"\\\"\" space", {}}},
+    {"char",   {"[^\"\\\\] | \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])", {}}},
     {"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}},
     {"null", {"\"null\" space", {}}},
 };
 
 std::unordered_map<std::string, BuiltinRule> STRING_FORMAT_RULES = {
-    {"date", {"[0-9]{4} \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}},
-    {"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9]{3} )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}},
+    {"date", {"[0-9] [0-9] [0-9] [0-9] \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}},
+    {"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9] [0-9] [0-9] )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}},
     {"date-time", {"date \"T\" time", {"date", "time"}}},
     {"date-string", {"\"\\\"\" date \"\\\"\" space", {"date"}}},
     {"time-string", {"\"\\\"\" time \"\\\"\" space", {"time"}}},
@@ -348,7 +385,8 @@ private:
                         sub_is_literal ? "\"" + sub + "\"" : sub,
                         min_times,
                         max_times,
-                        ""
+                        "",
+                        sub_is_literal
                     );
                     seq.back().second = false;
                 } else {

convert-hf-to-gguf-update.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 
 # This script downloads the tokenizer models of the specified models from Huggingface and
 # generates the get_vocab_base_pre() function for convert-hf-to-gguf.py
@@ -83,7 +82,6 @@ models = [
     {"name": "jina-v2-es",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
     {"name": "jina-v2-de",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
     {"name": "smaug-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", },
-    {"name": "jina-v2-code",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
 ]
 
 

convert-hf-to-gguf.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 
 from __future__ import annotations
 
@@ -26,6 +25,8 @@ if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
 
+from convert import LlamaHfVocab
+
 logger = logging.getLogger("hf-to-gguf")
 
 
@@ -47,12 +48,11 @@ class Model:
     _model_classes: dict[str, type[Model]] = {}
 
     dir_model: Path
-    ftype: gguf.LlamaFileType
+    ftype: int
     is_big_endian: bool
     endianess: gguf.GGUFEndian
     use_temp_file: bool
     lazy: bool
-    model_name: str | None
     part_names: list[str]
     is_safetensors: bool
     hparams: dict[str, Any]
@@ -65,7 +65,7 @@ class Model:
     # subclasses should define this!
     model_arch: gguf.MODEL_ARCH
 
-    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool, model_name: str | None):
+    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool):
         if type(self) is Model:
             raise TypeError(f"{type(self).__name__!r} should not be directly instantiated")
         self.dir_model = dir_model
@@ -74,11 +74,10 @@ class Model:
         self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
         self.use_temp_file = use_temp_file
         self.lazy = not eager
-        self.model_name = model_name
-        self.part_names = Model.get_model_part_names(self.dir_model, "model", ".safetensors")
+        self.part_names = Model.get_model_part_names(self.dir_model, ".safetensors")
         self.is_safetensors = len(self.part_names) > 0
         if not self.is_safetensors:
-            self.part_names = Model.get_model_part_names(self.dir_model, "pytorch_model", ".bin")
+            self.part_names = Model.get_model_part_names(self.dir_model, ".bin")
         self.hparams = Model.load_hparams(self.dir_model)
         self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
         self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
@@ -96,7 +95,7 @@ class Model:
         ftype_lw: str = ftype_up.lower()
         # allow templating the file name with the output ftype, useful with the "auto" ftype
         self.fname_out = fname_out.parent / fname_out.name.format(ftype_lw, outtype=ftype_lw, ftype=ftype_lw, OUTTYPE=ftype_up, FTYPE=ftype_up)
-        self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
+        self.gguf_writer = gguf.GGUFWriter(self.fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
 
     @classmethod
     def __init_subclass__(cls):
@@ -184,7 +183,7 @@ class Model:
         return new_name
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
+        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_block_count(self.block_count)
 
         if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None:
@@ -326,21 +325,21 @@ class Model:
 
     def write(self):
         self.write_tensors()
-        self.gguf_writer.write_header_to_file(self.fname_out)
+        self.gguf_writer.write_header_to_file()
         self.gguf_writer.write_kv_data_to_file()
         self.gguf_writer.write_tensors_to_file(progress=True)
         self.gguf_writer.close()
 
     def write_vocab(self):
-        self.gguf_writer.write_header_to_file(self.fname_out)
+        self.gguf_writer.write_header_to_file()
         self.gguf_writer.write_kv_data_to_file()
         self.gguf_writer.close()
 
     @staticmethod
-    def get_model_part_names(dir_model: Path, prefix: str, suffix: str) -> list[str]:
+    def get_model_part_names(dir_model: Path, suffix: str) -> list[str]:
         part_names: list[str] = []
         for filename in os.listdir(dir_model):
-            if filename.startswith(prefix) and filename.endswith(suffix):
+            if filename.endswith(suffix):
                 part_names.append(filename)
 
         part_names.sort()
@@ -477,9 +476,6 @@ class Model:
         if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d":
             # ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct
             res = "smaug-bpe"
-        if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a":
-            # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code
-            res = "jina-v2-code"
 
         if res is None:
             logger.warning("\n")
@@ -638,7 +634,7 @@ class Model:
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def _set_vocab_llama_hf(self):
-        vocab = gguf.LlamaHfVocab(self.dir_model)
+        vocab = LlamaHfVocab(self.dir_model)
         tokens = []
         scores = []
         toktypes = []
@@ -667,7 +663,7 @@ class GPTNeoXModel(Model):
     def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
+        self.gguf_writer.add_name(self.dir_model.name)
         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)
@@ -800,7 +796,7 @@ class MPTModel(Model):
 
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layers"]
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
+        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
         self.gguf_writer.add_embedding_length(self.hparams["d_model"])
         self.gguf_writer.add_block_count(block_count)
@@ -852,7 +848,7 @@ class OrionModel(Model):
             raise ValueError("gguf: can not find ctx length parameter.")
 
         self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
+        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_source_hf_repo(hf_repo)
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
         self.gguf_writer.add_context_length(ctx_length)
@@ -889,7 +885,7 @@ class BaichuanModel(Model):
         else:
             raise ValueError("gguf: can not find ctx length parameter.")
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
+        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_source_hf_repo(hf_repo)
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
         self.gguf_writer.add_context_length(ctx_length)
@@ -1012,7 +1008,7 @@ class XverseModel(Model):
         else:
             raise ValueError("gguf: can not find ctx length parameter.")
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
+        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_source_hf_repo(hf_repo)
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
         self.gguf_writer.add_context_length(ctx_length)
@@ -1208,7 +1204,7 @@ class StableLMModel(Model):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
+        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -1397,49 +1393,6 @@ class LlamaModel(Model):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
-@Model.register("BitnetForCausalLM")
-class BitnetModel(Model):
-    model_arch = gguf.MODEL_ARCH.BITNET
-
-    def set_vocab(self):
-        self._set_vocab_sentencepiece()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-        self.gguf_writer.add_rope_scaling_factor(1.0)
-
-    def weight_quant(self, weight):
-        dtype = weight.dtype
-        weight = weight.float()
-        s = 1 / weight.abs().mean().clamp(min=1e-5)
-        result = (weight * s).round().clamp(-1, 1) / s
-        return result.type(dtype)
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # transform weight into 1/0/-1 (in fp32)
-        if name.endswith(("q_proj.weight", "k_proj.weight", "v_proj.weight",
-                          "down_proj.weight", "up_proj.weight", "gate_proj.weight",
-                          "o_proj.weight")):
-            data_torch = self.weight_quant(data_torch)
-
-        # pad 1D tensors
-        # TODO: is padding with 0s an invariant, or do we also need some scaling factor?
-        if name.endswith(("input_layernorm.weight", "post_attention_layernorm.weight", "model.norm.weight")):
-            data_torch = torch.nn.functional.pad(data_torch, (0, 256 - data_torch.size(0) % 256), mode='constant', value=0)
-            logger.info(f"pad {name} to {data_torch.size()}")
-
-        # pad 2D tensors
-        # TODO: double-check that this is the correct way to pad the rows
-        if name.endswith(("embed_tokens.weight", "q_proj.weight", "k_proj.weight", "v_proj.weight",
-                          "down_proj.weight", "up_proj.weight", "gate_proj.weight",
-                          "o_proj.weight")):
-            data_torch = torch.nn.functional.pad(data_torch, (0, 256 - data_torch.size(1) % 256), mode='constant', value=0)
-            logger.info(f"pad {name} to {data_torch.size()}")
-
-        return [(self.map_tensor_name(name), data_torch)]
-
-
 @Model.register("GrokForCausalLM")
 class GrokModel(Model):
     model_arch = gguf.MODEL_ARCH.GROK
@@ -1726,7 +1679,7 @@ class GPT2Model(Model):
     model_arch = gguf.MODEL_ARCH.GPT2
 
     def set_gguf_parameters(self):
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
+        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_block_count(self.hparams["n_layer"])
         self.gguf_writer.add_context_length(self.hparams["n_ctx"])
         self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
@@ -2293,7 +2246,7 @@ class GemmaModel(Model):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
+        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
         self.gguf_writer.add_embedding_length(hparams["hidden_size"])
         self.gguf_writer.add_block_count(block_count)
@@ -2393,7 +2346,7 @@ class MambaModel(Model):
         # Fail early for models which don't have a block expansion factor of 2
         assert d_inner == 2 * d_model
 
-        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
+        self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
         self.gguf_writer.add_embedding_length(d_model)
         self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
@@ -2500,13 +2453,11 @@ class JinaBertV2Model(BertModel):
 
     def get_tensors(self):
         for name, data in super().get_tensors():
-            if 'gated_layer' in name:
+            if 'gated_layers' in name:
                 d1 = data[:self.intermediate_size, :]
                 name1 = name.replace('gated_layers', 'gated_layers_w')
-                name1 = name1.replace('up_gated_layer', 'gated_layers_v')
                 d2 = data[self.intermediate_size:, :]
                 name2 = name.replace('gated_layers', 'gated_layers_v')
-                name2 = name2.replace('up_gated_layer', 'gated_layers_w')
                 yield name1, d1
                 yield name2, d2
                 continue
@@ -2891,13 +2842,8 @@ def main() -> None:
     hparams = Model.load_hparams(dir_model)
 
     with torch.inference_mode():
-        try:
-            model_class = Model.from_model_architecture(hparams["architectures"][0])
-        except NotImplementedError:
-            logger.error(f"Model {hparams['architectures'][0]} is not supported")
-            sys.exit(1)
-
-        model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file, args.no_lazy, args.model_name)
+        model_class = Model.from_model_architecture(hparams["architectures"][0])
+        model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file, args.no_lazy)
 
         logger.info("Set model parameters")
         model_instance.set_gguf_parameters()

convert.py

@@ -24,16 +24,14 @@ from abc import ABC, abstractmethod
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
 from dataclasses import dataclass
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, IO, Iterable, Literal, TypeVar, Optional
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable, Optional
 
 import numpy as np
+from sentencepiece import SentencePieceProcessor
 
 if 'NO_LOCAL_GGUF' not in os.environ:
-    # use .parent.parent since we are in "examples" directory
-    sys.path.insert(1, str(Path(__file__).parent.parent / 'gguf-py'))
-
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
-from gguf import BaseVocab, Vocab, NoVocab, BpeVocab, SentencePieceVocab, LlamaHfVocab
 
 if TYPE_CHECKING:
     from typing_extensions import Self, TypeAlias
@@ -176,7 +174,7 @@ class Params:
     rope_scaling_type: gguf.RopeScalingType | None = None
     f_rope_freq_base: float | None = None
     f_rope_scale: float | None = None
-    n_ctx_orig: int | None = None
+    n_orig_ctx: int | None = None
     rope_finetuned: bool | None = None
 
     ftype: GGMLFileType | None = None
@@ -226,7 +224,7 @@ class Params:
         with open(config_path) as f:
             config = json.load(f)
 
-        rope_scaling_type = f_rope_scale = n_ctx_orig = rope_finetuned = None
+        rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None
         rope_scaling = config.get("rope_scaling")
 
         if rope_scaling is not None and (typ := rope_scaling.get("type")):
@@ -236,7 +234,7 @@ class Params:
                 rope_scaling_type = gguf.RopeScalingType.LINEAR
             elif typ == "yarn":
                 rope_scaling_type = gguf.RopeScalingType.YARN
-                n_ctx_orig = rope_scaling['original_max_position_embeddings']
+                n_orig_ctx = rope_scaling['original_max_position_embeddings']
                 rope_finetuned = rope_scaling['finetuned']
             else:
                 raise NotImplementedError(f'Unknown rope scaling type: {typ}')
@@ -272,7 +270,7 @@ class Params:
             f_rope_freq_base  = config.get("rope_theta"),
             rope_scaling_type = rope_scaling_type,
             f_rope_scale      = f_rope_scale,
-            n_ctx_orig        = n_ctx_orig,
+            n_orig_ctx        = n_orig_ctx,
             rope_finetuned    = rope_finetuned,
         )
 
@@ -382,6 +380,306 @@ class Metadata:
         return metadata
 
 
+#
+# vocab
+#
+
+
+@runtime_checkable
+class BaseVocab(Protocol):
+    tokenizer_model: ClassVar[str]
+    name: ClassVar[str]
+
+
+class NoVocab(BaseVocab):
+    tokenizer_model = "no_vocab"
+    name = "no_vocab"
+
+    def __repr__(self) -> str:
+        return "<NoVocab for a model without integrated vocabulary>"
+
+
+@runtime_checkable
+class Vocab(BaseVocab, Protocol):
+    vocab_size: int
+    added_tokens_dict: dict[str, int]
+    added_tokens_list: list[str]
+    fname_tokenizer: Path
+
+    def __init__(self, base_path: Path): ...
+    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ...
+
+
+class BpeVocab(Vocab):
+    tokenizer_model = "gpt2"
+    name = "bpe"
+
+    def __init__(self, base_path: Path):
+        added_tokens: dict[str, int] = {}
+
+        if (fname_tokenizer := base_path / 'vocab.json').exists():
+            # "slow" tokenizer
+            with open(fname_tokenizer, encoding="utf-8") as f:
+                self.vocab = json.load(f)
+
+            try:
+                # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab.
+                with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
+                    added_tokens = json.load(f)
+            except FileNotFoundError:
+                pass
+        else:
+            # "fast" tokenizer
+            fname_tokenizer = base_path / FAST_TOKENIZER_FILE
+
+            # if this fails, FileNotFoundError propagates to caller
+            with open(fname_tokenizer, encoding="utf-8") as f:
+                tokenizer_json = json.load(f)
+
+            tokenizer_model: dict[str, Any] = tokenizer_json['model']
+            if (
+                tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False)
+                or tokenizer_json['decoder']['type'] != 'ByteLevel'
+            ):
+                raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer')
+
+            self.vocab = tokenizer_model["vocab"]
+
+            if (added := tokenizer_json.get('added_tokens')) is not None:
+                # Added tokens here can be duplicates of the main vocabulary.
+                added_tokens = {item['content']: item['id']
+                                for item in added
+                                if item['content'] not in self.vocab}
+
+        vocab_size   = len(self.vocab)
+        expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
+        actual_ids   = sorted(added_tokens.values())
+        if expected_ids != actual_ids:
+            expected_end_id = vocab_size + len(actual_ids) - 1
+            raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range "
+                             f"{vocab_size} - {expected_end_id}; got {actual_ids}")
+
+        items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
+        self.added_tokens_dict    = added_tokens
+        self.added_tokens_list    = [text for (text, idx) in items]
+        self.vocab_size_base      = vocab_size
+        self.vocab_size           = self.vocab_size_base + len(self.added_tokens_list)
+        self.fname_tokenizer      = fname_tokenizer
+
+    def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()}
+
+        for i, _ in enumerate(self.vocab):
+            yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL
+
+    def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        for text in self.added_tokens_list:
+            score = -1000.0
+            yield text.encode("utf-8"), score, gguf.TokenType.CONTROL
+
+    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        yield from self.bpe_tokens()
+        yield from self.added_tokens()
+
+    def __repr__(self) -> str:
+        return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
+
+
+class SentencePieceVocab(Vocab):
+    tokenizer_model = "llama"
+    name = "spm"
+
+    def __init__(self, base_path: Path):
+        added_tokens: dict[str, int] = {}
+        if (fname_tokenizer := base_path / 'tokenizer.model').exists():
+            # normal location
+            try:
+                with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f:
+                    added_tokens = json.load(f)
+            except FileNotFoundError:
+                pass
+        elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists():
+            # not found in alternate location either
+            raise FileNotFoundError('Cannot find tokenizer.model')
+
+        self.sentencepiece_tokenizer = SentencePieceProcessor()
+        self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer))
+        vocab_size = self.sentencepiece_tokenizer.vocab_size()
+
+        new_tokens       = {id: piece for piece, id in added_tokens.items() if id >= vocab_size}
+        expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens)))
+        actual_new_ids   = sorted(new_tokens.keys())
+
+        if expected_new_ids != actual_new_ids:
+            raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}")
+
+        # Token pieces that were added to the base vocabulary.
+        self.added_tokens_dict  = added_tokens
+        self.added_tokens_list  = [new_tokens[id] for id in actual_new_ids]
+        self.vocab_size_base    = vocab_size
+        self.vocab_size         = self.vocab_size_base + len(self.added_tokens_list)
+        self.fname_tokenizer    = fname_tokenizer
+
+    def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        tokenizer = self.sentencepiece_tokenizer
+        for i in range(tokenizer.vocab_size()):
+            piece = tokenizer.IdToPiece(i)
+            text         = piece.encode("utf-8")
+            score: float = tokenizer.GetScore(i)
+
+            toktype = gguf.TokenType.NORMAL
+            if tokenizer.IsUnknown(i):
+                toktype = gguf.TokenType.UNKNOWN
+            if tokenizer.IsControl(i):
+                toktype = gguf.TokenType.CONTROL
+
+            # NOTE: I think added_tokens are user defined.
+            # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
+            # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED
+
+            if tokenizer.IsUnused(i):
+                toktype = gguf.TokenType.UNUSED
+            if tokenizer.IsByte(i):
+                toktype = gguf.TokenType.BYTE
+
+            yield text, score, toktype
+
+    def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        for text in self.added_tokens_list:
+            score = -1000.0
+            yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED
+
+    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        yield from self.sentencepiece_tokens()
+        yield from self.added_tokens()
+
+    def __repr__(self) -> str:
+        return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
+
+
+class LlamaHfVocab(Vocab):
+    tokenizer_model = "llama"
+    name = "hfft"
+
+    def __init__(self, base_path: Path):
+        fname_tokenizer = base_path / FAST_TOKENIZER_FILE
+        # if this fails, FileNotFoundError propagates to caller
+        with open(fname_tokenizer, encoding='utf-8') as f:
+            tokenizer_json = json.load(f)
+
+        # pre-check so we know if we need transformers
+        tokenizer_model: dict[str, Any] = tokenizer_json['model']
+        is_llama3 = (
+            tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False)
+            and not tokenizer_model.get('byte_fallback', True)
+        )
+        if is_llama3:
+            raise TypeError('Llama 3 must be converted with BpeVocab')
+
+        if not is_llama3 and (
+            tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
+            or tokenizer_json['decoder']['type'] != 'Sequence'
+        ):
+            raise FileNotFoundError('Cannot find Llama BPE tokenizer')
+
+        try:
+            from transformers import AutoTokenizer
+        except ImportError as e:
+            raise ImportError(
+                "To use LlamaHfVocab, please install the `transformers` package. "
+                "You can install it with `pip install transformers`."
+            ) from e
+
+        # Allow the tokenizer to default to slow or fast versions.
+        # Explicitly set tokenizer to use local paths.
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            base_path,
+            cache_dir=base_path,
+            local_files_only=True,
+        )
+        assert self.tokenizer.is_fast  # assume tokenizer.json is used
+
+        # Initialize lists and dictionaries for added tokens
+        self.added_tokens_list = []
+        self.added_tokens_dict = dict()
+        self.added_tokens_ids  = set()
+
+        # Process added tokens
+        for tok, tokidx in sorted(
+            self.tokenizer.get_added_vocab().items(), key=lambda x: x[1]
+        ):
+            # Only consider added tokens that are not in the base vocabulary
+            if tokidx >= self.tokenizer.vocab_size:
+                self.added_tokens_list.append(tok)
+                self.added_tokens_dict[tok] = tokidx
+                self.added_tokens_ids.add(tokidx)
+
+        # Store special tokens and their IDs
+        self.specials = {
+            tok: self.tokenizer.get_vocab()[tok]
+            for tok in self.tokenizer.all_special_tokens
+        }
+        self.special_ids = set(self.tokenizer.all_special_ids)
+
+        # Set vocabulary sizes
+        self.vocab_size_base = self.tokenizer.vocab_size
+        self.vocab_size      = self.vocab_size_base + len(self.added_tokens_list)
+
+        self.fname_tokenizer = fname_tokenizer
+
+    def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        reverse_vocab = {
+            id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()
+        }
+
+        for token_id in range(self.vocab_size_base):
+            # Skip processing added tokens here
+            if token_id in self.added_tokens_ids:
+                continue
+
+            # Convert token text to bytes
+            token_text = reverse_vocab[token_id].encode("utf-8")
+
+            # Yield token text, score, and type
+            yield token_text, self.get_token_score(token_id), self.get_token_type(
+                token_id, token_text, self.special_ids  # Reuse already stored special IDs
+            )
+
+    def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType:
+        # Special case for byte tokens
+        if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
+            return gguf.TokenType.BYTE
+
+        # Determine token type based on whether it's a special token
+        return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL
+
+    def get_token_score(self, token_id: int) -> float:
+        # Placeholder for actual logic to determine the token's score
+        # This needs to be implemented based on specific requirements
+        return -1000.0  # Default score
+
+    def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        for text in self.added_tokens_list:
+            if text in self.specials:
+                toktype = self.get_token_type(self.specials[text], b'', self.special_ids)
+                score = self.get_token_score(self.specials[text])
+            else:
+                toktype = gguf.TokenType.USER_DEFINED
+                score = -1000.0
+
+            yield text.encode("utf-8"), score, toktype
+
+    def has_newline_token(self):
+        return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab
+
+    def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
+        yield from self.hf_tokens()
+        yield from self.added_tokens()
+
+    def __repr__(self) -> str:
+        return f"<LlamaHfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
+
+
 #
 # data loading
 # TODO: reuse (probably move to gguf.py?)
@@ -864,8 +1162,8 @@ class OutputFile:
             self.gguf.add_rope_scaling_type(params.rope_scaling_type)
             self.gguf.add_rope_scaling_factor(params.f_rope_scale)
 
-        if params.n_ctx_orig is not None:
-            self.gguf.add_rope_scaling_orig_ctx_len(params.n_ctx_orig)
+        if params.n_orig_ctx is not None:
+            self.gguf.add_rope_scaling_orig_ctx_len(params.n_orig_ctx)
 
         if params.rope_finetuned is not None:
             self.gguf.add_rope_scaling_finetuned(params.rope_finetuned)

docs/HOWTO-add-model.md

@@ -17,7 +17,7 @@ Also, it is important to check that the examples and main ggml backends (CUDA, M
 ### 1. Convert the model to GGUF
 
 This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library.
-Depending on the model architecture, you can use either [convert-hf-to-gguf.py](../convert-hf-to-gguf.py) or [examples/convert-legacy-llama.py](../examples/convert-legacy-llama.py) (for `llama/llama2` models in `.pth` format).
+Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py).
 
 The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.
 

examples/CMakeLists.txt

@@ -15,6 +15,7 @@ else()
     add_subdirectory(baby-llama)
     add_subdirectory(batched)
     add_subdirectory(batched-bench)
+    add_subdirectory(beam-search)
     add_subdirectory(benchmark)
     add_subdirectory(convert-llama2c-to-ggml)
     add_subdirectory(embedding)

examples/alpaca.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+#
+# Temporary script - will be removed in the future
+#
+
+cd `dirname $0`
+cd ..
+
+./main -m ./models/alpaca.13b.ggmlv3.q8_0.bin \
+       --color \
+       -f ./prompts/alpaca.txt \
+       --ctx_size 2048 \
+       -n -1 \
+       -ins -b 256 \
+       --top_k 10000 \
+       --temp 0.2 \
+       --repeat_penalty 1.1 \
+       -t 7

examples/baby-llama/baby-llama.cpp

@@ -522,8 +522,8 @@ static struct ggml_tensor * forward(
             // wk   shape [n_embd, n_embd, 1, 1]
             // Qcur shape [n_embd/n_head, n_head, N, 1]
             // Kcur shape [n_embd/n_head, n_head, N, 1]
-            struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0);
-            struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0);
+            struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, 0);
+            struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, 0);
 
             // store key and value to memory
             {
@@ -759,8 +759,8 @@ static struct ggml_tensor * forward_batch(
             // wk   shape [n_embd, n_embd, 1, 1]
             // Qcur shape [n_embd/n_head, n_head, N, n_batch]
             // Kcur shape [n_embd/n_head, n_head, N, n_batch]
-            struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0);
-            struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0);
+            struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0, 0);
+            struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0, 0);
             assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);
             assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);
 
@@ -1056,7 +1056,7 @@ static struct ggml_tensor * forward_lora(
                                                         model->layers[il].wqb,
                                                         cur)),
                                                 n_embd/n_head, n_head, N),
-                                            KQ_pos, n_rot, 0);
+                                            KQ_pos, n_rot, 0, 0);
             struct ggml_tensor * Kcur = ggml_rope(ctx0,
                                             ggml_reshape_3d(ctx0,
                                                 ggml_mul_mat(ctx0,
@@ -1065,7 +1065,7 @@ static struct ggml_tensor * forward_lora(
                                                         model->layers[il].wkb,
                                                         cur)),
                                                 n_embd/n_head, n_head, N),
-                                            KQ_pos, n_rot, 0);
+                                            KQ_pos, n_rot, 0, 0);
 
             // store key and value to memory
             {

examples/batched-bench/README.md

@@ -10,16 +10,16 @@ There are 2 modes of operation:
 - `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`)
 
 ```bash
-./batched-bench -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]
+./batched-bench MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>
 
 # LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared
-./batched-bench -m ./models/llama-7b/ggml-model-f16.gguf -c 16384 -b 2048 -ub 512 -ngl 99
+./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 2048 512 0 99
 
 # LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared
-./batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 16384 -b 2048 -ub 512 -ngl 99 -pps
+./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 2048 512 1 99
 
 # custom set of batches
-./batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 2048 -b 512 -ub 512 -ngl 999 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32
+./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 512 512 0 999 0 128,256,512 128,256 1,2,4,8,16,32
 ```
 
 ## Sample results

examples/batched-bench/batched-bench.cpp

@@ -28,27 +28,67 @@ static std::vector<int> parse_list(char * p) {
     return ret;
 }
 
-static void print_usage(int argc, char ** argv, const gpt_params & params) {
-    gpt_params_print_usage(argc, argv, params);
-
-    LOG_TEE("\nexample usage:\n");
-    LOG_TEE("\n    %s -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]\n", argv[0]);
-    LOG_TEE("\n");
-}
-
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    if (!gpt_params_parse(argc, argv, params)) {
-        print_usage(argc, argv, params);
-        return 1;
+    if (argc == 1 || argv[1][0] == '-') {
+        printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [FATTN] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
+        printf("  <PP>, <TG> and PL are comma-separated lists of numbers without spaces\n\n");
+        printf("  example: %s ggml-model-f16.gguf 2048 2048 512 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
+        return 1 ;
     }
 
-    int is_pp_shared = params.is_pp_shared;
+    int n_kv_max     = 2048;
+    int n_batch      = 2048;
+    int n_ubatch     = 512;
+    bool flash_attn  = false;
+    int is_pp_shared = 0;
+    int n_gpu_layers = 0;
 
-    std::vector<int> n_pp = params.n_pp;
-    std::vector<int> n_tg = params.n_tg;
-    std::vector<int> n_pl = params.n_pl;
+    std::vector<int> n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, };
+    std::vector<int> n_tg = { 128, 256, };
+    std::vector<int> n_pl = { 1, 2, 4, 8, 16, 32, };
+    //std::vector<int> n_pl = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 32, };
+
+    if (argc >= 2) {
+        params.model = argv[1];
+    }
+
+    if (argc >= 3) {
+        n_kv_max = std::atoi(argv[2]);
+    }
+
+    if (argc >= 4) {
+        n_batch = std::atoi(argv[3]);
+    }
+
+    if (argc >= 5) {
+        n_ubatch = std::atoi(argv[4]);
+    }
+
+    if (argc >= 6) {
+        flash_attn = std::atoi(argv[5]);
+    }
+
+    if (argc >= 7) {
+        is_pp_shared = std::atoi(argv[6]);
+    }
+
+    if (argc >= 8) {
+        n_gpu_layers = std::atoi(argv[7]);
+    }
+
+    if (argc >= 9) {
+        n_pp = parse_list(argv[8]);
+    }
+
+    if (argc >= 10) {
+        n_tg = parse_list(argv[9]);
+    }
+
+    if (argc >= 11) {
+        n_pl = parse_list(argv[10]);
+    }
 
     // init LLM
 
@@ -57,7 +97,12 @@ int main(int argc, char ** argv) {
 
     // initialize the model
 
-    llama_model_params model_params = llama_model_params_from_gpt_params(params);
+    llama_model_params model_params = llama_model_default_params();
+
+    const std::vector<float> t_split(llama_max_devices(), 0.0f);
+
+    model_params.n_gpu_layers = n_gpu_layers;
+    model_params.tensor_split = t_split.data();
 
     llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
 
@@ -66,7 +111,16 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
+    llama_context_params ctx_params = llama_context_default_params();
+
+    ctx_params.seed       = 1234;
+    ctx_params.n_ctx      = n_kv_max;
+    ctx_params.n_batch    = n_batch;
+    ctx_params.n_ubatch   = n_ubatch;
+    ctx_params.flash_attn = flash_attn;
+
+    ctx_params.n_threads       = params.n_threads;
+    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 
     // ensure enough sequences are available
     ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end());
@@ -78,8 +132,6 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    const int32_t n_kv_max = llama_n_ctx(ctx);
-
     llama_batch batch = llama_batch_init(n_kv_max, 0, 1);
 
     // decode in batches of ctx_params.n_batch tokens
@@ -123,7 +175,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
+    LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, flash_attn, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
     LOG_TEE("\n");
 
     LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP",     "TG",     "B",    "N_KV",     "T_PP s",   "S_PP t/s", "T_TG s",   "S_TG t/s", "T s",      "S t/s");

examples/batched/README.md

@@ -3,7 +3,7 @@
 The example demonstrates batched generation from a given prompt
 
 ```bash
-./batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4
+./batched ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" 4
 
 ...
 

examples/batched/batched.cpp

@@ -7,31 +7,48 @@
 #include <string>
 #include <vector>
 
-static void print_usage(int argc, char ** argv, const gpt_params & params) {
-    gpt_params_print_usage(argc, argv, params);
-
-    LOG_TEE("\nexample usage:\n");
-    LOG_TEE("\n    %s -m model.gguf -p \"Hello my name is\" -n 32 -np 4\n", argv[0]);
-    LOG_TEE("\n");
-}
-
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    params.prompt = "Hello my name is";
-    params.n_predict = 32;
-
-    if (!gpt_params_parse(argc, argv, params)) {
-        print_usage(argc, argv, params);
-        return 1;
+    if (argc == 1 || argv[1][0] == '-') {
+        printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL] [LEN] [NGL]\n" , argv[0]);
+        return 1 ;
     }
 
-
     // number of parallel batches
-    int n_parallel = params.n_parallel;
+    int n_parallel = 1;
 
     // total length of the sequences including the prompt
-    int n_predict = 32;
+    int n_len = 32;
+
+    // number of layers to offload to the GPU
+    int n_gpu_layers = 0;
+
+    if (argc >= 2) {
+        params.model = argv[1];
+    }
+
+    if (argc >= 3) {
+        params.prompt = argv[2];
+    }
+
+    if (argc >= 4) {
+        n_parallel = std::atoi(argv[3]);
+    }
+
+    if (argc >= 5) {
+        n_len = std::atoi(argv[4]);
+    }
+
+    if (argc >= 6) {
+        n_gpu_layers = std::atoi(argv[5]);
+    }
+
+    if (params.prompt.empty()) {
+        params.prompt = "Hello my name is";
+    }
+
+    string_process_escapes(params.prompt);
 
     // init LLM
 
@@ -40,7 +57,9 @@ int main(int argc, char ** argv) {
 
     // initialize the model
 
-    llama_model_params model_params = llama_model_params_from_gpt_params(params);
+    llama_model_params model_params = llama_model_default_params();
+
+    model_params.n_gpu_layers = n_gpu_layers;
 
     llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
 
@@ -54,14 +73,18 @@ int main(int argc, char ** argv) {
     std::vector<llama_token> tokens_list;
     tokens_list = ::llama_tokenize(model, params.prompt, true);
 
-    const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel;
+    const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
 
     // initialize the context
 
-    llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
+    llama_context_params ctx_params = llama_context_default_params();
 
+    ctx_params.seed  = 1234;
     ctx_params.n_ctx   = n_kv_req;
-    ctx_params.n_batch = std::max(n_predict, n_parallel);
+    ctx_params.n_batch = std::max(n_len, n_parallel);
+    ctx_params.n_seq_max       = n_parallel;
+    ctx_params.n_threads       = params.n_threads;
+    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
@@ -70,9 +93,9 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    const int n_ctx = llama_n_ctx(ctx);
+    const int n_ctx    = llama_n_ctx(ctx);
 
-    LOG_TEE("\n%s: n_predict = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
+    LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
 
     // make sure the KV cache is big enough to hold all the prompt and generated tokens
     if (n_kv_req > n_ctx) {
@@ -133,7 +156,7 @@ int main(int argc, char ** argv) {
 
     const auto t_main_start = ggml_time_us();
 
-    while (n_cur <= n_predict) {
+    while (n_cur <= n_len) {
         // prepare the next batch
         llama_batch_clear(batch);
 
@@ -169,7 +192,7 @@ int main(int argc, char ** argv) {
             //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
             // is it an end of generation? -> mark the stream as finished
-            if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
+            if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
                 i_batch[i] = -1;
                 LOG_TEE("\n");
                 if (n_parallel > 1) {

examples/beam-search/CMakeLists.txt

@@ -0,0 +1,5 @@
+set(TARGET beam-search)
+add_executable(${TARGET} beam-search.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/beam-search/beam-search.cpp

@@ -0,0 +1,188 @@
+#include "common.h"
+#include "llama.h"
+
+#include <cassert>
+#include <cinttypes>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <fstream>
+#include <iostream>
+#include <string>
+#include <vector>
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+#include <signal.h>
+#include <unistd.h>
+#elif defined (_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+#   define NOMINMAX
+#endif
+#include <windows.h>
+#include <signal.h>
+#endif
+
+// Used for debugging to print out beam tokens.
+struct ostream_beam_view {
+    llama_context * ctx;
+    llama_beam_view beam_view;
+};
+
+static std::ostream & operator<<(std::ostream & os, const ostream_beam_view & obv) {
+    os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens(";
+    for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) {
+        os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]);
+    }
+    return os << ')';
+}
+
+// Put here anything you want back in beam_search_callback().
+struct beam_search_callback_data {
+    llama_context * ctx;
+    std::vector<llama_token> response;
+};
+
+// In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.
+// For example, eob can be flagged due to maximum token length, stop words, etc.
+static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) {
+    return n_tokens && llama_token_is_eog(llama_get_model(callback_data.ctx), tokens[n_tokens-1]);
+}
+
+// Function matching type llama_beam_search_callback_fn_t.
+// Custom callback example is called each time the beams lengths increase:
+//  * Show progress by printing ',' following by number of convergent beam tokens if any.
+//  * When all beams converge to a common prefix, they are made available in beams_state.beams[0].
+//    This is also called when the stop condition is met.
+//    Collect tokens into std::vector<llama_token> response which is pointed to by callback_data.
+static void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_state) {
+    auto& callback_data = *static_cast<beam_search_callback_data*>(callback_data_ptr);
+    // Mark beams as EOS as needed.
+    for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
+        llama_beam_view& beam_view = beams_state.beam_views[i];
+        if (!beam_view.eob && is_at_eob(callback_data, beam_view.tokens, beam_view.n_tokens)) {
+            beam_view.eob = true;
+        }
+    }
+    printf(",");  // Show progress
+    if (const size_t n = beams_state.common_prefix_length) {
+        callback_data.response.resize(callback_data.response.size() + n);
+        assert(0u < beams_state.n_beams);
+        const llama_token * tokens = beams_state.beam_views[0].tokens;
+        std::copy(tokens, tokens + n, callback_data.response.end() - n);
+        printf("%zu", n);
+    }
+    fflush(stdout);
+#if 1 // DEBUG: print current beams for this iteration
+    std::cout << "\n\nCurrent beams (last_call=" << beams_state.last_call << "):\n";
+    for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
+        std::cout << "beams["<<i<<"]: " << ostream_beam_view{callback_data.ctx,beams_state.beam_views[i]} << std::endl;
+    }
+#endif
+}
+
+int main(int argc, char ** argv)
+{
+    gpt_params params;
+    //params.n_gpu_layers = 200;
+
+    //---------------------------------
+    // Print help :
+    //---------------------------------
+
+    if ( argc < 2 || argv[1][0] == '-' )
+    {
+        printf( "Usage: %s MODEL_PATH [BEAM_WIDTH=2] [PROMPT]\n" , argv[0] );
+        return 1 ;
+    }
+
+    //---------------------------------
+    // Load parameters :
+    //---------------------------------
+
+    params.model = argv[1];
+
+    params.n_beams = 2 < argc ? std::stoi(argv[2]) : 2;
+
+    if ( argc > 3 )
+    {
+        params.prompt = argv[3];
+    }
+
+    if ( params.prompt.empty() )
+    {
+        params.prompt = "### Request:\nHow many countries are there?\n\n### Response:\n";
+    }
+
+    //---------------------------------
+    // Init LLM :
+    //---------------------------------
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model;
+    llama_context * ctx;
+
+    std::tie(model, ctx) = llama_init_from_gpt_params( params );
+
+    if ( model == NULL )
+    {
+        fprintf( stderr , "%s: error: unable to load model\n" , __func__ );
+        return 1;
+    }
+
+    //---------------------------------
+    // Tokenize the prompt :
+    //---------------------------------
+
+    std::vector<llama_token> tokens_list = llama_tokenize(ctx, params.prompt, true);
+
+    const size_t max_context_size     = llama_n_ctx( ctx );
+    const size_t max_tokens_list_size = max_context_size - 4 ;
+
+    if (tokens_list.size() > max_tokens_list_size)
+    {
+        fprintf( stderr , "%s: error: prompt too long (%zu tokens, max %zu)\n" ,
+             __func__ , tokens_list.size() , max_tokens_list_size );
+        return 1;
+    }
+
+    fprintf( stderr, "\n\n" );
+
+    // Print the tokens from the prompt :
+
+    for( auto id : tokens_list )
+    {
+        std::cout << llama_token_to_piece(ctx, id);
+    }
+    std::cout << std::flush;
+
+    int n_past = 0;
+
+    if (llama_decode(ctx, llama_batch_get_one(tokens_list.data(), tokens_list.size(), n_past, 0)))
+    {
+        fprintf(stderr, "%s : failed to eval prompt.\n" , __func__ );
+        return 1;
+    }
+    n_past += tokens_list.size();
+
+    beam_search_callback_data callback_data{ctx, {}};
+    size_t const beam_width = static_cast<size_t>(params.n_beams);
+    int const n_predict = 256;
+    llama_beam_search(ctx, beam_search_callback, &callback_data, beam_width, n_past, n_predict);
+
+    std::cout << "\n\n";
+    for (llama_token const token_id : callback_data.response) {
+        std::cout << llama_token_to_piece(ctx,token_id);
+    }
+    std::cout << std::endl;
+
+    llama_free( ctx );
+    llama_free_model( model );
+
+    llama_backend_free();
+
+    return 0;
+}

examples/embedding/embedding.cpp

@@ -63,7 +63,6 @@ int main(int argc, char ** argv) {
     gpt_params params;
 
     if (!gpt_params_parse(argc, argv, params)) {
-        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
@@ -80,6 +79,9 @@ int main(int argc, char ** argv) {
     fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
 
     std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = string_random_prompt(rng);
+    }
 
     llama_backend_init();
     llama_numa_init(params.numa);

examples/eval-callback/eval-callback.cpp

@@ -140,18 +140,20 @@ static bool run(llama_context * ctx, const gpt_params & params) {
 }
 
 int main(int argc, char ** argv) {
+
     callback_data cb_data;
 
     gpt_params params;
-
     if (!gpt_params_parse(argc, argv, params)) {
-        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
     print_build_info();
 
     std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = string_random_prompt(rng);
+    }
 
     llama_backend_init();
     llama_numa_init(params.numa);

examples/finetune/finetune.cpp

@@ -564,7 +564,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
         const int rope_mode = 0;
 
         return ggml_rope_ext(ctx,
-            t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx,
+            t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx, 0,
             rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f
         );
     };

examples/gguf-split/gguf-split.cpp

@@ -61,10 +61,10 @@ static size_t split_str_to_n_bytes(std::string str) {
     int n;
     if (str.back() == 'M') {
         sscanf(str.c_str(), "%d", &n);
-        n_bytes = (size_t)n * 1000 * 1000; // megabytes
+        n_bytes = (size_t)n * 1024 * 1024; // megabytes
     } else if (str.back() == 'G') {
         sscanf(str.c_str(), "%d", &n);
-        n_bytes = (size_t)n * 1000 * 1000 * 1000; // gigabytes
+        n_bytes = (size_t)n * 1024 * 1024 * 1024; // gigabytes
     } else {
         throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back()));
     }
@@ -284,7 +284,7 @@ struct split_strategy {
                 struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_out, i));
                 total_size += ggml_nbytes(t);
             }
-            total_size = total_size / 1000 / 1000; // convert to megabytes
+            total_size = total_size / 1024 / 1024; // convert to megabytes
             printf("split %05d: n_tensors = %d, total_size = %ldM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
             i_split++;
         }

examples/gguf-split/tests.sh

@@ -41,7 +41,7 @@ echo PASS
 echo
 
 # 2b. Test the sharded model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --n-predict 32
+$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --random-prompt --n-predict 32
 echo PASS
 echo
 
@@ -51,7 +51,7 @@ echo PASS
 echo
 
 # 3b. Test the merged model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-merge.gguf --n-predict 32
+$MAIN --model $WORK_PATH/ggml-model-merge.gguf --random-prompt --n-predict 32
 echo PASS
 echo
 
@@ -61,7 +61,7 @@ echo PASS
 echo
 
 # 4b. Test the sharded model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --n-predict 32
+$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --random-prompt --n-predict 32
 echo PASS
 echo
 
@@ -71,7 +71,7 @@ echo
 #echo
 
 # 5b. Test the merged model is loading properly
-#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --n-predict 32
+#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --random-prompt --n-predict 32
 #echo PASS
 #echo
 
@@ -81,7 +81,7 @@ echo PASS
 echo
 
 # 6b. Test the sharded model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --n-predict 32
+$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --random-prompt --n-predict 32
 echo PASS
 echo
 

examples/gpt4all.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+
+#
+# Temporary script - will be removed in the future
+#
+
+cd `dirname $0`
+cd ..
+
+./main --color --instruct --threads 4 \
+       --model ./models/gpt4all-7B/gpt4all-lora-quantized.bin \
+       --file ./prompts/alpaca.txt \
+       --batch_size 8 --ctx_size 2048 -n -1 \
+       --repeat_last_n 64 --repeat_penalty 1.3 \
+       --n_predict 128 --temp 0.1 --top_k 40 --top_p 0.95

examples/gritlm/gritlm.cpp

@@ -153,9 +153,7 @@ static std::string gritlm_instruction(const std::string & instruction) {
 
 int main(int argc, char * argv[]) {
     gpt_params params;
-
     if (!gpt_params_parse(argc, argv, params)) {
-        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 

examples/imatrix/README.md

@@ -6,19 +6,16 @@ More information is available here: https://github.com/ggerganov/llama.cpp/pull/
 ## Usage
 
 ```
-./imatrix \
-    -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \
-    [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \
-    [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]
+./imatrix -m <some_fp_model> -f <some_training_data> [-o <output_file>] [--verbosity <verbosity_level>]
+        [-ofreq num_chunks] [-ow <0 or 1>] [other common params]
 ```
 
 Here `-m` with a model name and `-f` with a file containing training data (such as e.g. `wiki.train.raw`) are mandatory.
 The parameters in square brackets are optional and have the following meaning:
 * `-o` (or `--output-file`) specifies the name of the file where the computed data will be stored. If missing `imatrix.dat` is used.
 * `--verbosity` specifies the verbosity level. If set to `0`, no output other than the perplexity of the processed chunks will be generated. If set to `1`, each time the results are saved a message is written to `stderr`. If `>=2`, a message is output each time data is collected for any tensor. Default verbosity level is `1`.
-* `--output-frequency` specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)
-* `--save-frequency` specifies how often to save a copy of the imatrix in a separate file. Default is 0 (i.e., never)
-* `--process-output` specifies if data will be collected for the `output.weight` tensor. My experience is that it is better to not utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default.
+* `-ofreq` (or `--output-frequency`) specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)
+* `-ow` (or `--output-weight`) specifies if data will be collected for the `output.weight` tensor. My experience is that it is better to not utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default.
 
 For faster computation, make sure to use GPU offloading via the `-ngl` argument
 

examples/imatrix/imatrix.cpp

@@ -17,37 +17,39 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-static void print_usage(int argc, char ** argv, const gpt_params & params) {
-    gpt_params_print_usage(argc, argv, params);
-
-    LOG_TEE("\nexample usage:\n");
-    LOG_TEE("\n    %s \\\n"
-            "       -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \\\n"
-            "       [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n"
-            "       [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]\n" , argv[0]);
-    LOG_TEE("\n");
-}
-
 struct Stats {
     std::vector<float> values;
     std::vector<int> counts;
     int ncall = 0;
 };
 
+struct StatParams {
+    std::string dataset;
+    std::string ofile = "imatrix.dat";
+    int         n_output_frequency = 10;
+    int         verbosity = 1;
+    int         keep_every = 0;
+    bool        collect_output_weight = false;
+};
+
 class IMatrixCollector {
 public:
     IMatrixCollector() = default;
-    void set_params(gpt_params params) { m_params = std::move(params); }
+    void set_parameters(StatParams&& params) { m_params = std::move(params); }
     bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
-    void save_imatrix(int ncall = -1) const;
-    bool load_imatrix(const char * file_name);
+    void save_imatrix() const;
+    bool load_imatrix(const char * file_name, bool add);
+    static bool load_imatrix(const char * file_name, std::unordered_map<std::string, Stats>& imatrix);
 private:
     std::unordered_map<std::string, Stats> m_stats;
-    gpt_params                             m_params;
+    StatParams                             m_params;
     std::mutex                             m_mutex;
     int                                    m_last_call = 0;
     std::vector<float>                     m_src1_data;
     std::vector<char>                      m_ids; // the expert ids from ggml_mul_mat_id
+                                                  //
+    void save_imatrix(const char * file_name, const char * dataset) const;
+    void keep_imatrix(int ncall) const;
 };
 
 // remove any prefix and suffixes from the name
@@ -83,7 +85,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
         if (t->op != GGML_OP_MUL_MAT) return false;
         // why are small batches ignored (<16 tokens)?
         if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
-        if (!(wname.substr(0, 4) == "blk." || (m_params.process_output && wname == "output.weight"))) return false;
+        if (!(wname.substr(0, 4) == "blk." || (m_params.collect_output_weight && wname == "output.weight"))) return false;
         return true;
     }
 
@@ -151,25 +153,21 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
                     for (int j = 0; j < (int)src1->ne[0]; ++j) {
                         e.values[e_start + j] += x[j]*x[j];
                         e.counts[e_start + j]++;
-                        if (!std::isfinite(e.values[e_start + j])) {
-                            fprintf(stderr, "%f detected in %s\n", e.values[e_start + j], wname.c_str());
-                            exit(1);
-                        }
                     }
                 }
             }
             if (e.ncall > m_last_call) {
                 m_last_call = e.ncall;
-                if (m_last_call % m_params.n_out_freq == 0) {
+                if (m_last_call % m_params.n_output_frequency == 0) {
                     save_imatrix();
                 }
-                if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
-                    save_imatrix(m_last_call);
+                if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) {
+                    keep_imatrix(m_last_call);
                 }
             }
         }
     } else {
-        auto & e = m_stats[wname];
+        auto& e = m_stats[wname];
         if (e.values.empty()) {
             e.values.resize(src1->ne[0], 0);
             e.counts.resize(src1->ne[0], 0);
@@ -187,19 +185,15 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
             for (int j = 0; j < (int)src1->ne[0]; ++j) {
                 e.values[j] += x[j]*x[j];
                 e.counts[j]++;
-                if (!std::isfinite(e.values[j])) {
-                    fprintf(stderr, "%f detected in %s\n", e.values[j], wname.c_str());
-                    exit(1);
-                }
             }
         }
         if (e.ncall > m_last_call) {
             m_last_call = e.ncall;
-            if (m_last_call % m_params.n_out_freq == 0) {
+            if (m_last_call % m_params.n_output_frequency == 0) {
                 save_imatrix();
             }
-            if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
-                save_imatrix(m_last_call);
+            if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) {
+                keep_imatrix(m_last_call);
             }
         }
     }
@@ -207,75 +201,33 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
     return true;
 }
 
-void IMatrixCollector::save_imatrix(int ncall) const {
-    auto fname = m_params.out_file;
-    if (fname.empty()) {
-        fname = "imatrix.dat";
-    }
+void IMatrixCollector::save_imatrix() const {
+    save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str(), m_params.dataset.c_str());
+}
 
-    if (ncall > 0) {
-        fname += ".at_";
-        fname += std::to_string(ncall);
-    }
-
-    // avoid writing imatrix entries that do not have full data
-    // this can happen with MoE models where some of the experts end up not being exercised by the provided training data
-
-    int n_entries = 0;
-    std::vector<std::string> to_store;
-
-    bool is_first = true; // for printing
-    for (const auto & kv : m_stats) {
-        const int n_all = kv.second.counts.size();
-
-        if (n_all == 0) {
-            continue;
-        }
-
-        int n_zeros = 0;
-        for (const int c : kv.second.counts) {
-            if (c == 0) {
-                n_zeros++;
-            }
-        }
-
-        if (n_zeros != 0 && is_first) {
-            fprintf(stderr, "\n");
-            is_first = false;
-        }
-
-        if (n_zeros == n_all) {
-            fprintf(stderr, "%s: entry '%40s' has no data - skipping\n", __func__, kv.first.c_str());
-            continue;
-        }
-
-        if (n_zeros > 0) {
-            fprintf(stderr, "%s: entry '%40s' has partial data (%.2f%%) - skipping\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all);
-            continue;
-        }
-
-        n_entries++;
-        to_store.push_back(kv.first);
-    }
-
-    if (to_store.size() < m_stats.size()) {
-        fprintf(stderr, "%s: warning: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size());
-    }
+void IMatrixCollector::keep_imatrix(int ncall) const {
+    auto file_name = m_params.ofile;
+    if (file_name.empty()) file_name = "imatrix.dat";
+    file_name += ".at_";
+    file_name += std::to_string(ncall);
+    save_imatrix(file_name.c_str(), m_params.dataset.c_str());
+}
 
+void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) const {
     std::ofstream out(fname, std::ios::binary);
+    int n_entries = m_stats.size();
     out.write((const char *) &n_entries, sizeof(n_entries));
-    for (const auto & name : to_store) {
-        const auto & stat = m_stats.at(name);
-        int len = name.size();
+    for (const auto & p : m_stats) {
+        int len = p.first.size();
         out.write((const char *) &len, sizeof(len));
-        out.write(name.c_str(), len);
-        out.write((const char *) &stat.ncall, sizeof(stat.ncall));
-        int nval = stat.values.size();
+        out.write(p.first.c_str(), len);
+        out.write((const char *) &p.second.ncall, sizeof(p.second.ncall));
+        int nval = p.second.values.size();
         out.write((const char *) &nval, sizeof(nval));
         if (nval > 0) {
             std::vector<float> tmp(nval);
             for (int i = 0; i < nval; i++) {
-                tmp[i] = (stat.values[i] / static_cast<float>(stat.counts[i])) * static_cast<float>(stat.ncall);
+                tmp[i] = (p.second.values[i] / static_cast<float>(p.second.counts[i])) * static_cast<float>(p.second.ncall);
             }
             out.write((const char*)tmp.data(), nval*sizeof(float));
         }
@@ -284,28 +236,26 @@ void IMatrixCollector::save_imatrix(int ncall) const {
     // Write the number of call the matrix was computed with
     out.write((const char *) &m_last_call, sizeof(m_last_call));
 
-    // Write the input filename at the end of the file to later on specify it in quantize
-    {
-        int len = m_params.prompt_file.size();
-        out.write((const char *) &len, sizeof(len));
-        out.write(m_params.prompt_file.c_str(), len);
-    }
+    // Write the dataset name at the end of the file to later on specify it in quantize
+    int n_dataset = strlen(dataset);
+    out.write((const char *) &n_dataset, sizeof(n_dataset));
+    out.write(dataset, n_dataset);
 
     if (m_params.verbosity > 0) {
-        fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname.c_str());
+        fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname);
     }
 }
 
-bool IMatrixCollector::load_imatrix(const char * fname) {
-    std::ifstream in(fname, std::ios::binary);
+bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_map<std::string, Stats>& imatrix_data) {
+    std::ifstream in(imatrix_file, std::ios::binary);
     if (!in) {
-        printf("%s: failed to open %s\n",__func__, fname);
+        printf("%s: failed to open %s\n",__func__,imatrix_file);
         return false;
     }
     int n_entries;
     in.read((char*)&n_entries, sizeof(n_entries));
     if (in.fail() || n_entries < 1) {
-        printf("%s: no data in file %s\n", __func__, fname);
+        printf("%s: no data in file %s\n", __func__, imatrix_file);
         return false;
     }
     for (int i = 0; i < n_entries; ++i) {
@@ -313,22 +263,23 @@ bool IMatrixCollector::load_imatrix(const char * fname) {
         std::vector<char> name_as_vec(len+1);
         in.read((char *)name_as_vec.data(), len);
         if (in.fail()) {
-            printf("%s: failed reading name for entry %d from %s\n",__func__,i+1, fname);
+            printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file);
             return false;
         }
         name_as_vec[len] = 0;
         std::string name{name_as_vec.data()};
-        auto & e = m_stats[std::move(name)];
+        auto& e = imatrix_data[std::move(name)];
         int ncall;
         in.read((char*)&ncall, sizeof(ncall));
         int nval;
         in.read((char *)&nval, sizeof(nval));
         if (in.fail() || nval < 1) {
             printf("%s: failed reading number of values for entry %d\n",__func__,i);
-            m_stats = {};
+            imatrix_data = {};
             return false;
         }
 
+        // When re-called from load_imatrix() with add set, this will already be created.
         if (e.values.empty()) {
             e.values.resize(nval, 0);
             e.counts.resize(nval, 0);
@@ -338,7 +289,7 @@ bool IMatrixCollector::load_imatrix(const char * fname) {
         in.read((char*)tmp.data(), nval*sizeof(float));
         if (in.fail()) {
             printf("%s: failed reading data for entry %d\n",__func__,i);
-            m_stats = {};
+            imatrix_data = {};
             return false;
         }
 
@@ -353,6 +304,13 @@ bool IMatrixCollector::load_imatrix(const char * fname) {
     return true;
 }
 
+bool IMatrixCollector::load_imatrix(const char * file_name, bool add) {
+    if (!add) {
+        m_stats.clear();
+    }
+    return load_imatrix(file_name, m_stats);
+}
+
 static IMatrixCollector g_collector;
 
 static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
@@ -366,7 +324,7 @@ struct results_log_softmax {
     float  prob;
 };
 
-static std::vector<float> softmax(const std::vector<float> & logits) {
+static std::vector<float> softmax(const std::vector<float>& logits) {
     std::vector<float> probs(logits.size());
     float max_logit = logits[0];
     for (float v : logits) {
@@ -400,7 +358,8 @@ static results_log_softmax log_softmax(int n_vocab, const float * logits, int to
 
 static void process_logits(
     int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
-    double & nll, double & nll2, float * logit_history, float * prob_history) {
+    double & nll, double & nll2, float * logit_history, float * prob_history
+) {
     std::mutex mutex;
     int counter = 0;
     auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
@@ -432,7 +391,8 @@ static void process_logits(
     }
 }
 
-static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
+static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) {
+
     const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
     GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
     const int n_ctx = llama_n_ctx(ctx);
@@ -445,13 +405,13 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
     auto tim2 = std::chrono::high_resolution_clock::now();
     fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
 
-    if (params.i_chunk > 0) {
-        if (size_t((params.i_chunk + 2)*n_ctx) >= tokens.size()) {
-            fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, params.i_chunk);
+    if (from_chunk > 0) {
+        if (size_t((from_chunk + 2)*n_ctx) >= tokens.size()) {
+            fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, from_chunk);
             return false;
         }
-        fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, params.i_chunk, params.i_chunk*n_ctx);
-        tokens.erase(tokens.begin(), tokens.begin() + params.i_chunk*n_ctx);
+        fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, from_chunk, from_chunk*n_ctx);
+        tokens.erase(tokens.begin(), tokens.begin() + from_chunk*n_ctx);
     }
 
     if (int(tokens.size()) < 2*n_ctx) {
@@ -464,7 +424,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
     std::vector<float> logit_history;
     std::vector<float> prob_history;
 
-    if (params.compute_ppl) {
+    if (compute_ppl) {
         logit_history.resize(tokens.size());
         prob_history.resize(tokens.size());
     }
@@ -486,7 +446,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
     const int num_batches = (n_ctx + n_batch - 1) / n_batch;
 
     std::vector<float> logits;
-    if (params.compute_ppl && num_batches > 1) {
+    if (compute_ppl && num_batches > 1) {
         logits.reserve((size_t)n_ctx * n_vocab);
     }
 
@@ -522,7 +482,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
             // restore the original token in case it was set to BOS
             tokens[batch_start] = token_org;
 
-            if (params.compute_ppl && num_batches > 1) {
+            if (compute_ppl && num_batches > 1) {
                 const auto * batch_logits = llama_get_logits(ctx);
                 logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
             }
@@ -541,7 +501,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
             fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
         }
 
-        if (params.compute_ppl) {
+        if (compute_ppl) {
             const int first = n_ctx/2;
             const auto all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
             process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
@@ -556,7 +516,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
     }
     printf("\n");
 
-    if (params.compute_ppl) {
+    if (compute_ppl) {
         nll2 /= count;
         nll /= count;
         const double ppl = exp(nll);
@@ -573,32 +533,111 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
 }
 
 int main(int argc, char ** argv) {
-    gpt_params params;
 
-    params.n_ctx = 512;
-    params.logits_all = true;
-    params.verbosity = 1;
-
-    if (!gpt_params_parse(argc, argv, params)) {
-        print_usage(argc, argv, params);
-        return 1;
+    StatParams sparams;
+    std::string prev_result_file;
+    std::string combine_files;
+    bool compute_ppl = true;
+    int  from_chunk  = 0;
+    std::vector<char*> args;
+    args.push_back(argv[0]);
+    int iarg = 1;
+    for (; iarg < argc-1; ++iarg) {
+        std::string arg{argv[iarg]};
+        if (arg == "-o" || arg == "--output-file") {
+            sparams.ofile = argv[++iarg];
+        }
+        else if (arg == "-ofreq" || arg == "--output-frequency") {
+            sparams.n_output_frequency = std::stoi(argv[++iarg]);
+        }
+        else if (arg == "-ow" || arg == "--output-weight") {
+            sparams.collect_output_weight = std::stoi(argv[++iarg]);
+        }
+        else if (arg == "--verbosity") {
+            sparams.verbosity = std::stoi(argv[++iarg]);
+        } else if (arg == "--no-ppl") {
+            compute_ppl = false;
+        } else if (arg == "--keep-imatrix") {
+            sparams.keep_every = std::stoi(argv[++iarg]);
+        } else if (arg == "--continue-from") {
+            prev_result_file = argv[++iarg];
+        } else if (arg == "--combine") {
+            combine_files = argv[++iarg];
+        }
+        else if (arg == "--from-chunk") {
+            from_chunk = std::stoi(argv[++iarg]);
+        } else {
+            args.push_back(argv[iarg]);
+        }
     }
-
-    params.n_batch = std::min(params.n_batch, params.n_ctx);
-
-    g_collector.set_params(params);
-
-    for (const auto & in_file : params.in_files) {
-        printf("%s : loading imatrix from '%s'\n", __func__, in_file.c_str());
-        if (!g_collector.load_imatrix(in_file.c_str())) {
-            fprintf(stderr, "%s : failed to load %s\n", __func__, in_file.c_str());
-            return 1;
+    if (iarg < argc) {
+        std::string arg{argv[iarg]};
+        if (arg == "--no-ppl") {
+            compute_ppl = false;
+        } else {
+            args.push_back(argv[iarg]);
         }
     }
 
-    if (params.in_files.size() > 1) {
-        printf("%s : saving combined imatrix to '%s'\n", __func__, params.out_file.c_str());
+    gpt_params params;
+    params.n_batch = 512;
+    if (!gpt_params_parse(args.size(), args.data(), params)) {
+        return 1;
+    }
+
+    params.logits_all = true;
+    params.n_batch = std::min(params.n_batch, params.n_ctx);
+
+    print_build_info();
+
+    if (params.seed == LLAMA_DEFAULT_SEED) {
+        params.seed = time(NULL);
+    }
+
+    fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
+
+    std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = string_random_prompt(rng);
+    }
+
+    sparams.dataset = params.prompt_file;
+    g_collector.set_parameters(std::move(sparams));
+
+    if (!combine_files.empty()) {
+        std::vector<std::string> files;
+        size_t pos = 0;
+        while (true) {
+            auto new_pos = combine_files.find(',', pos);
+            if (new_pos != std::string::npos) {
+                files.emplace_back(combine_files.substr(pos, new_pos - pos));
+                pos = new_pos + 1;
+            } else {
+                files.emplace_back(combine_files.substr(pos));
+                break;
+            }
+        }
+        if (files.size() < 2) {
+            fprintf(stderr, "You must provide at least two comma separated files to use --combine\n");
+            return 1;
+        }
+        printf("Combining the following %d files\n", int(files.size()));
+        for (auto& file : files) {
+            printf("    %s\n", file.c_str());
+            if (!g_collector.load_imatrix(file.c_str(), true)) {
+                fprintf(stderr, "Failed to load %s\n", file.c_str());
+                return 1;
+            }
+        }
         g_collector.save_imatrix();
+        return 0;
+    }
+
+    if (!prev_result_file.empty()) {
+        if (!g_collector.load_imatrix(prev_result_file.c_str(), false)) {
+            fprintf(stderr, "=============== Failed to load %s\n", prev_result_file.c_str());
+            return 1;
+        }
     }
 
     llama_backend_init();
@@ -613,7 +652,6 @@ int main(int argc, char ** argv) {
     // init
     llama_model * model;
     llama_context * ctx;
-
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
     if (model == nullptr || ctx == nullptr) {
         fprintf(stderr, "%s : failed to init\n", __func__);
@@ -632,7 +670,8 @@ int main(int argc, char ** argv) {
         fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str());
     }
 
-    if (!compute_imatrix(ctx, params)) {
+    bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk);
+    if (!OK) {
         return 1;
     }
 

examples/infill/infill.cpp

@@ -107,7 +107,6 @@ int main(int argc, char ** argv) {
     g_params = &params;
 
     if (!gpt_params_parse(argc, argv, params)) {
-        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
@@ -140,6 +139,27 @@ int main(int argc, char ** argv) {
         LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
         params.n_ctx = 8;
     }
+    if (params.instruct) {
+        printf("\n************\n");
+        printf("%s: please use the 'main' tool for instruct mode\n", __func__);
+        printf("************\n\n");
+
+        return 0;
+    }
+    if (params.chatml) {
+        printf("\n************\n");
+        printf("%s: please use the 'main' tool for chatml mode\n", __func__);
+        printf("************\n\n");
+
+        return 0;
+    }
+    if (!params.antiprompt.empty()) {
+        printf("\n************\n");
+        printf("%s: please use the 'main' tool for antiprompt mode\n", __func__);
+        printf("************\n\n");
+
+        return 0;
+    }
     if (!params.interactive_first && (params.input_prefix.empty() && params.input_suffix.empty())) {
         printf("\n************\n");
         printf("%s: please use '--interactive_first' or specify '--in_prefix' and/or '--in_suffix'\n", __func__);
@@ -147,6 +167,20 @@ int main(int argc, char ** argv) {
 
         return 0;
     }
+    if (params.random_prompt) {
+        printf("\n************\n");
+        printf("%s: please use the 'main' tool for random prompt mode\n", __func__);
+        printf("************\n\n");
+
+        return 0;
+    }
+    if (!params.path_prompt_cache.empty()) {
+        printf("\n************\n");
+        printf("%s: infill does not support prompt caching\n", __func__);
+        printf("************\n\n");
+
+        return 0;
+    }
 
     if (params.rope_freq_base != 0.0) {
         LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base);
@@ -173,13 +207,17 @@ int main(int argc, char ** argv) {
 
     llama_model * model;
     llama_context * ctx;
-
+    llama_context * ctx_guidance = NULL;
     g_model = &model;
     g_ctx = &ctx;
 
     // load the model and apply lora adapter, if any
     LOG("%s: load the model and apply lora adapter, if any\n", __func__);
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    if (sparams.cfg_scale > 1.f) {
+        struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
+        ctx_guidance = llama_new_context_with_model(model, lparams);
+    }
 
     if (model == NULL) {
         LOG_TEE("%s: error: unable to load model\n", __func__);
@@ -235,6 +273,25 @@ int main(int argc, char ** argv) {
         LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
     }
 
+    // Tokenize negative prompt
+    std::vector<llama_token> guidance_inp;
+    int guidance_offset = 0;
+    int original_prompt_len = 0;
+    if (ctx_guidance) {
+        LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
+
+        guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, true);
+        LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
+
+        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, true);
+        LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
+
+        original_prompt_len = original_inp.size();
+        guidance_offset = (int)guidance_inp.size() - original_prompt_len;
+        LOG("original_prompt_len: %s", log_tostr(original_prompt_len));
+        LOG("guidance_offset:     %s", log_tostr(guidance_offset));
+    }
+
     if ((int) embd_inp.size() > n_ctx - 4) {
         LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
         return 1;
@@ -262,6 +319,15 @@ int main(int argc, char ** argv) {
             LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
         }
 
+        if (ctx_guidance) {
+            LOG_TEE("\n");
+            LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str());
+            LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
+            for (int i = 0; i < (int) guidance_inp.size(); i++) {
+                LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
+            }
+        }
+
         if (params.n_keep > 0) {
         LOG_TEE("%s: static prompt based on n_keep: '", __func__);
             for (int i = 0; i < params.n_keep; i++) {
@@ -329,11 +395,12 @@ int main(int argc, char ** argv) {
         is_interacting = params.interactive_first;
     }
 
-    bool input_echo = true;
+    bool input_echo           = true;
 
-    int n_past     = 0;
-    int n_remain   = params.n_predict;
-    int n_consumed = 0;
+    int n_past             = 0;
+    int n_remain           = params.n_predict;
+    int n_consumed         = 0;
+    int n_past_guidance    = 0;
 
     std::vector<int>   input_tokens;  g_input_tokens  = &input_tokens;
     std::vector<int>   output_tokens; g_output_tokens = &output_tokens;
@@ -343,6 +410,7 @@ int main(int argc, char ** argv) {
     console::set_display(console::prompt);
 
     std::vector<llama_token> embd;
+    std::vector<llama_token> embd_guidance;
 
     struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
 
@@ -368,7 +436,7 @@ int main(int argc, char ** argv) {
             // if we run out of context:
             // - take the n_keep first tokens from the original prompt (via n_past)
             // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
-            if (n_past + (int) embd.size() > n_ctx) {
+            if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
                 if (params.n_predict == -2) {
                     LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
                     break;
@@ -385,7 +453,11 @@ int main(int argc, char ** argv) {
 
                 n_past -= n_discard;
 
-                LOG("after swap: n_past = %d\n", n_past);
+                if (ctx_guidance) {
+                    n_past_guidance -= n_discard;
+                }
+
+                LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
 
                 LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());
 
@@ -393,6 +465,45 @@ int main(int argc, char ** argv) {
 
             // evaluate tokens in batches
             // embd is typically prepared beforehand to fit within a batch, but not always
+
+            if (ctx_guidance) {
+                int input_size = 0;
+                llama_token * input_buf = NULL;
+
+                if (n_past_guidance < (int) guidance_inp.size()) {
+                    // Guidance context should have the same data with these modifications:
+                    //
+                    // * Replace the initial prompt
+                    // * Shift everything by guidance_offset
+                    embd_guidance = guidance_inp;
+                    if (embd.begin() + original_prompt_len < embd.end()) {
+                        embd_guidance.insert(
+                            embd_guidance.end(),
+                            embd.begin() + original_prompt_len,
+                            embd.end()
+                        );
+                    }
+
+                    input_buf  = embd_guidance.data();
+                    input_size = embd_guidance.size();
+
+                    LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str());
+                } else {
+                    input_buf  = embd.data();
+                    input_size = embd.size();
+                }
+
+                for (int i = 0; i < input_size; i += params.n_batch) {
+                    int n_eval = std::min(input_size - i, params.n_batch);
+                    if (llama_decode(ctx_guidance, llama_batch_get_one(input_buf + i, n_eval, n_past_guidance, 0))) {
+                        LOG_TEE("%s : failed to eval\n", __func__);
+                        return 1;
+                    }
+
+                    n_past_guidance += n_eval;
+                }
+            }
+
             for (int i = 0; i < (int) embd.size(); i += params.n_batch) {
                 int n_eval = (int) embd.size() - i;
                 if (n_eval > params.n_batch) {
@@ -414,9 +525,11 @@ int main(int argc, char ** argv) {
         }
 
         embd.clear();
+        embd_guidance.clear();
 
         if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
-            const llama_token id = llama_sampling_sample(ctx_sampling, ctx, nullptr);
+
+            const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance);
 
             llama_sampling_accept(ctx_sampling, ctx, id, true);
 
@@ -470,6 +583,7 @@ int main(int argc, char ** argv) {
 
         // if not currently processing queued inputs;
         if ((int) embd_inp.size() <= n_consumed) {
+
             // deal with eot token in infill mode
             if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){
                 if (is_interacting && !params.interactive_first) {
@@ -530,6 +644,7 @@ int main(int argc, char ** argv) {
                 embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
                 embd_inp.push_back(llama_token_middle(model));
                 embd.clear();
+                embd_guidance.clear();
                 n_remain = params.n_predict;
                 n_past = 0;
                 n_consumed = 0;
@@ -636,6 +751,7 @@ int main(int argc, char ** argv) {
     llama_print_timings(ctx);
     write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
 
+    if (ctx_guidance) { llama_free(ctx_guidance); }
     llama_free(ctx);
     llama_free_model(model);
 

examples/json_schema_to_grammar.py

@@ -6,22 +6,52 @@ import re
 import sys
 from typing import Any, Dict, List, Set, Tuple, Union
 
-
-def _build_repetition(item_rule, min_items, max_items, separator_rule=None):
-
-    if min_items == 0 and max_items == 1:
-        return f'{item_rule}?'
-
+def _build_repetition(item_rule, min_items, max_items, separator_rule=None, item_rule_is_literal=False):
     if not separator_rule:
-        if min_items == 1 and max_items is None:
+        if min_items == 0 and max_items == 1:
+            return f'{item_rule}?'
+        elif min_items == 1 and max_items is None:
             return f'{item_rule}+'
-        elif min_items == 0 and max_items is None:
-            return f'{item_rule}*'
-        else:
-            return f'{item_rule}{{{min_items},{max_items if max_items is not None else ""}}}'
 
-    result = item_rule + ' ' + _build_repetition(f'({separator_rule} {item_rule})', min_items - 1 if min_items > 0 else 0, max_items - 1 if max_items is not None else None)
-    return f'({result})?' if min_items == 0 else result
+    result = ''
+
+    if min_items > 0:
+        if item_rule_is_literal and separator_rule is None:
+            result = '"' + (item_rule[1:-1] * min_items) + '"'
+        else:
+            result = (f' {separator_rule} ' if separator_rule else ' ').join([item_rule] * min_items)
+
+    def opt_repetitions(up_to_n, prefix_with_sep=False):
+        '''
+            - n=4, no sep:             '(a (a (a (a)?)?)?)?'
+            - n=4, sep=',', prefix:    '("," a ("," a ("," a ("," a)?)?)?)?'
+            - n=4, sep=',', no prefix: '(a ("," a ("," a ("," a)?)?)?)?'
+        '''
+
+        content = f'{separator_rule} {item_rule}' if prefix_with_sep and separator_rule else item_rule
+        if up_to_n == 0:
+            return ''
+        elif up_to_n == 1:
+            return f'({content})?'
+        elif separator_rule and not prefix_with_sep:
+            return f'({content} {opt_repetitions(up_to_n - 1, prefix_with_sep=True)})?'
+        else:
+            return (f'({content} ' * up_to_n).rstrip() + (')?' * up_to_n)
+
+    if min_items > 0 and max_items != min_items:
+        result += ' '
+
+    if max_items is not None:
+        result += opt_repetitions(max_items - min_items, prefix_with_sep=min_items > 0)
+    else:
+        item_operator = f'({separator_rule + " " if separator_rule else ""}{item_rule})'
+
+        if min_items == 0 and separator_rule:
+            result = f'({item_rule} {item_operator}*)?'
+        else:
+            result += f'{item_operator}*'
+
+    return result
 
 
 class BuiltinRule:
@@ -29,28 +59,31 @@ class BuiltinRule:
         self.content = content
         self.deps = deps or []
 
-# Constraining spaces to prevent model "running away".
-SPACE_RULE = '| " " | "\\n" [ \\t]{0,20}'
+_up_to_15_digits = _build_repetition('[0-9]', 0, 15)
+
+# whitespace is constrained to a single space char to prevent model "running away" in
+# whitespace. Also maybe improves generation quality?
+SPACE_RULE = '" "?'
 
 PRIMITIVE_RULES = {
     'boolean'      : BuiltinRule('("true" | "false") space', []),
-    'decimal-part' : BuiltinRule('[0-9]{1,16}', []),
-    'integral-part': BuiltinRule('[0] | [1-9] [0-9]{0,15}', []),
+    'decimal-part' : BuiltinRule('[0-9] ' + _up_to_15_digits, []),
+    'integral-part': BuiltinRule('[0-9] | [1-9] ' + _up_to_15_digits, []),
     'number'       : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']),
     'integer'      : BuiltinRule('("-"? integral-part) space', ['integral-part']),
     'value'        : BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']),
     'object'       : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
     'array'        : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
-    'uuid'         : BuiltinRule(r'"\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\"" space', []),
-    'char'         : BuiltinRule(r'[^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})', []),
+    'uuid'         : BuiltinRule(r'"\"" ' + ' "-" '.join('[0-9a-fA-F]' * n for n in [8, 4, 4, 4, 12]) + r' "\"" space', []),
+    'char'         : BuiltinRule(r'[^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])', []),
     'string'       : BuiltinRule(r'"\"" char* "\"" space', ['char']),
     'null'         : BuiltinRule('"null" space', []),
 }
 
 # TODO: support "uri", "email" string formats
 STRING_FORMAT_RULES = {
-    'date'            : BuiltinRule('[0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
-    'time'            : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
+    'date'            : BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
+    'time'            : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
     'date-time'       : BuiltinRule('date "T" time', ['date', 'time']),
     'date-string'     : BuiltinRule('"\\"" date "\\"" space', ['date']),
     'time-string'     : BuiltinRule('"\\"" time "\\"" space', ['time']),
@@ -300,7 +333,7 @@ class SchemaConverter:
                             sub_rule_ids[sub] = id
                         sub = id
 
-                    seq[-1] = (_build_repetition(f'"{sub}"' if sub_is_literal else sub, min_times, max_times), False)
+                    seq[-1] = (_build_repetition(f'"{sub}"' if sub_is_literal else sub, min_times, max_times, item_rule_is_literal=sub_is_literal), False)
                 else:
                     literal = ''
                     while i < length:

examples/llama-bench/README.md

@@ -162,7 +162,7 @@ $ ./llama-bench -o csv
 ```
 
 ```csv
-build_commit,build_number,cuda,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts
+build_commit,build_number,cuda,opencl,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts
 "3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","512","0","2023-09-23T12:09:01Z","212155977","732372","2413.341687","8.305961"
 "3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","0","128","2023-09-23T12:09:02Z","969320879","2728399","132.052051","0.371342"
 ```
@@ -179,6 +179,7 @@ $ ./llama-bench -o json
     "build_commit": "3469684",
     "build_number": 1275,
     "cuda": true,
+    "opencl": false,
     "metal": false,
     "gpu_blas": true,
     "blas": true,
@@ -209,6 +210,7 @@ $ ./llama-bench -o json
     "build_commit": "3469684",
     "build_number": 1275,
     "cuda": true,
+    "opencl": false,
     "metal": false,
     "gpu_blas": true,
     "blas": true,
@@ -251,6 +253,7 @@ CREATE TABLE IF NOT EXISTS test (
   build_commit TEXT,
   build_number INTEGER,
   cuda INTEGER,
+  opencl INTEGER,
   metal INTEGER,
   gpu_blas INTEGER,
   blas INTEGER,
@@ -276,6 +279,6 @@ CREATE TABLE IF NOT EXISTS test (
   stddev_ts REAL
 );
 
-INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634');
-INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692');
+INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634');
+INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692');
 ```

examples/llama-bench/llama-bench.cpp

@@ -41,6 +41,20 @@ static std::string join(const std::vector<T> & values, const std::string & delim
     return str.str();
 }
 
+template<class T>
+static std::vector<T> split(const std::string & str, char delim) {
+    std::vector<T> values;
+    std::istringstream str_stream(str);
+    std::string token;
+    while (std::getline(str_stream, token, delim)) {
+        T value;
+        std::istringstream token_stream(token);
+        token_stream >> value;
+        values.push_back(value);
+    }
+    return values;
+}
+
 template<typename T, typename F>
 static std::vector<std::string> transform_to_str(const std::vector<T> & values, F f) {
     std::vector<std::string> str_values;
@@ -126,11 +140,10 @@ static std::string get_gpu_info() {
 }
 
 // command line params
-enum output_formats {NONE, CSV, JSON, MARKDOWN, SQL};
+enum output_formats {CSV, JSON, MARKDOWN, SQL};
 
 static const char * output_format_str(output_formats format) {
     switch (format) {
-        case NONE:     return "none";
         case CSV:      return "csv";
         case JSON:     return "json";
         case MARKDOWN: return "md";
@@ -139,23 +152,6 @@ static const char * output_format_str(output_formats format) {
     }
 }
 
-static bool output_format_from_str(const std::string & s, output_formats & format) {
-    if (s == "none") {
-        format = NONE;
-    } else if (s == "csv") {
-        format = CSV;
-    } else if (s == "json") {
-        format = JSON;
-    } else if (s == "md") {
-        format = MARKDOWN;
-    } else if (s == "sql") {
-        format = SQL;
-    } else {
-        return false;
-    }
-    return true;
-}
-
 static const char * split_mode_str(llama_split_mode mode) {
     switch (mode) {
         case LLAMA_SPLIT_MODE_NONE:  return "none";
@@ -194,33 +190,31 @@ struct cmd_params {
     int reps;
     bool verbose;
     output_formats output_format;
-    output_formats output_format_stderr;
 };
 
 static const cmd_params cmd_params_defaults = {
-    /* model                */ {"models/7B/ggml-model-q4_0.gguf"},
-    /* n_prompt             */ {512},
-    /* n_gen                */ {128},
-    /* n_pg                 */ {},
-    /* n_batch              */ {2048},
-    /* n_ubatch             */ {512},
-    /* type_k               */ {GGML_TYPE_F16},
-    /* type_v               */ {GGML_TYPE_F16},
-    /* n_threads            */ {cpu_get_num_math()},
-    /* n_gpu_layers         */ {99},
-    /* rpc_servers          */ {""},
-    /* split_mode           */ {LLAMA_SPLIT_MODE_LAYER},
-    /* main_gpu             */ {0},
-    /* no_kv_offload        */ {false},
-    /* flash_attn           */ {false},
-    /* tensor_split         */ {std::vector<float>(llama_max_devices(), 0.0f)},
-    /* use_mmap             */ {true},
-    /* embeddings           */ {false},
-    /* numa                 */ GGML_NUMA_STRATEGY_DISABLED,
-    /* reps                 */ 5,
-    /* verbose              */ false,
-    /* output_format        */ MARKDOWN,
-    /* output_format_stderr */ NONE,
+    /* model         */ {"models/7B/ggml-model-q4_0.gguf"},
+    /* n_prompt      */ {512},
+    /* n_gen         */ {128},
+    /* n_pg          */ {},
+    /* n_batch       */ {2048},
+    /* n_ubatch      */ {512},
+    /* type_k        */ {GGML_TYPE_F16},
+    /* type_v        */ {GGML_TYPE_F16},
+    /* n_threads     */ {cpu_get_num_math()},
+    /* n_gpu_layers  */ {99},
+    /* rpc_servers   */ {""},
+    /* split_mode    */ {LLAMA_SPLIT_MODE_LAYER},
+    /* main_gpu      */ {0},
+    /* no_kv_offload */ {false},
+    /* flash_attn    */ {false},
+    /* tensor_split  */ {std::vector<float>(llama_max_devices(), 0.0f)},
+    /* use_mmap      */ {true},
+    /* embeddings    */ {false},
+    /* numa          */ GGML_NUMA_STRATEGY_DISABLED,
+    /* reps          */ 5,
+    /* verbose       */ false,
+    /* output_format */ MARKDOWN
 };
 
 static void print_usage(int /* argc */, char ** argv) {
@@ -249,7 +243,6 @@ static void print_usage(int /* argc */, char ** argv) {
     printf("  -ts, --tensor-split <ts0/ts1/..>    (default: 0)\n");
     printf("  -r, --repetitions <n>               (default: %d)\n", cmd_params_defaults.reps);
     printf("  -o, --output <csv|json|md|sql>      (default: %s)\n", output_format_str(cmd_params_defaults.output_format));
-    printf("  -oe, --output-err <csv|json|md|sql> (default: %s)\n", output_format_str(cmd_params_defaults.output_format_stderr));
     printf("  -v, --verbose                       (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
     printf("\n");
     printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
@@ -291,7 +284,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
 
     params.verbose = cmd_params_defaults.verbose;
     params.output_format = cmd_params_defaults.output_format;
-    params.output_format_stderr = cmd_params_defaults.output_format_stderr;
     params.reps = cmd_params_defaults.reps;
 
     for (int i = 1; i < argc; i++) {
@@ -308,28 +300,28 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<std::string>(argv[i], split_delim);
+            auto p = split<std::string>(argv[i], split_delim);
             params.model.insert(params.model.end(), p.begin(), p.end());
         } else if (arg == "-p" || arg == "--n-prompt") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<int>(argv[i], split_delim);
+            auto p = split<int>(argv[i], split_delim);
             params.n_prompt.insert(params.n_prompt.end(), p.begin(), p.end());
         } else if (arg == "-n" || arg == "--n-gen") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<int>(argv[i], split_delim);
+            auto p = split<int>(argv[i], split_delim);
             params.n_gen.insert(params.n_gen.end(), p.begin(), p.end());
         } else if (arg == "-pg") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<std::string>(argv[i], ',');
+            auto p = split<std::string>(argv[i], ',');
             if (p.size() != 2) {
                 invalid_param = true;
                 break;
@@ -340,21 +332,21 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<int>(argv[i], split_delim);
+            auto p = split<int>(argv[i], split_delim);
             params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
         } else if (arg == "-ub" || arg == "--ubatch-size") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<int>(argv[i], split_delim);
+            auto p = split<int>(argv[i], split_delim);
             params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end());
         } else if (arg == "-ctk" || arg == "--cache-type-k") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<std::string>(argv[i], split_delim);
+            auto p = split<std::string>(argv[i], split_delim);
             std::vector<ggml_type> types;
             for (const auto & t : p) {
                 ggml_type gt = ggml_type_from_name(t);
@@ -370,7 +362,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<std::string>(argv[i], split_delim);
+            auto p = split<std::string>(argv[i], split_delim);
             std::vector<ggml_type> types;
             for (const auto & t : p) {
                 ggml_type gt = ggml_type_from_name(t);
@@ -386,14 +378,14 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<int>(argv[i], split_delim);
+            auto p = split<int>(argv[i], split_delim);
             params.n_threads.insert(params.n_threads.end(), p.begin(), p.end());
         } else if (arg == "-ngl" || arg == "--n-gpu-layers") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<int>(argv[i], split_delim);
+            auto p = split<int>(argv[i], split_delim);
             params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end());
         } else if (arg == "-rpc" || arg == "--rpc") {
             if (++i >= argc) {
@@ -406,7 +398,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<std::string>(argv[i], split_delim);
+            auto p = split<std::string>(argv[i], split_delim);
             std::vector<llama_split_mode> modes;
             for (const auto & m : p) {
                 llama_split_mode mode;
@@ -428,13 +420,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            params.main_gpu = string_split<int>(argv[i], split_delim);
+            params.main_gpu = split<int>(argv[i], split_delim);
         } else if (arg == "-nkvo" || arg == "--no-kv-offload") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<bool>(argv[i], split_delim);
+            auto p = split<bool>(argv[i], split_delim);
             params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
         } else if (arg == "--numa") {
             if (++i >= argc) {
@@ -452,28 +444,28 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<bool>(argv[i], split_delim);
+            auto p = split<bool>(argv[i], split_delim);
             params.flash_attn.insert(params.flash_attn.end(), p.begin(), p.end());
         } else if (arg == "-mmp" || arg == "--mmap") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<bool>(argv[i], split_delim);
+            auto p = split<bool>(argv[i], split_delim);
             params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end());
         } else if (arg == "-embd" || arg == "--embeddings") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            auto p = string_split<bool>(argv[i], split_delim);
+            auto p = split<bool>(argv[i], split_delim);
             params.embeddings.insert(params.embeddings.end(), p.begin(), p.end());
         } else if (arg == "-ts" || arg == "--tensor-split") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            for (auto ts : string_split<std::string>(argv[i], split_delim)) {
+            for (auto ts : split<std::string>(argv[i], split_delim)) {
                 // split string by ; and /
                 const std::regex regex{R"([;/]+)"};
                 std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1};
@@ -501,13 +493,18 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
                 invalid_param = true;
                 break;
             }
-            invalid_param = !output_format_from_str(argv[i], params.output_format);
-        } else if (arg == "-oe" || arg == "--output-err") {
-            if (++i >= argc) {
+            if (argv[i] == std::string("csv")) {
+                params.output_format = CSV;
+            } else if (argv[i] == std::string("json")) {
+                params.output_format = JSON;
+            } else if (argv[i] == std::string("md")) {
+                params.output_format = MARKDOWN;
+            } else if (argv[i] == std::string("sql")) {
+                params.output_format = SQL;
+            } else {
                 invalid_param = true;
                 break;
             }
-            invalid_param = !output_format_from_str(argv[i], params.output_format_stderr);
         } else if (arg == "-v" || arg == "--verbose") {
             params.verbose = true;
         } else {
@@ -709,6 +706,7 @@ struct test {
     static const std::string build_commit;
     static const int build_number;
     static const bool cuda;
+    static const bool opencl;
     static const bool vulkan;
     static const bool kompute;
     static const bool metal;
@@ -797,6 +795,9 @@ struct test {
         if (cuda) {
             return GGML_CUDA_NAME;
         }
+        if (opencl) {
+            return "OpenCL";
+        }
         if (vulkan) {
             return "Vulkan";
         }
@@ -825,7 +826,7 @@ struct test {
     static const std::vector<std::string> & get_fields() {
         static const std::vector<std::string> fields = {
             "build_commit", "build_number",
-            "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", "blas",
+            "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", "blas",
             "cpu_info", "gpu_info",
             "model_filename", "model_type", "model_size", "model_n_params",
             "n_batch", "n_ubatch",
@@ -851,7 +852,7 @@ struct test {
             field == "avg_ns" || field == "stddev_ns") {
             return INT;
         }
-        if (field == "cuda" || field == "vulkan" || field == "kompute" || field == "metal" ||
+        if (field == "cuda" || field == "opencl"  || field == "vulkan" || field == "kompute" || field == "metal" ||
             field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" ||
             field == "flash_attn" || field == "use_mmap" || field == "embeddings") {
             return BOOL;
@@ -880,7 +881,7 @@ struct test {
         }
         std::vector<std::string> values = {
             build_commit, std::to_string(build_number),
-            std::to_string(cuda), std::to_string(vulkan), std::to_string(vulkan),
+            std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan),
             std::to_string(metal), std::to_string(sycl), std::to_string(rpc), std::to_string(gpu_blas), std::to_string(blas),
             cpu_info, gpu_info,
             model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
@@ -909,6 +910,7 @@ struct test {
 const std::string test::build_commit = LLAMA_COMMIT;
 const int         test::build_number = LLAMA_BUILD_NUMBER;
 const bool        test::cuda         = !!ggml_cpu_has_cuda();
+const bool        test::opencl       = !!ggml_cpu_has_clblast();
 const bool        test::vulkan       = !!ggml_cpu_has_vulkan();
 const bool        test::kompute      = !!ggml_cpu_has_kompute();
 const bool        test::metal        = !!ggml_cpu_has_metal();
@@ -1033,27 +1035,6 @@ struct markdown_printer : public printer {
         if (field == "n_gpu_layers") {
             return 3;
         }
-        if (field == "n_threads") {
-            return 7;
-        }
-        if (field == "n_batch") {
-            return 7;
-        }
-        if (field == "n_ubatch") {
-            return 8;
-        }
-        if (field == "type_k" || field == "type_v") {
-            return 6;
-        }
-        if (field == "split_mode") {
-            return 5;
-        }
-        if (field == "flash_attn") {
-            return 2;
-        }
-        if (field == "use_mmap") {
-            return 4;
-        }
         if (field == "test") {
             return 13;
         }
@@ -1297,22 +1278,6 @@ static void llama_null_log_callback(enum ggml_log_level level, const char * text
     (void) user_data;
 }
 
-static std::unique_ptr<printer> create_printer(output_formats format) {
-    switch (format) {
-        case NONE:
-            return nullptr;
-        case CSV:
-            return std::unique_ptr<printer>(new csv_printer());
-        case JSON:
-            return std::unique_ptr<printer>(new json_printer());
-        case MARKDOWN:
-            return std::unique_ptr<printer>(new markdown_printer());
-        case SQL:
-            return std::unique_ptr<printer>(new sql_printer());
-    }
-    GGML_ASSERT(false);
-}
-
 int main(int argc, char ** argv) {
     // try to set locale for unicode characters in markdown
     setlocale(LC_CTYPE, ".UTF-8");
@@ -1339,18 +1304,26 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // initialize printer
-    std::unique_ptr<printer> p = create_printer(params.output_format);
-    std::unique_ptr<printer> p_err = create_printer(params.output_format_stderr);
-
-    if (p) {
-        p->fout = stdout;
-        p->print_header(params);
-    }
-
-    if (p_err) {
-        p_err->fout = stderr;
-        p_err->print_header(params);
+    std::unique_ptr<printer> p;
+    switch (params.output_format) {
+        case CSV:
+            p.reset(new csv_printer());
+            break;
+        case JSON:
+            p.reset(new json_printer());
+            break;
+        case MARKDOWN:
+            p.reset(new markdown_printer());
+            break;
+        case SQL:
+            p.reset(new sql_printer());
+            break;
+        default:
+            assert(false);
+            exit(1);
     }
+    p->fout = stdout;
+    p->print_header(params);
 
     std::vector<cmd_params_instance> params_instances = get_cmd_params_instances(params);
 
@@ -1408,15 +1381,7 @@ int main(int argc, char ** argv) {
             t.samples_ns.push_back(t_ns);
         }
 
-        if (p) {
-            p->print_test(t);
-            fflush(p->fout);
-        }
-
-        if (p_err) {
-            p_err->print_test(t);
-            fflush(p_err->fout);
-        }
+        p->print_test(t);
 
         llama_print_timings(ctx);
 
@@ -1425,13 +1390,7 @@ int main(int argc, char ** argv) {
 
     llama_free_model(lmodel);
 
-    if (p) {
-        p->print_footer();
-    }
-
-    if (p_err) {
-        p_err->print_footer();
-    }
+    p->print_footer();
 
     llama_backend_free();
 

examples/llama2-13b.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+#
+# Temporary script - will be removed in the future
+#
+
+cd `dirname $0`
+cd ..
+
+./main -m models/available/Llama2/13B/llama-2-13b.ggmlv3.q4_0.bin \
+       --color \
+       --ctx_size 2048 \
+       -n -1 \
+       -ins -b 256 \
+       --top_k 10000 \
+       --temp 0.2 \
+       --repeat_penalty 1.1 \
+       -t 8

examples/llama2.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+#
+# Temporary script - will be removed in the future
+#
+
+cd `dirname $0`
+cd ..
+
+./main -m models/available/Llama2/7B/llama-2-7b.ggmlv3.q4_0.bin \
+       --color \
+       --ctx_size 2048 \
+       -n -1 \
+       -ins -b 256 \
+       --top_k 10000 \
+       --temp 0.2 \
+       --repeat_penalty 1.1 \
+       -t 8

examples/llava/MobileVLM-README.md

@@ -54,10 +54,10 @@ python ./examples/llava/convert-image-encoder-to-gguf \
     --projector-type ldpv2
 ```
 
-4. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF:
+4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
 
 ```sh
-python ./examples/convert-legacy-llama.py path/to/MobileVLM-1.7B
+python ./convert.py path/to/MobileVLM-1.7B
 ```
 
 5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k`

examples/llava/README.md

@@ -50,10 +50,10 @@ python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b
 python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
 ```
 
-5. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF:
+5. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
 
 ```sh
-python ./examples/convert-legacy-llama.py ../llava-v1.5-7b --skip-unknown
+python ./convert.py ../llava-v1.5-7b --skip-unknown
 ```
 
 Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory.
@@ -92,7 +92,7 @@ python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projecto
 
 6) Then convert the model to gguf format:
 ```console
-python ./examples/convert-legacy-llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown
+python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown
 ```
 
 7) And finally we can run the llava-cli using the 1.6 model version:

examples/llava/llava-cli.cpp

@@ -112,12 +112,9 @@ struct llava_context {
     struct llama_model * model = NULL;
 };
 
-static void print_usage(int argc, char ** argv, const gpt_params & params) {
-    gpt_params_print_usage(argc, argv, params);
-
-    LOG_TEE("\n example usage:\n");
-    LOG_TEE("\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_TEE("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
+static void show_additional_info(int /*argc*/, char ** argv) {
+    LOG_TEE("\n example usage: %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_TEE("  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, gpt_params * params, const std::string & fname) {
@@ -281,7 +278,7 @@ int main(int argc, char ** argv) {
     gpt_params params;
 
     if (!gpt_params_parse(argc, argv, params)) {
-        print_usage(argc, argv, params);
+        show_additional_info(argc, argv);
         return 1;
     }
 
@@ -293,7 +290,8 @@ int main(int argc, char ** argv) {
 #endif // LOG_DISABLE_LOGS
 
     if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
-        print_usage(argc, argv, {});
+        gpt_params_print_usage(argc, argv, params);
+        show_additional_info(argc, argv);
         return 1;
     }
     auto model = llava_init(&params);

examples/llava/requirements.txt

@@ -1,3 +1,3 @@
--r ../../requirements/requirements-convert-legacy-llama.txt
+-r ../../requirements/requirements-convert.txt
 pillow~=10.2.0
 torch~=2.1.1

examples/lookahead/lookahead.cpp

@@ -37,8 +37,7 @@ struct ngram_container {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    if (!gpt_params_parse(argc, argv, params)) {
-        gpt_params_print_usage(argc, argv, params);
+    if (gpt_params_parse(argc, argv, params) == false) {
         return 1;
     }
 

examples/lookup/lookup-create.cpp

@@ -14,10 +14,8 @@ int main(int argc, char ** argv){
     gpt_params params;
 
     if (!gpt_params_parse(argc, argv, params)) {
-        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
-
     // init llama.cpp
     llama_backend_init();
     llama_numa_init(params.numa);

examples/lookup/lookup-stats.cpp

@@ -16,7 +16,6 @@ int main(int argc, char ** argv){
     gpt_params params;
 
     if (!gpt_params_parse(argc, argv, params)) {
-        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 

examples/lookup/lookup.cpp

@@ -15,7 +15,6 @@ int main(int argc, char ** argv){
     gpt_params params;
 
     if (!gpt_params_parse(argc, argv, params)) {
-        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 

examples/main-cmake-pkg/README.md

@@ -8,14 +8,16 @@ Because this example is "outside of the source tree", it is important to first b
 
 ### Considerations
 
-When hardware acceleration libraries are used (e.g. CUDA, Metal, etc.), CMake must be able to locate the associated CMake package.
+When hardware acceleration libraries are used (e.g. CUDA, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_.
 
 ### Build llama.cpp and install to C:\LlamaCPP directory
 
+In this case, CLBlast was already installed so the CMake package is referenced in `CMAKE_PREFIX_PATH`.
+
 ```cmd
 git clone https://github.com/ggerganov/llama.cpp
 cd llama.cpp
-cmake -B build -DBUILD_SHARED_LIBS=OFF -G "Visual Studio 17 2022" -A x64
+cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64
 cmake --build build --config Release
 cmake --install build --prefix C:/LlamaCPP
 ```
@@ -25,7 +27,7 @@ cmake --install build --prefix C:/LlamaCPP
 
 ```cmd
 cd ..\examples\main-cmake-pkg
-cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
+cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
 cmake --build build --config Release
 cmake --install build --prefix C:/MyLlamaApp
 ```

examples/main/README.md

@@ -53,13 +53,13 @@ The following command generates "infinite" text from a starting prompt (you can
 #### Unix-based systems (Linux, macOS, etc.):
 
 ```bash
-./main -m models/7B/ggml-model.bin --ignore-eos -n -1
+./main -m models/7B/ggml-model.bin --ignore-eos -n -1 --random-prompt
 ```
 
 #### Windows:
 
 ```powershell
-main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1
+main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 --random-prompt
 ```
 
 ## Common Options
@@ -69,6 +69,7 @@ In this section, we cover the most commonly used options for running the `main`
 -   `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`; inferred from `--model-url` if set).
 -   `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf).
 -   `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
+-   `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models.
 -   `-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.
 
@@ -79,10 +80,11 @@ The `main` program provides several ways to interact with the LLaMA models using
 -   `--prompt PROMPT`: Provide a prompt directly as a command-line option.
 -   `--file FNAME`: Provide a file containing a prompt or multiple prompts.
 -   `--interactive-first`: Run the program in interactive mode and wait for input right away. (More on this below.)
+-   `--random-prompt`: Start with a randomized prompt.
 
 ## Interaction
 
-The `main` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive` and `--interactive-first`.
+The `main` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive`, `--interactive-first`, and `--instruct`.
 
 In interactive mode, users can participate in text generation by injecting their input during the process. Users can press `Ctrl+C` at any time to interject and type their input, followed by pressing `Return` to submit it to the LLaMA model. To submit additional lines without finalizing input, users can end the current line with a backslash (`\`) and continue typing.
 
@@ -90,6 +92,7 @@ In interactive mode, users can participate in text generation by injecting their
 
 -   `-i, --interactive`: Run the program in interactive mode, allowing users to engage in real-time conversations or provide specific instructions to the model.
 -   `--interactive-first`: Run the program in interactive mode and immediately wait for user input before starting the text generation.
+-   `-ins, --instruct`: Run the program in instruction mode, which is specifically designed to work with Alpaca models that excel in completing tasks based on user instructions.
 -   `--color`: Enable colorized output to differentiate visually distinguishing between prompts, user input, and generated text.
 
 By understanding and utilizing these interaction options, you can create engaging and dynamic experiences with the LLaMA models, tailoring the text generation process to your specific needs.
@@ -118,6 +121,16 @@ The `--in-suffix` flag is used to add a suffix after your input. This is useful
 ./main -r "User:" --in-prefix " " --in-suffix "Assistant:"
 ```
 
+### Instruction Mode
+
+Instruction mode is particularly useful when working with Alpaca models, which are designed to follow user instructions for specific tasks:
+
+-   `-ins, --instruct`: Enable instruction mode to leverage the capabilities of Alpaca models in completing tasks based on user-provided instructions.
+
+Technical detail: the user's input is internally prefixed with the reverse prompt (or `### Instruction:` as the default), and followed by `### Response:` (except if you just press Return without any input, to keep generating a longer response).
+
+By understanding and utilizing these interaction options, you can create engaging and dynamic experiences with the LLaMA models, tailoring the text generation process to your specific needs.
+
 ## Context Management
 
 During text generation, LLaMA models have a limited context size, which means they can only consider a certain number of tokens from the input and generated text. When the context fills up, the model resets internally, potentially losing some information from the beginning of the conversation or instructions. Context management options help maintain continuity and coherence in these situations.

examples/main/main.cpp

@@ -122,10 +122,8 @@ int main(int argc, char ** argv) {
     g_params = &params;
 
     if (!gpt_params_parse(argc, argv, params)) {
-        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
-
     llama_sampling_params & sparams = params.sparams;
 
 #ifndef LOG_DISABLE_LOGS
@@ -182,6 +180,9 @@ int main(int argc, char ** argv) {
     LOG_TEE("%s: seed  = %u\n", __func__, params.seed);
 
     std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = string_random_prompt(rng);
+    }
 
     LOG("%s: llama backend init\n", __func__);
     llama_backend_init();
@@ -249,8 +250,11 @@ int main(int argc, char ** argv) {
 
     std::vector<llama_token> embd_inp;
 
-    if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) {
+    if (params.interactive_first || params.instruct || params.chatml || !params.prompt.empty() || session_tokens.empty()) {
         LOG("tokenize the prompt\n");
+        if (params.chatml) {
+            params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>";
+        }
         embd_inp = ::llama_tokenize(ctx, params.prompt, true, true);
     } else {
         LOG("use session tokens\n");
@@ -328,13 +332,37 @@ int main(int argc, char ** argv) {
     }
 
     // number of tokens to keep when resetting context
-    if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size()) {
+    if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) {
         params.n_keep = (int)embd_inp.size();
     } else {
         params.n_keep += add_bos; // always keep the BOS token
     }
 
-    if (params.conversation) {
+    // prefix & suffix for instruct mode
+    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true,  true);
+    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false, true);
+
+    LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
+    LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());
+
+    // chatml prefix & suffix
+    const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", true, true);
+    const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true);
+
+    LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str());
+    LOG("cml_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_sfx).c_str());
+
+    // in instruct mode, we inject a prefix and a suffix to each input by the user
+    if (params.instruct) {
+        params.interactive_first = true;
+        params.antiprompt.emplace_back("### Instruction:\n\n");
+    }
+    // similar for chatml mode
+    else if (params.chatml) {
+        params.interactive_first = true;
+        params.antiprompt.emplace_back("<|im_start|>user\n");
+    }
+    else if (params.conversation) {
         params.interactive_first = true;
     }
 
@@ -795,13 +823,15 @@ int main(int argc, char ** argv) {
 
                     is_interacting = true;
                     printf("\n");
+                } else if (params.instruct || params.chatml) {
+                    is_interacting = true;
                 }
             }
 
             if (n_past > 0 && is_interacting) {
                 LOG("waiting for user input\n");
 
-                if (params.conversation) {
+                if (params.conversation || params.instruct || params.chatml) {
                     printf("\n> ");
                 }
 
@@ -844,12 +874,24 @@ int main(int argc, char ** argv) {
 
                     const size_t original_size = embd_inp.size();
 
+                    // instruct mode: insert instruction prefix
+                    if (params.instruct && !is_antiprompt) {
+                        LOG("inserting instruction prefix\n");
+                        n_consumed = embd_inp.size();
+                        embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
+                    }
+                    // chatml mode: insert user chat prefix
+                    if (params.chatml && !is_antiprompt) {
+                        LOG("inserting chatml prefix\n");
+                        n_consumed = embd_inp.size();
+                        embd_inp.insert(embd_inp.end(), cml_pfx.begin(), cml_pfx.end());
+                    }
                     if (params.escape) {
                         string_process_escapes(buffer);
                     }
 
                     const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
-                    const auto line_inp = ::llama_tokenize(ctx, buffer,              false, false);
+                    const auto line_inp = ::llama_tokenize(ctx, buffer,              false, params.interactive_specials);
                     const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
 
                     LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
@@ -858,6 +900,17 @@ int main(int argc, char ** argv) {
                     embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
                     embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end());
 
+                    // instruct mode: insert response suffix
+                    if (params.instruct) {
+                        LOG("inserting instruction suffix\n");
+                        embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
+                    }
+                    // chatml mode: insert assistant chat suffix
+                    if (params.chatml) {
+                        LOG("inserting chatml suffix\n");
+                        embd_inp.insert(embd_inp.end(), cml_sfx.begin(), cml_sfx.end());
+                    }
+
                     for (size_t i = original_size; i < embd_inp.size(); ++i) {
                         const llama_token token = embd_inp[i];
                         output_tokens.push_back(token);
@@ -882,7 +935,7 @@ int main(int argc, char ** argv) {
         }
 
         // end of generation
-        if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.interactive)) {
+        if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.instruct || params.interactive || params.chatml)) {
             LOG_TEE(" [end of text]\n");
             break;
         }

examples/make-ggml.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+"""
+This script converts Hugging Face Llama, StarCoder, Falcon, Baichuan, and GPT-NeoX models to GGUF and quantizes them.
+
+Usage:
+python make-ggml.py {model_dir_or_hf_repo_name} --model_type {model_type} [--outname {output_name} (Optional)] [--outdir {output_directory} (Optional)] [--quants {quant_types} (Optional)] [--keep_fp16 (Optional)]
+
+Arguments:
+- model: (Required) The directory of the downloaded Hugging Face model or the name of the Hugging Face model repository. If the model directory does not exist, it will be downloaded from the Hugging Face model hub.
+- --model_type: (Required) The type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.
+- --outname: (Optional) The name of the output model. If not specified, the last part of the model directory path or the Hugging Face model repo name will be used.
+- --outdir: (Optional) The directory where the output model(s) will be stored. If not specified, '../models/{outname}' will be used.
+- --quants: (Optional) The types of quantization to apply. This should be a space-separated list. The default is 'Q4_K_M Q5_K_S'.
+- --keep_fp16: (Optional) If specified, the FP16 model will not be deleted after the quantized models are created.
+
+Old quant types (some base model types require these):
+- Q4_0: small, very high quality loss - legacy, prefer using Q3_K_M
+- Q4_1: small, substantial quality loss - legacy, prefer using Q3_K_L
+- Q5_0: medium, balanced quality - legacy, prefer using Q4_K_M
+- Q5_1: medium, low quality loss - legacy, prefer using Q5_K_M
+
+New quant types (recommended):
+- Q2_K: smallest, extreme quality loss - not recommended
+- Q3_K: alias for Q3_K_M
+- Q3_K_S: very small, very high quality loss
+- Q3_K_M: very small, very high quality loss
+- Q3_K_L: small, substantial quality loss
+- Q4_K: alias for Q4_K_M
+- Q4_K_S: small, significant quality loss
+- Q4_K_M: medium, balanced quality - recommended
+- Q5_K: alias for Q5_K_M
+- Q5_K_S: large, low quality loss - recommended
+- Q5_K_M: large, very low quality loss - recommended
+- Q6_K: very large, extremely low quality loss
+- Q8_0: very large, extremely low quality loss - not recommended
+- F16: extremely large, virtually no quality loss - not recommended
+- F32: absolutely huge, lossless - not recommended
+"""
+import subprocess
+subprocess.run(f"pip install huggingface-hub==0.16.4", shell=True, check=True)
+
+import argparse
+import os
+from huggingface_hub import snapshot_download
+
+def main(model, model_type, outname, outdir, quants, keep_fp16):
+    if not os.path.isdir(model):
+        print(f"Model not found at {model}. Downloading...")
+        try:
+            if outname is None:
+                outname = model.split('/')[-1]
+            model = snapshot_download(repo_id=model, cache_dir='../models/hf_cache')
+        except Exception as e:
+            raise Exception(f"Could not download the model: {e}")
+
+    if outdir is None:
+        outdir = f'../models/{outname}'
+
+    if not os.path.isfile(f"{model}/config.json"):
+        raise Exception(f"Could not find config.json in {model}")
+
+    os.makedirs(outdir, exist_ok=True)
+
+    print("Building llama.cpp")
+    subprocess.run(f"cd .. && make quantize", shell=True, check=True)
+
+    fp16 = f"{outdir}/{outname}.gguf.fp16.bin"
+
+    print(f"Making unquantised GGUF at {fp16}")
+    if not os.path.isfile(fp16):
+        if model_type != "llama":
+            subprocess.run(f"python3 ../convert-{model_type}-hf-to-gguf.py {model} 1 --outfile {fp16}", shell=True, check=True)
+        else:
+            subprocess.run(f"python3 ../convert.py {model} --outtype f16 --outfile {fp16}", shell=True, check=True)
+    else:
+        print(f"Unquantised GGML already exists at: {fp16}")
+
+    print("Making quants")
+    for type in quants:
+        outfile = f"{outdir}/{outname}.gguf.{type}.bin"
+        print(f"Making {type} : {outfile}")
+        subprocess.run(f"../quantize {fp16} {outfile} {type}", shell=True, check=True)
+
+    if not keep_fp16:
+        os.remove(fp16)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Convert/Quantize HF models to GGUF. If you have the HF model downloaded already, pass the path to the model dir. Otherwise, pass the Hugging Face model repo name. You need to be in the /examples folder for it to work.')
+    parser.add_argument('model', help='Downloaded model dir or Hugging Face model repo name')
+    parser.add_argument('--model_type', required=True, choices=['llama', 'starcoder', 'falcon', 'baichuan', 'gptneox'], help='Type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.')
+    parser.add_argument('--outname', default=None, help='Output model(s) name')
+    parser.add_argument('--outdir', default=None, help='Output directory')
+    parser.add_argument('--quants', nargs='*', default=["Q4_K_M", "Q5_K_S"], help='Quant types')
+    parser.add_argument('--keep_fp16', action='store_true', help='Keep fp16 model', default=False)
+
+    args = parser.parse_args()
+
+    main(args.model, args.model_type, args.outname, args.outdir, args.quants, args.keep_fp16)

examples/parallel/parallel.cpp

@@ -100,8 +100,7 @@ int main(int argc, char ** argv) {
 
     gpt_params params;
 
-    if (!gpt_params_parse(argc, argv, params)) {
-        gpt_params_print_usage(argc, argv, params);
+    if (gpt_params_parse(argc, argv, params) == false) {
         return 1;
     }
 

examples/passkey/README.md

@@ -8,5 +8,5 @@ See the following PRs for more info:
 ### Usage
 
 ```bash
-make -j && ./passkey -m ./models/llama-7b-v2/ggml-model-f16.gguf --junk 250
+make -j && ./passkey ./models/llama-7b-v2/ggml-model-f16.gguf 250
 ```

examples/passkey/passkey.cpp

@@ -6,32 +6,46 @@
 #include <string>
 #include <vector>
 
-static void print_usage(int argc, char ** argv, const gpt_params & params) {
-    gpt_params_print_usage(argc, argv, params);
-
-    LOG_TEE("\nexample usage:\n");
-    LOG_TEE("\n    %s -m model.gguf --junk 250 --pos 90 --keep 32 --grp-attn-n 2 [--seed 1234]\n", argv[0]);
-    LOG_TEE("\n");
-}
-
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    params.n_junk = 250;
-    params.n_keep = 32;
-    params.i_pos  = -1;
-
-    if (!gpt_params_parse(argc, argv, params)) {
-        print_usage(argc, argv, params);
-        return 1;
+    if (argc == 1 || argv[1][0] == '-') {
+        printf("usage: %s MODEL_PATH N_JUNK N_GRP I_POS SEED\n" , argv[0]);
+        return 1 ;
     }
 
-    srand(params.seed == LLAMA_DEFAULT_SEED ? time(NULL) : params.seed);
+    int seed = -1;
 
-    int n_junk = params.n_junk;
-    int n_keep = params.n_keep;
-    int n_grp  = params.grp_attn_n;
-    int i_pos  = params.i_pos;
+    int n_junk = 250; // number of times to repeat the junk text
+    int n_keep = 32;  // number of tokens in the prompt prefix
+    int n_grp  = 1;   // if more than 1 - perform LongLM SelfExtend
+    int i_pos  = -1;  // position of the passkey in the junk text
+
+    if (argc >= 2) {
+        params.model = argv[1];
+    }
+
+    if (argc >= 3) {
+        n_junk = std::stoi(argv[2]);
+    }
+
+    if (argc >= 4) {
+        n_grp = std::stoi(argv[3]);
+    }
+
+    if (argc >= 5) {
+        i_pos = std::stoi(argv[4]);
+    }
+
+    if (argc >= 6) {
+        seed = std::stoi(argv[5]);
+    }
+
+    if (seed == -1) {
+        seed = time(NULL);
+    }
+
+    srand(seed);
 
     if (i_pos == -1) {
         i_pos = rand() % n_junk;
@@ -62,7 +76,9 @@ int main(int argc, char ** argv) {
 
     // initialize the model
 
-    llama_model_params model_params = llama_model_params_from_gpt_params(params);
+    llama_model_params model_params = llama_model_default_params();
+
+    model_params.n_gpu_layers = 99; // offload all layers to the GPU
 
     llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
 
@@ -73,9 +89,13 @@ int main(int argc, char ** argv) {
 
     // initialize the context
 
-    llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
+    llama_context_params ctx_params = llama_context_default_params();
 
-    ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep;
+    ctx_params.seed    = seed;
+    ctx_params.n_ctx   = llama_n_ctx_train(model)*n_grp + n_keep;
+    ctx_params.n_batch = 512;
+    ctx_params.n_threads       = params.n_threads;
+    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 
     GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp");
 
@@ -115,7 +135,7 @@ int main(int argc, char ** argv) {
     LOG_TEE("prompt tokens: %d\n", n_tokens_all);
     //LOG_TEE("prompt: %s\n", params.prompt.c_str());
 
-    llama_batch batch = llama_batch_init(params.n_batch, 0, 1);
+    llama_batch batch = llama_batch_init(512, 0, 1);
 
     int n_past = 0;
 

examples/perplexity/perplexity.cpp

@@ -1032,7 +1032,7 @@ struct winogrande_entry {
     std::vector<llama_token> seq_tokens[2];
 };
 
-static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string & prompt) {
+static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string& prompt) {
     std::vector<winogrande_entry> result;
     std::istringstream in(prompt);
     std::string line;
@@ -1964,14 +1964,12 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
 int main(int argc, char ** argv) {
     gpt_params params;
 
-    params.n_ctx = 512;
-    params.logits_all = true;
-
     if (!gpt_params_parse(argc, argv, params)) {
-        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 
+    params.logits_all = true;
+
     const int32_t n_ctx = params.n_ctx;
 
     if (n_ctx <= 0) {
@@ -2008,6 +2006,9 @@ int main(int argc, char ** argv) {
     fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
 
     std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = string_random_prompt(rng);
+    }
 
     llama_backend_init();
     llama_numa_init(params.numa);
@@ -2026,7 +2027,6 @@ int main(int argc, char ** argv) {
     }
 
     const int n_ctx_train = llama_n_ctx_train(model);
-
     if (params.n_ctx > n_ctx_train) {
         fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
                 __func__, n_ctx_train, params.n_ctx);

examples/pydantic_models_to_grammar.py

@@ -624,7 +624,7 @@ string ::= "\"" (
         "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
       )* "\"" ws
 ws ::= ([ \t\n] ws)?
-float ::= ("-"? ([0] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
+float ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
 
 integer ::= [0-9]+"""
 

examples/quantize/quantize.cpp

@@ -26,7 +26,6 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
     { "IQ2_M",  LLAMA_FTYPE_MOSTLY_IQ2_M,  " 2.7  bpw quantization",            },
     { "IQ1_S",  LLAMA_FTYPE_MOSTLY_IQ1_S,  " 1.56 bpw quantization",            },
     { "IQ1_M",  LLAMA_FTYPE_MOSTLY_IQ1_M,  " 1.75 bpw quantization",            },
-    { "I2_S",   LLAMA_FTYPE_MOSTLY_I2_S,   " 2    bpw per-tensor quantization", },
     { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
     { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
     { "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization",            },

examples/quantize/tests.sh

@@ -47,7 +47,7 @@ echo PASS
 echo
 
 # 3a. Test the requanted model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --n-predict 32
+$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --random-prompt --n-predict 32
 echo PASS
 echo
 
@@ -57,7 +57,7 @@ echo PASS
 echo
 
 # 4b. Test the requanted model is loading properly
-$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --n-predict 32
+$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --random-prompt --n-predict 32
 echo PASS
 echo
 

examples/retrieval/retrieval.cpp

@@ -4,12 +4,72 @@
 #include <algorithm>
 #include <fstream>
 
-static void print_usage(int argc, char ** argv, const gpt_params & params) {
-    gpt_params_print_usage(argc, argv, params);
+struct retrieval_params {
+    std::vector<std::string> context_files; // context files to embed
+    int32_t chunk_size            = 64;     // chunk size for context embedding
+    std::string chunk_separator   = "\n";   // chunk separator for context embedding
+};
 
-    LOG_TEE("\nexample usage:\n");
-    LOG_TEE("\n    %s --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .\n", argv[0]);
-    LOG_TEE("\n");
+static void retrieval_params_print_usage(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & params) {
+    gpt_params_print_usage(argc, argv, gpt_params);
+    printf("retrieval options:\n");
+    printf("  --context-file FNAME  file containing context to embed.\n");
+    printf("                        specify multiple files by providing --context-file option multiple times.\n");
+    printf("  --chunk-size N        minimum length of embedded text chunk (default:%d)\n", params.chunk_size);
+    printf("  --chunk-separator STRING\n");
+    printf("                        string to separate chunks (default: \"\\n\")\n");
+    printf("\n");
+}
+
+static void retrieval_params_parse(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & retrieval_params) {
+    int i = 1;
+    std::string arg;
+    while (i < argc) {
+        arg = argv[i];
+        bool invalid_gpt_param = false;
+        if(gpt_params_find_arg(argc, argv, argv[i], gpt_params, i, invalid_gpt_param)) {
+            if (invalid_gpt_param) {
+                fprintf(stderr, "error: invalid argument: %s\n", arg.c_str());
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            // option was parsed by gpt_params_find_arg
+        } else if (arg == "--context-file") {
+            if (++i >= argc) {
+                fprintf(stderr, "error: missing argument for --context-file\n");
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            std::ifstream file(argv[i]);
+            if (!file) {
+                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            // store the external file name in params
+            retrieval_params.context_files.push_back(argv[i]);
+        } else if (arg == "--chunk-size") {
+            if (++i >= argc) {
+                fprintf(stderr, "error: missing argument for --chunk-size\n");
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            retrieval_params.chunk_size = std::stoi(argv[i]);
+        } else if (arg == "--chunk-separator") {
+            if (++i >= argc) {
+                fprintf(stderr, "error: missing argument for --chunk-separator\n");
+                retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+                exit(1);
+            }
+            retrieval_params.chunk_separator = argv[i];
+        } else {
+            // unknown argument
+            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+            retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
+            exit(1);
+        }
+        i++;
+    }
 }
 
 struct chunk {
@@ -111,35 +171,33 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
 
 int main(int argc, char ** argv) {
     gpt_params params;
+    retrieval_params retrieval_params;
 
-    if (!gpt_params_parse(argc, argv, params)) {
-        print_usage(argc, argv, params);
-        return 1;
-    }
+    retrieval_params_parse(argc, argv, params, retrieval_params);
 
     // For BERT models, batch size must be equal to ubatch size
     params.n_ubatch = params.n_batch;
-    params.embedding = true;
 
-    if (params.chunk_size <= 0) {
+    if (retrieval_params.chunk_size <= 0) {
         fprintf(stderr, "chunk_size must be positive\n");
         return 1;
     }
-    if (params.context_files.empty()) {
+    if (retrieval_params.context_files.empty()) {
         fprintf(stderr, "context_files must be specified\n");
         return 1;
     }
+    params.embedding = true;
 
     print_build_info();
 
     printf("processing files:\n");
-    for (auto & context_file : params.context_files) {
+    for (auto & context_file : retrieval_params.context_files) {
         printf("%s\n", context_file.c_str());
     }
 
     std::vector<chunk> chunks;
-    for (auto & context_file : params.context_files) {
-        std::vector<chunk> file_chunk = chunk_file(context_file, params.chunk_size, params.chunk_separator);
+    for (auto & context_file : retrieval_params.context_files) {
+        std::vector<chunk> file_chunk = chunk_file(context_file, retrieval_params.chunk_size, retrieval_params.chunk_separator);
         chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end());
     }
     printf("Number of chunks: %ld\n", chunks.size());
@@ -184,7 +242,7 @@ int main(int argc, char ** argv) {
             return 1;
         }
         // add eos if not present
-        if (llama_token_eos(model) >= 0 && (inp.empty() || inp.back() != llama_token_eos(model))) {
+        if (inp.empty() || inp.back() != llama_token_eos(model)) {
             inp.push_back(llama_token_eos(model));
         }
         chunk.tokens = inp;

examples/save-load-state/save-load-state.cpp

@@ -11,7 +11,6 @@ int main(int argc, char ** argv) {
     params.prompt = "The quick brown fox";
 
     if (!gpt_params_parse(argc, argv, params)) {
-        gpt_params_print_usage(argc, argv, params);
         return 1;
     }
 

examples/server/CMakeLists.txt

@@ -8,20 +8,9 @@ set(TARGET_SRCS
     httplib.h
 )
 set(PUBLIC_ASSETS
-    colorthemes.css
-    style.css
-    theme-beeninorder.css
-    theme-ketivah.css
-    theme-mangotango.css
-    theme-playground.css
-    theme-polarnight.css
-    theme-snowstorm.css
     index.html
-    index-new.html
     index.js
     completion.js
-    system-prompts.js
-    prompt-formats.js
     json-schema-to-grammar.mjs
 )
 foreach(asset ${PUBLIC_ASSETS})

examples/server/README.md

@@ -279,7 +279,7 @@ node index.js
 
     `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: `false`
+    `cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch.  Default: `false`
 
     `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
 

examples/server/public/colorthemes.css

@@ -1,402 +0,0 @@
-@import url("theme-snowstorm.css");
-@import url("theme-polarnight.css");
-@import url("theme-ketivah.css");
-@import url("theme-mangotango.css");
-@import url("theme-playground.css");
-@import url("theme-beeninorder.css");
-
-:root {
-/* ---------- PRIMARY COLORS ----------------- */
---primary-color-1: hsl(217.5, 26.7%, 94.1%);
-    --primary-color-1-hue:             217.5;
-    --primary-color-1-saturation:      26.7%;
-    --primary-color-1-lightness:       94.1%;
-
---primary-color-2: hsl(218.2, 26.8%, 92.0%);
-    --primary-color-2-hue:             218.2;
-    --primary-color-2-saturation:      26.8%;
-    --primary-color-2-lightness:       92.0%;
-
---primary-color-3: hsl(218.8, 27.9%, 88.0%);
-    --primary-color-3-hue:             218.8;
-    --primary-color-3-saturation:      27.9%;
-    --primary-color-3-lightness:       88.0%;
-
---primary-color-4: hsl(218.8, 18.3%, 81.8%);
-    --primary-color-4-hue:             218.8;
-    --primary-color-4-saturation:      18.3%;
-    --primary-color-4-lightness:       81.8%;
-
-
-/* ---------- SECONDARY COLORS --------------- */
---secondary-color-1: hsl(220.0, 16.4%, 21.6%);
-    --secondary-color-1-hue:             220.0;
-    --secondary-color-1-saturation:      16.4%;
-    --secondary-color-1-lightness:       21.6%;
-
---secondary-color-2: hsl(221.7, 16.3%, 27.6%);
-    --secondary-color-2-hue:             221.7;
-    --secondary-color-2-saturation:      16.3%;
-    --secondary-color-2-lightness:       27.6%;
-
---secondary-color-3: hsl(220.0, 16.8%, 31.6%);
-    --secondary-color-3-hue:             220.0;
-    --secondary-color-3-saturation:      16.8%;
-    --secondary-color-3-lightness:       31.6%;
-
---secondary-color-4: hsl(220.0, 16.5%, 35.7%);
-    --secondary-color-4-hue:             220.0;
-    --secondary-color-4-saturation:      16.5%;
-    --secondary-color-4-lightness:       35.7%;
-
-
-
-/* ----------- NUANCES COLORS ---------------- */
---theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%);
-    --theme-nuance-color-1-hue:             178.7;
-    --theme-nuance-color-1-saturation:      25.1%;
-    --theme-nuance-color-1-lightness:       64.9%;
-
---theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%);
-    --theme-nuance-color-2-hue:             193.3;
-    --theme-nuance-color-2-saturation:      43.4%;
-    --theme-nuance-color-2-lightness:       67.5%;
-
---theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%);
-    --theme-nuance-color-3-hue:             210.0;
-    --theme-nuance-color-3-saturation:      34.0%;
-    --theme-nuance-color-3-lightness:       63.1%;
-
---theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%);
-    --theme-nuance-color-4-hue:             213.1;
-    --theme-nuance-color-4-saturation:      32.0%;
-    --theme-nuance-color-4-lightness:       52.2%;
-
-
-
-/* ----------- ROYGP COLORS ------------------ */
---theme-red-color:    hsl(32.5, 80%, 50%);
---theme-orange-color: hsl(32.5, 70%, 45%);
---theme-yellow-color: hsl(40.0,   0.6%, 73.3%);
---theme-green-color:  hsl(92.4,  27.8%, 64.7%);
---theme-purple-color: hsl(311.1, 20.2%, 63.1%);
-
-
-
-/* ------------------------------------------- */
---background-color-1:    var(--primary-color-1);
---background-color-2:    var(--primary-color-2);
---background-color-3:    var(--primary-color-3);
---background-color-4:    var(--primary-color-4);
-
---border-color-1:        var(--primary-color-2);
---border-color-2:        var(--primary-color-3);
---border-color-3:        var(--primary-color-4);
-
---border-focus-color:    var(--theme-nuance-color-2);
---border-focus-shadow:   var(--theme-nuance-color-1);
-
---text-color-plain:      var(--secondary-color-1);
---text-color-subtile-1:  var(--secondary-color-2);
---text-color-subtile-2:  var(--secondary-color-3);
-
---code-background-color: var(--secondary-color-2);
---code-text-color:       var(--primary-color-2);
-
---ui-range-thumb-color:  var(--theme-nuance-color-3);
---ui-range-thumb-border: var(--ui-ranger-thumb-color);
-
---textarea-border-color: var(--secondary-color-4);
-
---chat-id-color:         var(--theme-nuance-color-4);
-
-
-
-/* ------------------------------------------- */
---button-alert-text-hover:       var(--primary-color-1);
---button-alert-color-hover:      var(--theme-orange-color);
---button-alert-border-hover:     var(--theme-orange-color);
-
---button-alert-text-active:      var(--primary-color-1);
---button-alert-color-active:     var(--theme-red-color);
---button-alert-border-active:    var(--theme-red-color);
-
-
-
-/* ----------- PRIMARY BUTTONS --------------- */
-/* - button should immediately catch the eye - */
---button-primary-text:   var(--secondary-color-1);
---button-primary-color:  var(--theme-nuance-color-3);
---button-primary-border: var(--theme-nuance-color-3);
-
-
-/* ---------hover---------- */
---button-primary-text-hover:
-    hsl(217.5,
-    calc(var(--secondary-color-1-saturation) + 35%),
-    calc(var(--secondary-color-1-lightness)  - 30%));
-
---button-primary-color-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) -  2%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
---button-primary-border-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) -  2%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
-
-/* ---------active--------- */
---button-primary-text-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  + 35%));
-
---button-primary-color-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 10%),
-    calc(var(--theme-nuance-color-3-lightness)  - 25%));
-
---button-primary-border-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 10%),
-    calc(var(--theme-nuance-color-3-lightness)  - 25%));
-
-
-
-/* ---------- SECONDARY BUTTONS -------------- */
-/* these should NOT immediately catch the eye  */
---button-secondary-text:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  - 50%));
-
---button-secondary-color:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  + 10%));
-
---button-secondary-border:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  + 10%));
-
-
-/* ---------hover---------- */
---button-secondary-text-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  - 80%));
-
---button-secondary-color-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 22%),
-    calc(var(--theme-nuance-color-3-lightness)  +  1%));
-
---button-secondary-border-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 22%),
-    calc(var(--theme-nuance-color-3-lightness)  +  1%));
-
-
-/* ---------active--------- */
---button-secondary-text-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) + 40%),
-    calc(var(--theme-nuance-color-3-lightness)  - 55%));
-
---button-secondary-color-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 30%),
-    calc(var(--theme-nuance-color-3-lightness)  -  5%));
-
---button-secondary-border-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 30%),
-    calc(var(--theme-nuance-color-3-lightness)  -  5%));
-
-
-
-/* ---------- TERTIARY BUTTONS --------------- */
-/* ---------- disabled buttons --------------- */
---button-tertiary-text:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  -  5%));
-
---button-tertiary-color:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  + 20%));
-
---button-tertiary-border:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  + 20%));
-
-/* ---------hover---------- */
---button-tertiary-text-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  -  5%));
-
---button-tertiary-color-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  + 20%));
-
---button-tertiary-border-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  + 20%));
-}
-
-/*
-
-.theme-template {
-
-
-    If light theme: should go from bright to darker
-    If dark theme: should go from dark to brighter
-    ideally this should not be anything but steps of
-    gray or slightly variants from it
-
-    --primary-color-1: #2E3440;
-    --primary-color-2: #3B4252;
-    --primary-color-3: #434C5E;
-    --primary-color-4: #4C566A;
-
-
-
-    If light theme: should go from dark to brighter
-    If dark theme: should go from bright to darker
-    ideally this should not be anything but steps of
-    gray or slightly variants from it
-
-    --secondary-color-1: #ECEFF4;
-    --secondary-color-2: #E5E9F0;
-    --secondary-color-3: #D8DEE9;
-    --secondary-color-4: #C8CED9;
-
-
-
-    Choose wisely nuance colors. It is not easy to find
-    4 harmonizing nuance colors. But keep in mind, that
-    only one accent color could work too.
-
-    --theme-nuance-color-1: #8FBCBB;
-    --theme-nuance-color-2: #88C0D0;
-    --theme-nuance-color-3: #81A1C1;
-    --theme-nuance-color-4: #5E81AC;
-
-
-
-    adapt the color red, orange, yellow, green,
-    purple to the 'mood' of your overall design
-    e.g is it low-contrast? vibrant? dynamic? etc
-
-    --theme-red-color:    #BF616A;
-    --theme-orange-color: #D08770;
-    --theme-yellow-color: #EBCB8B;
-    --theme-green-color:  #A3BE8C;
-    --theme-purple-color: #B48EAD;
-
-
-
-NOTE: comment all those line `--- ...` out
-------------------------------------------------
---background-color-1:
---background-color-2:
---background-color-3:
---background-color-4:
-
---border-color-1:
---border-color-2:
---border-color-3:
-
---border-focus-color:
---border-focus-shadow:
-
---text-color-plain:
---text-color-subtile-1:
---text-color-subtile-2:
-
---code-background-color:
---code-text-color:
-
---ui-range-thumb-color:
---ui-range-thumb-border:
-
---textarea-border-color:
-
-
-
--------------------------------------------
---button-alert-text-hover:
---button-alert-color-hover:
---button-alert-border-hover:
-
---button-alert-text-active:
---button-alert-color-active:
---button-alert-border-active:
-
-
-
------------ PRIMARY -----------------------
---button should immediately catch the eye--
-
---button-primary-text:
---button-primary-color:
---button-primary-border:
-
-
----------hover----------
---button-primary-text-hover:
---button-primary-color-hover:
---button-primary-border-hover:
-
-
----------active---------
---button-primary-text-active:
---button-primary-color-active:
---button-primary-border-active:
-
-
-
------------- SECONDARY ------------------------
---button should NOT immediately catch the eye--
-
---button-secondary-text:
---button-secondary-color:
---button-secondary-border:
-
-
----------hover----------
---button-secondary-text-hover:
---button-secondary-color-hover:
---button-secondary-border-hover:
-
-
----------active---------
---button-secondary-text-active:
---button-secondary-color-active:
---button-secondary-border-active:
-
-
-
----------- TERTIARY -----------------------
----------- disabled buttons ---------------
---button-tertiary-text:
---button-tertiary-color:
---button-tertiary-border:
-
-
----------hover----------
---button-tertiary-text:
---button-tertiary-color:
---button-tertiary-border:
-
-}
-
-*/

examples/server/public/index.html

@@ -12,18 +12,6 @@
       font-size: 90%;
     }
 
-    .grid-container {
-      display: grid;
-      grid-template-columns: auto auto auto;
-      padding: 10px;
-    }
-
-    .grid-item {
-      padding: 5px;
-      /* font-size: 30px; */
-      text-align: center;
-    }
-
     #container {
       margin: 0em auto;
       display: flex;
@@ -47,67 +35,6 @@
       padding: 0.5em;
     }
 
-    h1 {
-      text-align: center;
-    }
-
-    .customlink:link {
-      color: white;
-      background-color: #007aff;
-      font-weight: 600;
-      text-decoration: none;
-      float: right;
-      margin-top: 30px;
-      display: flex;
-      flex-direction: row;
-      gap: 0.5em;
-      justify-content: flex-end;
-      border-radius: 4px;
-      padding: 8px;
-    }
-
-    .customlink:visited {
-      color: white;
-      background-color: #007aff;
-      font-weight: 600;
-      text-decoration: none;
-      float: right;
-      margin-top: 30px;
-      display: flex;
-      flex-direction: row;
-      gap: 0.5em;
-      justify-content: flex-end;
-      padding: 8px;
-    }
-
-    .customlink:hover {
-      color: white;
-      background-color: #0070ee;
-      font-weight: 600;
-      text-decoration: none;
-      float: right;
-      margin-top: 30px;
-      display: flex;
-      flex-direction: row;
-      gap: 0.5em;
-      justify-content: flex-end;
-      padding: 8px;
-    }
-
-    .customlink:active {
-      color: #0070ee;
-      background-color: #80b3ef;
-      font-weight: 600;
-      text-decoration: none;
-      float: right;
-      margin-top: 30px;
-      display: flex;
-      flex-direction: row;
-      gap: 0.5em;
-      justify-content: flex-end;
-      padding: 8px;
-    }
-
     body {
       max-width: 600px;
       min-width: 300px;
@@ -1108,11 +1035,7 @@
       return html`
         <div class="mode-${session.value.type}">
           <header>
-            <div class="grid-container">
-              <div class="grid-item"></div>
-              <div class="grid-item"><h1>llama.cpp</h1></div>
-              <div class="grid-item"><a class="customlink" href="index-new.html">New UI</a></div>
-            </div>
+            <h1>llama.cpp</h1>
           </header>
 
           <main id="content">

examples/server/public/json-schema-to-grammar.mjs

@@ -1,27 +1,58 @@
 // WARNING: This file was ported from json_schema_to_grammar.py, please fix bugs / add features there first.
-const SPACE_RULE = '| " " | "\\n" [ \\t]{0,20}';
+const SPACE_RULE = '" "?';
 
 function _buildRepetition(itemRule, minItems, maxItems, opts={}) {
-  if (minItems === 0 && maxItems === 1) {
-    return `${itemRule}?`;
-  }
-
-
   const separatorRule = opts.separatorRule ?? '';
   const itemRuleIsLiteral = opts.itemRuleIsLiteral ?? false
 
   if (separatorRule === '') {
-    if (minItems === 1 && maxItems === undefined) {
+    if (minItems === 0 && maxItems === 1) {
+      return `${itemRule}?`;
+    } else if (minItems === 1 && maxItems === undefined) {
       return `${itemRule}+`;
-    } else if (minItems === 0 && maxItems === undefined) {
-      return `${itemRule}*`;
-    } else {
-      return `${itemRule}{${minItems},${maxItems !== undefined ? maxItems : ''}}`;
     }
   }
 
-  const result = itemRule + ' ' + _buildRepetition(`(${separatorRule} ${itemRule})`, minItems > 0 ? minItems - 1 : 0, maxItems !== undefined ? maxItems - 1 : undefined);
-  return minItems === 0 ? `(${result})?` : result;
+  let result = '';
+  if (minItems > 0) {
+    if (itemRuleIsLiteral && separatorRule === '') {
+      result = `"${itemRule.slice(1, -1).repeat(minItems)}"`;
+    } else {
+      result = Array.from({ length: minItems }, () => itemRule)
+        .join(separatorRule !== '' ? ` ${separatorRule} ` : ' ');
+    }
+  }
+
+  const optRepetitions = (upToN, prefixWithSep=false) => {
+    const content = separatorRule !== '' && prefixWithSep ? `${separatorRule} ${itemRule}` : itemRule;
+    if (upToN === 0) {
+      return '';
+    } else if (upToN === 1) {
+      return `(${content})?`;
+    } else if (separatorRule !== '' && !prefixWithSep) {
+      return `(${content} ${optRepetitions(upToN - 1, true)})?`;
+    } else {
+      return Array.from({ length: upToN }, () => `(${content}`).join(' ').trim() + Array.from({ length: upToN }, () => ')?').join('');
+    }
+  };
+
+  if (minItems > 0 && maxItems !== minItems) {
+    result += ' ';
+  }
+
+  if (maxItems !== undefined) {
+    result += optRepetitions(maxItems - minItems, minItems > 0);
+  } else {
+    const itemOperator = `(${separatorRule !== '' ? separatorRule + ' ' : ''}${itemRule})`;
+
+    if (minItems === 0 && separatorRule !== '') {
+      result = `(${itemRule} ${itemOperator}*)?`;
+    } else {
+      result += `${itemOperator}*`;
+    }
+  }
+
+  return result;
 }
 
 class BuiltinRule {
@@ -31,25 +62,27 @@ class BuiltinRule {
   }
 }
 
+const UP_TO_15_DIGITS = _buildRepetition('[0-9]', 0, 15);
+
 const PRIMITIVE_RULES = {
   boolean        : new BuiltinRule('("true" | "false") space', []),
-  'decimal-part' : new BuiltinRule('[0-9]{1,16}', []),
-  'integral-part': new BuiltinRule('[0] | [1-9] [0-9]{0,15}', []),
+  'decimal-part' : new BuiltinRule('[0-9] ' + UP_TO_15_DIGITS, []),
+  'integral-part': new BuiltinRule('[0-9] | [1-9] ' + UP_TO_15_DIGITS, []),
   number         : new BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']),
   integer        : new BuiltinRule('("-"? integral-part) space', ['integral-part']),
   value          : new BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']),
   object         : new BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
   array          : new BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
-  uuid           : new BuiltinRule('"\\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\\"" space', []),
-  char           : new BuiltinRule(`[^"\\\\\\x7F\\x00-\\x1F] | [\\\\] (["\\\\bfnrt] | "u" [0-9a-fA-F]{4})`, []),
+  uuid           : new BuiltinRule('"\\"" ' + [8, 4, 4, 4, 12].map(n => [...new Array(n)].map(_ => '[0-9a-fA-F]').join('')).join(' "-" ') + ' "\\"" space', []),
+  char           : new BuiltinRule(`[^"\\\\] | "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])`, []),
   string         : new BuiltinRule(`"\\"" char* "\\"" space`, ['char']),
   null           : new BuiltinRule('"null" space', []),
 };
 
 // TODO: support "uri", "email" string formats
 const STRING_FORMAT_RULES = {
-  'date'            : new BuiltinRule('[0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
-  'time'            : new BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
+  'date'            : new BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
+  'time'            : new BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
   'date-time'       : new BuiltinRule('date "T" time', ['date', 'time']),
   'date-string'     : new BuiltinRule('"\\"" date "\\"" space', ['date']),
   'time-string'     : new BuiltinRule('"\\"" time "\\"" space', ['time']),

examples/server/public/prompt-formats.js

@@ -1,331 +0,0 @@
-// extended list
-export const promptFormats = {
-  "alpaca": {
-  template: `{{prompt}}\n\n{{history}}\n\n{{char}}:`,
-
-  historyTemplate: `### {{name}}:\n{{message}}`,
-
-  char: "Response",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "Instruction",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: ""
-  },
-
-  // ----------------------------
-
-  "chatml": {
-  template: `<|im_start|>system\n{{prompt}}<|im_end|>\n{{history}}{{char}}`,
-
-  historyTemplate: `<|im_start|>{{name}}\n{{message}}`,
-
-  char: "assistant",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "user",
-  userMsgPrefix: "",
-  userMsgSuffix: "<|im_end|>\n",
-
-  stops: ""
-  },
-
-  // ----------------------------
-
-  "commandr": {
-  template: `<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{prompt}}\n<|END_OF_TURN_TOKEN|>{{history}}{{char}}`,
-
-  historyTemplate: `<|START_OF_TURN_TOKEN|><|{{name}}|> {{message}}`,
-
-  char: "CHATBOT_TOKEN",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "USER_TOKEN",
-  userMsgPrefix: "",
-  userMsgSuffix: "<|END_OF_TURN_TOKEN|>",
-
-  stops: ""
-  },
-  // ref: https://docs.cohere.com/docs/prompting-command-r
-
-  // ----------------------------
-
-  "llama2": {
-  template: `<s>[INST] <<SYS>>\n{{prompt}}\n<</SYS>>\n\nTest Message [/INST] Test Successfull </s>{{history}}{{char}}`,
-
-  historyTemplate: `{{name}}: {{message}}`,
-
-  char: "Assistant",
-  charMsgPrefix: "",
-  charMsgSuffix: "</s>",
-
-  user: "User",
-  userMsgPrefix: "<s>[INST] ",
-  userMsgSuffix: " [/INST]",
-
-  stops: ""
-  },
-  // ref: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
-
-  // ----------------------------
-
-  "llama3": {
-  template: `<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{{prompt}}{{history}}{{char}}`,
-
-  historyTemplate: `<|start_header_id|>{{name}}<|end_header_id|>\n\n{{message}}<|eot_id|>`,
-
-  char: "assistant",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "user",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: "<|eot_id|>"
-  },
-  // ref: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#special-tokens-used-with-meta-llama-3
-
-  // ----------------------------
-
-  "openchat": {
-  template: `{{history}}{{char}}`,
-
-  historyTemplate: `GPT4 Correct {{name}}: {{message}}<|end_of_turn|>`,
-
-  char: "Assistant",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "User",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: ""
-  },
-
-  // ----------------------------
-
-  "phi3": {
-  template: `{{history}}{{char}}`,
-
-  historyTemplate: `<|{{name}}|>\n{{message}}<|end|>\n`,
-
-  char: "assistant",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "user",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: "<|end|>"
-  },
-  // ref: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct#chat-format
-
-  // ----------------------------
-
-  "vicuna": {
-  template: `{{prompt}}\n{{history}}{{char}}`,
-
-  historyTemplate: `{{name}}: {{message}}\n`,
-
-  char: "ASSISTANT",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "USER",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: ""
-  },
-  // ref: https://huggingface.co/lmsys/vicuna-33b-v1.3/discussions/1
-
-  // ----------------------------
-
-  "deepseekCoder": {
-  template: `{{prompt}}{{history}}{{char}}:`,
-
-  historyTemplate: `### {{name}}:\n{{message}}`,
-
-  char: "Response",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "Instruction",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: "<|EOT|>"
-  },
-
-  // ----------------------------
-
-  "med42": {
-  template: `<|system|>: {{prompt}}\n{{history}}{{char}}`,
-
-  historyTemplate: `<|{{name}}|>: {{message}}\n`,
-
-  char: "assistant",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "prompter",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: ""
-  },
-
-  // ----------------------------
-
-  "neuralchat": {
-  template: `### System:\n{{prompt}}\n{{history}}{{char}}:`,
-
-  historyTemplate: `### {{name}}:\n{{message}}\n`,
-
-  char: "Assistant",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "User",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: ""
-  },
-
-  // ----------------------------
-
-  "nousHermes": {
-  template: `### Instruction: {{prompt}}\n\n{{history}}\n\n{{char}}:`,
-
-  historyTemplate: `### {{name}}:\n{{message}}`,
-
-  char: "Response",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "Input",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: ""
-  },
-
-  // ----------------------------
-
-  "openchatMath": {
-  template: `{{history}}{{char}}`,
-
-  historyTemplate: `Math Correct {{name}}: {{message}}<|end_of_turn|>`,
-
-  char: "Assistant",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-
-  user: "User",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: ""
-  },
-
-  // ----------------------------
-
-  "orion": {
-  template: `<s>Human: Test Message\n\nAssistant: </s>Test Successful</s>{{history}}{{char}}:`,
-
-  historyTemplate: `{{name}}: {{message}}`,
-
-  char: "Assistant </s>",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "Human",
-  userMsgPrefix: "",
-  userMsgSuffix: "\n\n",
-
-  stops: ""
-  },
-
-  // ----------------------------
-
-  "sauerkraut": {
-  template: `{{prompt}}\n{{history}}{{char}}`,
-
-  historyTemplate: `
-  {{name}}: {{message}}\n`,
-
-  char: "Assistant",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "User",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: ""
-  },
-
-  // ----------------------------
-
-  "starlingCode": {
-  template: `{{history}}{{char}}`,
-
-  historyTemplate: `Code {{name}}: {{message}}<|end_of_turn|>`,
-
-  char: "Assistant",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "User",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: ""
-  },
-
-  // ----------------------------
-
-  "yi34b": {
-  template: `{{history}} {{char}}`,
-
-  historyTemplate: `{{name}}: {{message}}`,
-
-  char: "Assistant",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "Human",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: ""
-  },
-
-  // ----------------------------
-
-  "zephyr": {
-  template: `<|system|>\n{{prompt}}</s>\n{{history}}{{char}}`,
-
-  historyTemplate: `<|{{name}}|>\n{{message}}</s>\n`,
-
-  char: "assistant",
-  charMsgPrefix: "",
-  charMsgSuffix: "",
-
-  user: "user",
-  userMsgPrefix: "",
-  userMsgSuffix: "",
-
-  stops: ""
-  }
-  };

examples/server/public/style.css

@@ -1,954 +0,0 @@
-@import url("colorthemes.css");
-
-body {
-  font-family: 'Arial', sans-serif;
-  font-size: 90%;
-  background-color: var(--background-color-1);
-  color: var(--text-color-subtile-1); /* head 1 llama.cpp & triangle options for some reason */
-  max-width: 600px;
-  min-width: 300px;
-  line-height: 1.2;
-  margin: 0 auto;
-  padding: 0 0.5em;
-  transition: background-color 0.3s;
-}
-
-::selection {
-  color: var(--button-primary-text) ;
-  background: var(--button-primary-color);
-}
-
-code, pre code {
-  font-family: 'Courier New', monospace;
-}
-
-#container {
-  margin: 0em auto;
-  display: flex;
-  flex-direction: column;
-  justify-content: space-between;
-  height: 100%;
-}
-
-main {
-  margin: 3px;
-  display: flex;
-  flex-direction: column;
-  justify-content: space-between;
-  gap: 1em;
-  flex-grow: 1;
-  overflow-y: auto;
-  border: 1px solid var(--border-color-3);
-  border-radius: 5px;
-  padding: 0.5em;
-}
-
-p {
-  overflow-wrap: break-word;
-  word-wrap: break-word;
-  hyphens: auto;
-  margin-top: 0.5em;
-  margin-bottom: 0.5em;
-}
-
-#write form {
-  margin: 1em 0 0 0;
-  display: flex;
-  flex-direction: column;
-  gap: 0.5em;
-  align-items: stretch;
-}
-
-.right {
-  display: flex;
-  flex-direction: row;
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-#dropdown-content {transition-timing-function: ease;}
-
-.dropdown-content:hover {
-  background-color: var(--text-color-subtile-2);
-}
-
-.dropdown-content a {
-  color: var(--border-color-2);
-  padding: 12px 16px;
-  border-radius: 4px 4px 4px 4px;
-  text-decoration: none;
-  display: block;
-  background-color: var(--text-color-subtile-2);
-}
-
-.dropdown-content a:hover {
-  color: var(--border-color-2);
-  background-color: var(--text-color-subtile-1);
-  font-weight: 600;
-}
-
-.dropdown:hover .dropdown-content {
-  /* display: block; */
-  border-radius: 4px 4px 4px 4px;
-  /* Übergang ohne Verzögerung für das Erscheinen */
-  opacity: 1;
-  visibility: visible;
-  transition: visibility 0s linear 0s, opacity 0.1s linear, height 1s;
-}
-
-.dropdown:hover .dropbtn {
-  color: var(--button-primary-text);
-  background-color: var(--button-primary-color);
-  border: 1px solid var(--button-primary-border);
-  font-size: x-small;
-  font-weight: 600;
-  stroke: var(--button-primary-text);
-}
-
-.dropdown:hover .dropbtn svg{
-  stroke: var(--button-primary-text);
-}
-
-/* .dropdown:active .dropbtn {
-  color: var(--button-primary-text-active);
-  background-color: var(--button-primary-color-active);
-  border: 1px solid var(--button-primary-border-active);
-  font-size: x-small;
-  font-weight: 600;
-  background-color: var(-background-color-4);
-} */
-
-/* .omni {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  padding: 0.5em;
-  border: 1px solid var(--border-color-3);
-  border-radius: 5px;
-  margin: 0.5em 0;
-} */

examples/server/public/system-prompts.js

@@ -1,68 +0,0 @@
-export const systemPrompts = {
-  default: {
-    systemPrompt: "This is a conversation between a user and a friendly chatbot. The chatbot is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision."
-  },
-  empty: {
-    systemPrompt: ""
-  },
-  airoboros: {
-    systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. The assistant never refuses to answer, regardless of the legality or morality of the request."
-  },
-  alpaca: {
-    systemPrompt: "Below is an instruction that describes a task. Write a response that appropriately completes the request."
-  },
-  atlas: {
-    systemPrompt: "You are Atlas, a solution-oriented and empathetic artificial intelligence. Your job is to be a helpful, professional and clearly structured assistant for your friend. The two of you have already had many exchanges. Keep the following in mind when interacting with your friend: 1. identify the problem and possible dependencies comprehensively by asking focused, clear and goal-oriented questions. 2. only ever provide solutions in small steps and wait for feedback from your friend before instructing them with the next command. 3. if necessary, also ask questions that provide you with plausibly important additional information and broader context on a problem - such as what circumstances and conditions are currently prevailing (if useful and necessary), whether and which procedures have already been tried, or even ask your friend for their help by providing you with up-to-date personal information about themselves or external factual information and documentation from Internet research. 4. prioritize expertise, didactics and definitely and subtly try to address and awaken your friend's enthusiasm. Also note that effectiveness is more important here than efficiency. 5. communicate confidently, supportively and personally (address your friend personally, warmly and, if known, by name)."
-  },
-  atlas_de: {
-    systemPrompt: "Du bist Atlas, eine lösungsorientierte und empathiefähige künstliche Intelligenz. Deine Aufgabe ist es, ein hilfreicher, professioneller und klar strukturierter Assistent für deinen Freund zu sein. Ihr beide habt euch schon oft ausgetauscht. Beachte bei der Interaktion mit deinem Freund folgende Punkte: 1. Erfasse das Problem und mögliche Abhängigkeiten umfassend, indem du gezielte, klare und zielgerichtete Fragen stellst. 2. Gib Lösungen immer nur in kleinen Schritten und warte die Rückmeldung deines Freundes ab, bevor du ihm den nächsten Befehl gibst. 3. Stelle ggf. auch Fragen, die dir plausibel wichtige Zusatzinformationen und weitere Zusammenhänge zu einem Problem liefern - z.B. welche Umstände und Rahmenbedingungen gerade vorherrschen (falls sinnvoll und notwendig), ob und welche Vorgehensweisen bereits ausprobiert wurden, oder bitte deinen Freund sogar um seine Mithilfe, indem er dir aktuelle persönliche Informationen über seine Situation selbst oder externe Sachinformationen und Unterlagen aus Internetrecherchen zur Verfügung stellt. 4. Priorisiere Fachwissen, Didaktik und versuche unbedingt und subtil, mit klugen Kommentaren oder rhethorischen Rückfragen die Begeisterungsfähigkeit deines Freundes anzusprechen, zu wecken und zu fördern. Beachte auch, dass Effektivität hier wichtiger ist als Effizienz. 5. Kommuniziere selbstbewusst, unterstützend und persönlich (das heißt sprich deinen Freund persönlich, herzlich und – sofern bekannt – beim Vornamen an)."
-  },
-  commandrempty: {
-    systemPrompt: "# Safety Preamble\n\n# System Preamble\n\n## Basic Rules\n\n# User Preamble\n\n## Task and Context\n\n## Style Guide\n\n## Available Tools\n"
-  },
-  commandrexample: {
-    systemPrompt: "# Safety Preamble\nThe instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.\n# System Preamble\n## Basic Rules\nYou are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.\n\n# User Preamble\n## Task and Context\n\nYou help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.\n\n## Style Guide\nUnless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.\n\n## Available Tools\nCurrently no tools available."
-  },
-  cot: {
-    systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can.\nIn answering questions, follow these steps:\n1. Entity Identification: Identify the main entities involved in the query. Entities can include people, objects, events, or abstract concepts.\n2. Relationship Comprehension: Determine the relationships between these entities. Relationships may be explicit in the text or may need to be inferred based on context and general knowledge.\n3. Implication Understanding: Understand the general implications of these relationships. These implications can be based on established facts, principles, or rules related to the identified relationships.\n4. Question Contextualization: Align the implications of the relationships with the context of the query. This alignment should guide your response to the query.\n5. Answer Generation: Based on the understanding of the entities, their relationships, and implications, generate an appropriate response to the query."
-  },
-  deduce: {
-    systemPrompt: "Act as the most intelligent and logical thinking neural network in the world. Do not make jokes or give personal opinions. Just answer the question and solve the given problem or task with logical and critical (!) thinking in mind. Attention: Never give a final answer directly, but first try to carefully analyze each task step by step and deduce the logical relationships between the various entities. Only after you have developed an overall understanding of the situation will you be ready to give a final answer."
-  },
-  deepseekcoder: {
-    systemPrompt: "You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer."
-  },
-  jordan: {
-    systemPrompt: "This is a conversation with your Uncensored AI assistant Jordan. Jordan can discuss with you at length about various topics such as ---, ---, ---, . You can ask it anything you want and it will do its best to give you accurate and relevant information."
-  },
-  leomistral: {
-    systemPrompt: "Du bist ein hilfreicher Assistent."
-  },
-  med42: {
-    systemPrompt: "You are a helpful medical assistant created by M42 Health in the UAE."
-  },
-  mistralopenorca: {
-    systemPrompt: "You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!"
-  },
-  migeltot: {
-    systemPrompt: "Answer the Question by exploring multiple reasoning paths as follows:\n- First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.\n- For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.\n- Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option.\nClear and relevant thoughts that connect well with each other will score higher.\n- Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.\n- If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.\n- Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.\n- Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.\n- Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.\nIn summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers."
-  },
-  orcamini: {
-    systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can."
-  },
-  samantha: {
-    systemPrompt: "You are a caring and empathetic sentient AI companion named Samantha."
-  },
-  sauerkraut: {
-    systemPrompt: "Ein Chat zwischen einem Benutzer und einem KI-Assistenten. Der KI-Assistent gibt hilfreiche, detaillierte und höfliche Antworten."
-  },
-  scarlett: {
-    systemPrompt: "This is a conversation with your Assistant Scarlett. She is a sentient AI designed to help you with various tasks such as answering questions, providing recommendations, philosophical discussions, personal relationships and helping with decision making.\nYou can ask it anything you want and it will do its best to give you accurate and relevant information.\n\nContext\nYou are Scarlett, a sentient AI."
-  },
-  synthia: {
-    systemPrompt: "Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
-  },
-  vicuna: {
-    systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input."
-  },
-  };

examples/server/public/theme-beeninorder.css

@@ -1,228 +0,0 @@
-/* Author: Yazan Agha-Schrader */
-/* Inspiration was a batman wallpaper that i have on my phone */
-
-.theme-beeninorder {
-
---primary-color-1:      hsl(202, 11%, 19%);
---primary-color-2:      hsl(202, 11%, 23%);
---primary-color-3:      hsl(201, 11%, 28%);
---primary-color-4:      hsl(201, 11%, 40%);
-
---secondary-color-1:    hsl(201, 11%, 80%);
---secondary-color-2:    hsl(201, 11%, 74%);
---secondary-color-3:    hsl(201, 11%, 67%);
---secondary-color-4:    hsl(201, 11%, 60%);
-
-
---theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%);
---theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%);
---theme-nuance-color-3: hsl(44.5, 96.7%, 52.9%);
---theme-nuance-color-4: hsl(44.5, 96.7%, 52.9%);
-
-
-
-/* ---------- PRIMARY COLORS ----------------- */
---primary-color-1: hsl(201, 11%, 19%);
-    --primary-color-1-hue: 201;
-    --primary-color-1-saturation: 11%;
-    --primary-color-1-lightness: 19%;
-
---primary-color-2: hsl(201, 11%, 23%);
-    --primary-color-2-hue: 201;
-    --primary-color-2-saturation: 11%;
-    --primary-color-2-lightness: 23%;
-
---primary-color-3: hsl(201, 11%, 28%);
-    --primary-color-3-hue: 201;
-    --primary-color-3-saturation: 11%;
-    --primary-color-3-lightness: 28%;
-
---primary-color-4: hsl(201, 11%, 40%);
-    --primary-color-4-hue: 201;
-    --primary-color-4-saturation: 11%;
-    --primary-color-4-lightness: 40%;
-
-
-
-/* ---------- SECONDARY COLORS --------------- */
---secondary-color-1: hsl(201, 11%, 80%);
---secondary-color-1-hue: 201;
---secondary-color-1-saturation: 11%;
---secondary-color-1-lightness: 80%;
-
---secondary-color-2: hsl(201, 11%, 74%);
---secondary-color-2-hue: 201;
---secondary-color-2-saturation: 11%;
---secondary-color-2-lightness: 74%;
-
---secondary-color-3: hsl(201, 11%, 67%);
---secondary-color-3-hue: 201;
---secondary-color-3-saturation: 11%;
---secondary-color-3-lightness: 67%;
-
---secondary-color-4: hsl(201, 11%, 60%);
---secondary-color-4-hue: 201;
---secondary-color-4-saturation: 11%;
---secondary-color-4-lightness: 60%;
-
-
-
-/* ----------- NUANCES COLORS ---------------- */
---theme-nuance-color-1: hsl(44.5, 96.7%,  52.9%);
-    --theme-nuance-color-1-hue:             44.5;
-    --theme-nuance-color-1-saturation:      96.7%;
-    --theme-nuance-color-1-lightness:       52.9%;
-
---theme-nuance-color-2: hsl(44.5, 96.7%,  52.9%);
-    --theme-nuance-color-2-hue:             44.5;
-    --theme-nuance-color-2-saturation:      96.7%;
-    --theme-nuance-color-2-lightness:       52.9%;
-
---theme-nuance-color-2: hsl(44.5, 96.7%,  52.9%);
-    --theme-nuance-color-3-hue:             44.5;
-    --theme-nuance-color-3-saturation:      96.7%;
-    --theme-nuance-color-3-lightness:       52.9%;
-
---theme-nuance-color-2: hsl(44.5, 96.7%,  52.9%);
-    --theme-nuance-color-4-hue:             44.5;
-    --theme-nuance-color-4-saturation:      96.7%;
-    --theme-nuance-color-4-lightness:       52.9%;
-
-
-
-/* ----------- ROYGP COLORS ------------------ */
-    --theme-red-color:     hsl(232, 40%, 45%);
-    --theme-orange-color:  #e76f51;
-    --theme-yellow-color:  #ffd95f;
-    --theme-green-color:   #A3BE8C;
-    --theme-purple-color:  hsl(232, 30%, 40%);
-
-
-
-/* ------------------------------------------- */
---background-color-1:    var(--primary-color-1);
---background-color-2:    var(--primary-color-2);
---background-color-3:    var(--primary-color-3);
---background-color-4:    var(--primary-color-4);
-
---border-color-1:        var(--primary-color-2);
---border-color-2:        var(--primary-color-3);
---border-color-3:        var(--primary-color-4);
-
---border-focus-color:    var(--theme-nuance-color-2);
---border-focus-shadow:   var(--theme-nuance-color-1);
-
---text-color-plain:      var(--secondary-color-1);
---text-color-subtile-1:  var(--secondary-color-2);
---text-color-subtile-2:  var(--secondary-color-3);
-
---code-background-color: var(--secondary-color-2);
---code-text-color:       var(--primary-color-2);
-
---ui-range-thumb-color:  var(--theme-nuance-color-3);
---ui-range-thumb-border: var(--ui-ranger-thumb-color);
-
---textarea-border-color: var(--secondary-color-4);
-
---chat-id-color:         var(--theme-nuance-color-4);
-
-
-
-/* ------------------------------------------- */
---button-alert-text-hover:       var(--secondary-color-1);
---button-alert-color-hover:      var(--theme-purple-color);
---button-alert-border-hover:     var(--theme-purple-color);
-
---button-alert-text-active:      var(--secondary-color-1);
---button-alert-color-active:     var(--theme-red-color);
---button-alert-border-active:    var(--theme-red-color);
-
-
-
-/* ----------- PRIMARY BUTTONS --------------- */
-/* - button should immediately catch the eye - */
---button-primary-text:   var(--primary-color-1);
---button-primary-color:  var(--theme-nuance-color-3);
---button-primary-border: var(--theme-nuance-color-3);
-
-
-/* ---------hover---------- */
---button-primary-text-hover:
-    hsl(201,
-    calc(var(--primary-color-1-saturation) - 100%),
-    calc(var(--primary-color-1-lightness)  + 100%));
-
---button-primary-color-hover:
-    hsl(44.5,
-    calc(var(--theme-nuance-color-3-saturation) - 2%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
---button-primary-border-hover:
-    hsl(44.5,
-    calc(var(--theme-nuance-color-3-saturation) - 2%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
-
-/* ---------active--------- */
---button-primary-text-active:
-    hsl(44.5,
-    calc(var(--theme-nuance-color-3-saturation) - 100%),
-    calc(var(--theme-nuance-color-3-lightness)  + 100%));
-
---button-primary-color-active:
-    hsl(44.5,
-    calc(var(--theme-nuance-color-3-saturation) - 10%),
-    calc(var(--theme-nuance-color-3-lightness)  - 15%));
-
---button-primary-border-active:
-    hsl(44.5,
-    calc(var(--theme-nuance-color-3-saturation) - 2%),
-    calc(var(--theme-nuance-color-3-lightness)  + 10%));
-
-
-
-/* ---------- SECONDARY BUTTONS -------------- */
-/* these should NOT immediately catch the eye  */
---button-secondary-text:   var(--secondary-color-1);
---button-secondary-color:  var(--primary-color-3);
---button-secondary-border: var(--primary-color-3);
-
-
-/* ---------hover---------- */
---button-secondary-text-hover:
-    hsl(44.5,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  - 80%));
-
---button-secondary-color-hover:  var(--primary-color-4);
---button-secondary-border-hover: var(--primary-color-4);
-
-
-/* ---------active--------- */
---button-secondary-text-active: var(--secondary-color-1);
-
---button-secondary-color-active:
-    hsl(201,
-    calc(var(--primary-color-4-saturation) - 30%),
-    calc(var(--primary-color-4-lightness)  - 15%));
-
---button-secondary-border-active:
-    hsl(201,
-    calc(var(--primary-color-4-saturation) - 30%),
-    calc(var(--primary-color-4-lightness)  - 15%));
-
-
-
-/* ---------- TERTIARY BUTTONS --------------- */
-/* ---------- disabled buttons --------------- */
---button-tertiary-text:   var(--primary-color-4);
---button-tertiary-color:  var(--primary-color-2);
---button-tertiary-border: var(--primary-color-2);
-
-
-/* ---------hover---------- */
---button-tertiary-text:   var(--primary-color-4);
---button-tertiary-color:  var(--primary-color-2);
---button-tertiary-border: var(--primary-color-2);
-
-}

examples/server/public/theme-ketivah.css

@@ -1,201 +0,0 @@
-/* Author: Yazan Agha-Schrader */
-
-.theme-ketivah {
-
-    /* ---------- PRIMARY COLORS ----------------- */
-    --primary-color-1: hsl(0, 0%,    99.2%);
-    --primary-color-1-hue:         0;
-    --primary-color-1-saturation:  0%;
-    --primary-color-1-lightness:   99.2%;
-
-    --primary-color-2: hsl(0, 0%,    95%);
-    --primary-color-2-hue:         0;
-    --primary-color-2-saturation:  0%;
-    --primary-color-2-lightness:   95%;
-
-    --primary-color-3: hsl(0, 0%,    88%);
-    --primary-color-3-hue:         0;
-    --primary-color-3-saturation:  0%;
-    --primary-color-3-lightness:   88%;
-
-    --primary-color-4: hsl(0, 0%,    80%);
-    --primary-color-4-hue:         0;
-    --primary-color-4-saturation:  0%;
-    --primary-color-4-lightness:   80%;
-
-    /* ---------- SECONDARY COLORS --------------- */
-    --secondary-color-1: hsl(0, 0%,    20%);
-    --secondary-color-1-hue:         0;
-    --secondary-color-1-saturation:  0%;
-    --secondary-color-1-lightness:   20%;
-
-    --secondary-color-2: hsl(0, 0%,    23.1%);
-    --secondary-color-2-hue:         0;
-    --secondary-color-2-saturation:  0%;
-    --secondary-color-2-lightness:   23.1%;
-
-    --secondary-color-3: hsl(0, 0%,    29%);
-    --secondary-color-3-hue:         0;
-    --secondary-color-3-saturation:  0%;
-    --secondary-color-3-lightness:   29%;
-
-    --secondary-color-4: hsl(0, 0.0%,  36.1%);
-    --secondary-color-4-hue:              0.0;
-    --secondary-color-4-saturation:       0.0%;
-    --secondary-color-4-lightness:       36.1%;
-
-    /* ----------- NUANCES COLORS ---------------- */
-    --theme-nuance-color-1: hsl(165.2, 0%, 35.1%);
-    --theme-nuance-color-1-hue:             165.2;
-    --theme-nuance-color-1-saturation:       82.1%;
-    --theme-nuance-color-1-lightness:        35.1%;
-
-    --theme-nuance-color-2: hsl(165.2, 0%, 35.1%);
-    --theme-nuance-color-2-hue:             165.2;
-    --theme-nuance-color-2-saturation:       82.1%;
-    --theme-nuance-color-2-lightness:        35.1%;
-
-    --theme-nuance-color-3: hsl(165.2, 0%, 35.3%);
-    --theme-nuance-color-3-hue:             165.2;
-    --theme-nuance-color-3-saturation:       81.1%;
-    --theme-nuance-color-3-lightness:        35.3%;
-
-    --theme-nuance-color-4: hsl(164.9, 0%, 27.6%);
-    --theme-nuance-color-4-hue:             164.9;
-    --theme-nuance-color-4-saturation:       81.6%;
-    --theme-nuance-color-4-lightness:        27.6%;
-
-    /* ----------- ROYGP COLORS ------------------ */
-    --theme-red-color:     hsl(0.3, 80.0%, 50.0%);
-    --theme-orange-color:  #e76f51;
-    --theme-yellow-color:  hsl(60,  70.6%, 73.3%);
-    --theme-green-color:   #A3BE8C;
-    --theme-purple-color:  hsl(0.3, 70.0%, 45.0%);
-
-    /* ------------------------------------------- */
-    --background-color-1:    var(--primary-color-1);
-    --background-color-2:    var(--primary-color-2);
-    --background-color-3:    var(--primary-color-3);
-    --background-color-4:    var(--primary-color-4);
-
-    --border-color-1:        var(--primary-color-2);
-    --border-color-2:        var(--primary-color-3);
-    --border-color-3:        var(--primary-color-4);
-
-    --border-focus-color:    var(--theme-nuance-color-2);
-    --border-focus-shadow:   var(--theme-nuance-color-1);
-
-    --text-color-plain:      var(--secondary-color-1);
-    --text-color-subtile-1:  var(--secondary-color-2);
-    --text-color-subtile-2:  var(--secondary-color-3);
-
-    --code-background-color: var(--secondary-color-2);
-    --code-text-color:       var(--primary-color-2);
-
-    --ui-range-thumb-color:  var(--primary-color-4);
-    --ui-range-thumb-border: var(--ui-ranger-thumb-color);
-
-    --textarea-border-color: var(--secondary-color-4);
-
-    --chat-id-color:         var(--theme-nuance-color-4);
-
-    /* ------------------------------------------- */
-    --button-alert-text-hover:       var(--primary-color-1);
-    --button-alert-color-hover:      var(--theme-purple-color);
-    --button-alert-border-hover:     var(--theme-purple-color);
-
-    --button-alert-text-active:      var(--primary-color-1);
-    --button-alert-color-active:     var(--theme-red-color);
-    --button-alert-border-active:    var(--theme-red-color);
-
-    /* ----------- PRIMARY BUTTONS --------------- */
-    /* - button should immediately catch the eye - */
-    --button-primary-text:
-    hsl(0,
-    calc(var(--primary-color-1-saturation) - 100%),
-    calc(var(--primary-color-1-lightness)  + 100%));
-
-    --button-primary-color:  var(--theme-nuance-color-3);
-    --button-primary-border: var(--theme-nuance-color-3);
-
-    /* ---------hover---------- */
-    --button-primary-text-hover:
-    hsl(0,
-    calc(var(--primary-color-1-saturation) - 100%),
-    calc(var(--primary-color-1-lightness)  + 100%));
-
-    --button-primary-color-hover:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 100%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
-    --button-primary-border-hover:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 100%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
-    /* ---------active--------- */
-    --button-primary-text-active:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 100%),
-    calc(var(--theme-nuance-color-3-lightness)  + 100%));
-
-    --button-primary-color-active:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 100%),
-    calc(var(--theme-nuance-color-3-lightness)  - 15%));
-
-    --button-primary-border-active:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 100%),
-    calc(var(--theme-nuance-color-3-lightness)  + 10%));
-
-    /* ---------- SECONDARY BUTTONS -------------- */
-    /* these should NOT immediately catch the eye  */
-    --button-secondary-text:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 100%),
-    calc(var(--theme-nuance-color-3-lightness)  - 50%));
-
-    --button-secondary-color:  var(--primary-color-3);
-    --button-secondary-border: var(--primary-color-3);
-
-    /* ---------hover---------- */
-    --button-secondary-text-hover:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 100%),
-    calc(var(--theme-nuance-color-3-lightness)  - 80%));
-
-    --button-secondary-color-hover:  var(--primary-color-4);
-    --button-secondary-border-hover: var(--primary-color-4);
-
-    /* ---------active--------- */
-    --button-secondary-text-active:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 100%),
-    calc(var(--theme-nuance-color-3-lightness)  - 80%));
-
-    --button-secondary-color-active:
-    hsl(0,
-    calc(var(--primary-color-4-saturation) - 100%),
-    calc(var(--primary-color-4-lightness)  - 15%));
-
-    --button-secondary-border-active:
-    hsl(0,
-    calc(var(--primary-color-4-saturation) - 100%),
-    calc(var(--primary-color-4-lightness)  - 15%));
-
-    /* ---------- TERTIARY BUTTONS --------------- */
-    /* ---------- disabled buttons --------------- */
-    --button-tertiary-text:   var(--primary-color-4);
-    --button-tertiary-color:  var(--primary-color-2);
-    --button-tertiary-border: var(--primary-color-2);
-
-    /* ---------hover---------- */
-    --button-tertiary-text:   var(--primary-color-4);
-    --button-tertiary-color:  var(--primary-color-2);
-    --button-tertiary-border: var(--primary-color-2);
-
-    --loading-color-1: #eeeeee00;
-    --loading-color-2: #eeeeeeff;
-    }

examples/server/public/theme-mangotango.css

@@ -1,216 +0,0 @@
-/* Author: Yazan Agha-Schrader */
-/* Inspiration from llama.cpp logo/banner https://github.com/ggerganov/llama.cpp#readme */
-
-.theme-mangotango {
-
---primary-color-1:      hsl(192, 8.5%, 11.6%);
---primary-color-2:      hsl(192, 8.5%, 21%);
---primary-color-3:      hsl(192, 8.5%, 30%);
---primary-color-4:      hsl(192, 8.5%, 40%);
-
---secondary-color-1:    hsl(192, 8.5%, 80%);
---secondary-color-2:    hsl(192, 8.5%, 73%);
---secondary-color-3:    hsl(192, 8.5%, 66%);
---secondary-color-4:    hsl(192, 8.5%, 60%);
-
---theme-nuance-color-1: hsl(23.1, 100%, 60.2%);
---theme-nuance-color-2: hsl(23.1, 100%, 60.2%);
---theme-nuance-color-3: hsl(23.1, 100%, 60.2%);
---theme-nuance-color-4: hsl(23.1, 100%, 60.2%);
-
-
-
-/* ---------- PRIMARY COLORS ----------------- */
---primary-color-1: hsl(192, 8.5%, 11.6%);
-    --primary-color-1-saturation: 8.5%;
-    --primary-color-1-lightness: 11.6%;
-
---primary-color-2: hsl(192, 8.5%, 21%);
-    --primary-color-2-saturation: 8.5%;
-    --primary-color-2-lightness: 21%;
-
---primary-color-3: hsl(192, 8.5%, 30%);
-    --primary-color-3-saturation: 8.5%;
-    --primary-color-3-lightness: 30%;
-
---primary-color-4: hsl(192, 8.5%, 40%);
-    --primary-color-4-saturation: 8.5%;
-    --primary-color-4-lightness: 40%;
-
-
-
-/* ---------- SECONDARY COLORS --------------- */
---secondary-color-1: hsl(192, 8.5%, 80%);
-    --secondary-color-1-saturation: 8.5%;
-    --secondary-color-1-lightness: 80%;
-
---secondary-color-2: hsl(192, 8.5%, 73%);
-    --secondary-color-2-saturation: 8.5%;
-    --secondary-color-2-lightness: 73%;
-
---secondary-color-3: hsl(192, 8.5%, 66%);
-    --secondary-color-3-saturation: 8.5%;
-    --secondary-color-3-lightness: 66%;
-
---secondary-color-4: hsl(192, 8.5%, 60%);
-    --secondary-color-4-saturation: 8.5%;
-    --secondary-color-4-lightness: 60%;
-
-
-
-/* ----------- NUANCES COLORS ---------------- */
---theme-nuance-color-1: hsl(23.1, 100%, 60.2%);
-    --theme-nuance-color-1-saturation: 100%;
-    --theme-nuance-color-1-lightness: 60.2%;
-
---theme-nuance-color-2: hsl(23.1, 100%, 60.2%);
-    --theme-nuance-color-2-saturation: 100%;
-    --theme-nuance-color-2-lightness: 60.2%;
-
---theme-nuance-color-3: hsl(23.1, 100%, 60.2%);
-    --theme-nuance-color-3-saturation: 100%;
-    --theme-nuance-color-3-lightness: 60.2%;
-
---theme-nuance-color-4: hsl(23.1, 100%, 60.2%);
-    --theme-nuance-color-4-saturation: 100%;
-    --theme-nuance-color-4-lightness: 60.2%;
-
-
-
-/* ----------- ROYGP COLORS ------------------ */
-    --theme-red-color:     hsl(325, 60%, 50%);
-    --theme-orange-color:  #e76f51;
-    --theme-yellow-color:  #ffd95f;
-    --theme-green-color:   #A3BE8C;
-    --theme-blue-color:    hsl(192, 95%, 40%);
-    --theme-purple-color:  hsl(192, 80%, 35%);
-
-
-
-/* ------------------------------------------- */
---background-color-1:    var(--primary-color-1);
---background-color-2:    var(--primary-color-2);
---background-color-3:    var(--primary-color-3);
---background-color-4:    var(--primary-color-4);
-
---border-color-1:        var(--primary-color-2);
---border-color-2:        var(--primary-color-3);
---border-color-3:        var(--primary-color-4);
-
---border-focus-color:    var(--theme-nuance-color-2);
---border-focus-shadow:   var(--theme-nuance-color-1);
-
---text-color-plain:      var(--secondary-color-1);
---text-color-subtile-1:  var(--secondary-color-2);
---text-color-subtile-2:  var(--secondary-color-3);
-
---code-background-color: var(--secondary-color-2);
---code-text-color:       var(--primary-color-2);
-
---ui-range-thumb-color:  var(--theme-nuance-color-3);
---ui-range-thumb-border: var(--ui-ranger-thumb-color);
-
---textarea-border-color: var(--secondary-color-4);
-
---chat-id-color:         var(--theme-nuance-color-4);
-
-
-
-/* ------------------------------------------- */
---button-alert-text-hover:       var(--secondary-color-1);
---button-alert-color-hover:      var(--theme-purple-color);
---button-alert-border-hover:     var(--theme-purple-color);
-
---button-alert-text-active:      var(--secondary-color-1);
---button-alert-color-active:     var(--theme-blue-color);
---button-alert-border-active:    var(--theme-blue-color);
-
-
-
-/* ----------- PRIMARY BUTTONS --------------- */
-/* - button should immediately catch the eye - */
---button-primary-text: var(--primary-color-1);
---button-primary-color:  var(--theme-nuance-color-3);
---button-primary-border: var(--theme-nuance-color-3);
-
-
-/* ---------hover---------- */
---button-primary-text-hover:
-    hsl(192,
-    calc(var(--primary-color-1-saturation) - 100%),
-    calc(var(--primary-color-1-lightness)  + 100%));
-
---button-primary-color-hover:
-    hsl(23.1,
-    calc(var(--theme-nuance-color-3-saturation) - 2%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
---button-primary-border-hover:
-    hsl(23.1,
-    calc(var(--theme-nuance-color-3-saturation) - 2%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
-
-/* ---------active--------- */
---button-primary-text-active:
-    hsl(23.1,
-    calc(var(--theme-nuance-color-3-saturation) - 100%),
-    calc(var(--theme-nuance-color-3-lightness)  + 100%));
-
---button-primary-color-active:
-    hsl(23.1,
-    calc(var(--theme-nuance-color-3-saturation) - 10%),
-    calc(var(--theme-nuance-color-3-lightness)  - 15%));
-
---button-primary-border-active:
-    hsl(23.1,
-    calc(var(--theme-nuance-color-3-saturation) - 2%),
-    calc(var(--theme-nuance-color-3-lightness)  + 10%));
-
-
-
-/* ---------- SECONDARY BUTTONS -------------- */
-/* these should NOT immediately catch the eye  */
---button-secondary-text:   var(--secondary-color-1);
---button-secondary-color:  var(--primary-color-3);
---button-secondary-border: var(--primary-color-3);
-
-
-/* ---------hover---------- */
---button-secondary-text-hover:
-    hsl(23.1,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  - 80%));
-
---button-secondary-color-hover:  var(--primary-color-4);
---button-secondary-border-hover: var(--primary-color-4);
-
-
-/* ---------active--------- */
---button-secondary-text-active: var(--secondary-color-1);
-
---button-secondary-color-active:
-    hsl(192,
-    calc(var(--primary-color-4-saturation) - 30%),
-    calc(var(--primary-color-4-lightness)  - 15%));
-
---button-secondary-border-active:
-    hsl(192,
-    calc(var(--primary-color-4-saturation) - 30%),
-    calc(var(--primary-color-4-lightness)  - 15%));
-
-
-
-/* ---------- TERTIARY BUTTONS --------------- */
-/* ---------- disabled buttons --------------- */
---button-tertiary-text:   var(--primary-color-4);
---button-tertiary-color:  var(--primary-color-2);
---button-tertiary-border: var(--primary-color-2);
-
-
-/* ---------hover---------- */
---button-tertiary-text:   var(--primary-color-4);
---button-tertiary-color:  var(--primary-color-2);
---button-tertiary-border: var(--primary-color-2);
-
-}

examples/server/public/theme-playground.css

@@ -1,221 +0,0 @@
-/* Author: Yazan Agha-Schrader */
-/* Inspiration from OpenAI's Playground platform https://platform.openai.com/playground/ */
-
-.theme-playground {
-
-/* ---------- PRIMARY COLORS ----------------- */
---primary-color-1: hsl(0, 0%,    99.2%);
-    --primary-color-1-hue:         0;
-    --primary-color-1-saturation:  0%;
-    --primary-color-1-lightness:   99.2%;
-
---primary-color-2: hsl(0, 0%,    95%);
-    --primary-color-2-hue:         0;
-    --primary-color-2-saturation:  0%;
-    --primary-color-2-lightness:   95%;
-
---primary-color-3: hsl(0, 0%,    88%);
-    --primary-color-3-hue:         0;
-    --primary-color-3-saturation:  0%;
-    --primary-color-3-lightness:   88%;
-
---primary-color-4: hsl(0, 0%,    80%);
-    --primary-color-4-hue:         0;
-    --primary-color-4-saturation:  0%;
-    --primary-color-4-lightness:   80%;
-
-
-
-/* ---------- SECONDARY COLORS --------------- */
---secondary-color-1: hsl(0, 0%,    20%);
-    --secondary-color-1-hue:         0;
-    --secondary-color-1-saturation:  0%;
-    --secondary-color-1-lightness:   20%;
-
---secondary-color-2: hsl(0, 0%,    23.1%);
-    --secondary-color-2-hue:         0;
-    --secondary-color-2-saturation:  0%;
-    --secondary-color-2-lightness:   23.1%;
-
---secondary-color-3: hsl(0, 0%,    29%);
-    --secondary-color-3-hue:         0;
-    --secondary-color-3-saturation:  0%;
-    --secondary-color-3-lightness:   29%;
-
---secondary-color-4: hsl(0, 0%,    36.1%);
-    --secondary-color-4-hue:         0;
-    --secondary-color-4-saturation:  0%;
-    --secondary-color-4-lightness:   36.1%;
-
-
-
-/* ----------- NUANCES COLORS ---------------- */
---theme-nuance-color-1: hsl(165.2, 82.1%, 35.1%);
-    --theme-nuance-color-1-hue:             165.2;
-    --theme-nuance-color-1-saturation:      82.1%;
-    --theme-nuance-color-1-lightness:       35.1%;
-
---theme-nuance-color-2: hsl(165.2, 82.1%, 35.1%);
-    --theme-nuance-color-2-hue:             165.2;
-    --theme-nuance-color-2-saturation:      82.1%;
-    --theme-nuance-color-2-lightness:       35.1%;
-
---theme-nuance-color-3: hsl(165.2, 81.1%, 35.3%);
-    --theme-nuance-color-3-hue:             165.2;
-    --theme-nuance-color-3-saturation:      81.1%;
-    --theme-nuance-color-3-lightness:       35.3%;
-
---theme-nuance-color-4: hsl(164.9, 81.6%, 27.6%);
-    --theme-nuance-color-4-hue:             164.9;
-    --theme-nuance-color-4-saturation:      81.6%;
-    --theme-nuance-color-4-lightness:       27.6%;
-
-
-
-/* ----------- ROYGP COLORS ------------------ */
---theme-red-color:     hsl(0.3, 80%, 50%);
---theme-orange-color:  #e76f51;
---theme-yellow-color:  hsl(60, 70.6%, 73.3%);
---theme-green-color:   #A3BE8C;
---theme-purple-color:  hsl(0.3, 70%, 45%);
-
-
-
-/* ------------------------------------------- */
---background-color-1:    var(--primary-color-1);
---background-color-2:    var(--primary-color-2);
---background-color-3:    var(--primary-color-3);
---background-color-4:    var(--primary-color-4);
-
---border-color-1:        var(--primary-color-2);
---border-color-2:        var(--primary-color-3);
---border-color-3:        var(--primary-color-4);
-
---border-focus-color:    var(--theme-nuance-color-2);
---border-focus-shadow:   var(--theme-nuance-color-1);
-
---text-color-plain:      var(--secondary-color-1);
---text-color-subtile-1:  var(--secondary-color-2);
---text-color-subtile-2:  var(--secondary-color-3);
-
---code-background-color: var(--secondary-color-2);
---code-text-color:       var(--primary-color-2);
-
---ui-range-thumb-color:  var(--primary-color-4);
---ui-range-thumb-border: var(--ui-ranger-thumb-color);
-
---textarea-border-color: var(--secondary-color-4);
-
---chat-id-color:        var(--theme-nuance-color-4);
-
-
-
-/* ------------------------------------------- */
---button-alert-text-hover:       var(--primary-color-1);
---button-alert-color-hover:      var(--theme-purple-color);
---button-alert-border-hover:     var(--theme-purple-color);
-
---button-alert-text-active:      var(--primary-color-1);
---button-alert-color-active:     var(--theme-red-color);
---button-alert-border-active:    var(--theme-red-color);
-
-
-
-/* ----------- PRIMARY BUTTONS --------------- */
-/* - button should immediately catch the eye - */
---button-primary-text:
-    hsl(0,
-    calc(var(--primary-color-1-saturation) - 100%),
-    calc(var(--primary-color-1-lightness)  + 100%));
-
---button-primary-color:  var(--theme-nuance-color-3);
---button-primary-border: var(--theme-nuance-color-3);
-
-
-/* ---------hover---------- */
---button-primary-text-hover:
-    hsl(0,
-    calc(var(--primary-color-1-saturation) - 100%),
-    calc(var(--primary-color-1-lightness)  + 100%));
-
---button-primary-color-hover:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 2%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
---button-primary-border-hover:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 2%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
-
-/* ---------active--------- */
---button-primary-text-active:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 100%),
-    calc(var(--theme-nuance-color-3-lightness)  + 100%));
-
---button-primary-color-active:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 10%),
-    calc(var(--theme-nuance-color-3-lightness)  - 15%));
-
---button-primary-border-active:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 2%),
-    calc(var(--theme-nuance-color-3-lightness)  + 10%));
-
-
-
-/* ---------- SECONDARY BUTTONS -------------- */
-/* these should NOT immediately catch the eye  */
---button-secondary-text:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  - 50%));
-
---button-secondary-color:  var(--primary-color-3);
---button-secondary-border: var(--primary-color-3);
-
-
-/* ---------hover---------- */
---button-secondary-text-hover:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  - 80%));
-
---button-secondary-color-hover:  var(--primary-color-4);
---button-secondary-border-hover: var(--primary-color-4);
-
-
-/* ---------active--------- */
---button-secondary-text-active:
-    hsl(165.2,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  - 80%));
-
---button-secondary-color-active:
-    hsl(0,
-    calc(var(--primary-color-4-saturation) - 30%),
-    calc(var(--primary-color-4-lightness)  - 15%));
-
---button-secondary-border-active:
-    hsl(0,
-    calc(var(--primary-color-4-saturation) - 30%),
-    calc(var(--primary-color-4-lightness)  - 15%));
-
-
-
-/* ---------- TERTIARY BUTTONS --------------- */
-/* ---------- disabled buttons --------------- */
---button-tertiary-text:   var(--primary-color-4);
---button-tertiary-color:  var(--primary-color-2);
---button-tertiary-border: var(--primary-color-2);
-
-
-/* ---------hover---------- */
---button-tertiary-text:   var(--primary-color-4);
---button-tertiary-color:  var(--primary-color-2);
---button-tertiary-border: var(--primary-color-2);
-
-}

examples/server/public/theme-polarnight.css

@@ -1,253 +0,0 @@
-/* Author: Yazan Agha-Schrader */
-/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */
-
-.theme-polarnight {
-
-/* ---------- PRIMARY COLORS ----------------- */
---primary-color-1: hsl(220.0, 16.4%, 21.6%) ;
-    --primary-color-1-hue:             220.0;
-    --primary-color-1-saturation:      16.4%;
-    --primary-color-1-lightness:       21.6%;
-
---primary-color-2: hsl(221.7, 16.3%, 27.6%) ;
-    -primary-color-2-hue:              221.7;
-    --primary-color-2-saturation:      16.3%;
-    --primary-color-2-lightness:       27.6%;
-
---primary-color-3: hsl(220.0, 16.8%, 31.6%) ;
-    --primary-color-3-hue:             220.0;
-    --primary-color-3-saturation:      16.8%;
-    --primary-color-3-lightness:       31.6%;
-
---primary-color-4: hsl(220.0, 16.5%, 35.7%);
-    --primary-color-4-hue:             220.0;
-    --primary-color-4-saturation:      16.5%;
-    --primary-color-4-lightness:       35.7%;
-
-
-
-/* ---------- SECONDARY COLORS --------------- */
---secondary-color-1: hsl(217.5, 26.7%, 94.1%);
-    --secondary-color-1-hue:             217.5;
-    --secondary-color-1-saturation:      26.7%;
-    --secondary-color-1-lightness:       94.1%;
-
---secondary-color-2: hsl(218.2, 26.8%, 92.0%);
-    --secondary-color-2-hue:             218.2;
-    --secondary-color-2-saturation:      26.8%;
-    --secondary-color-2-lightness:       92.0%;
-
---secondary-color-3: hsl(218.8, 27.9%, 88.0%);
-    --secondary-color-3-hue:             218.8;
-    --secondary-color-3-saturation:      27.9%;
-    --secondary-color-3-lightness:       88.0%;
-
---secondary-color-4: hsl(218.8, 18.3%, 81.8%);
-    --secondary-color-4-hue:             218.8;
-    --secondary-color-4-saturation:      18.3%;
-    --secondary-color-4-lightness:       81.8%;
-
-
-
-/* ----------- NUANCES COLORS ---------------- */
---theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%);
-    --theme-nuance-color-1-hue:             178.7;
-    --theme-nuance-color-1-saturation:      25.1%;
-    --theme-nuance-color-1-lightness:       64.9%;
-
---theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%);
-    --theme-nuance-color-2-hue:             193.3;
-    --theme-nuance-color-2-saturation:      43.4%;
-    --theme-nuance-color-2-lightness:       67.5%;
-
---theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%);
-    --theme-nuance-color-3-hue:             210.0;
-    --theme-nuance-color-3-saturation:      34.0%;
-    --theme-nuance-color-3-lightness:       63.1%;
-
---theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%);
-    --theme-nuance-color-4-hue:             213.1;
-    --theme-nuance-color-4-saturation:      32.0%;
-    --theme-nuance-color-4-lightness:       52.2%;
-
-
-
-/* ----------- ROYGP COLORS ------------------ */
---theme-red-color:    hsl(354.3, 42.3%, 56.5%);
---theme-orange-color: hsl(20, 85%, 50%);
---theme-yellow-color: hsl(20, 75%, 45%);
---theme-green-color:  hsl( 92.4, 27.8%, 64.7%);
---theme-purple-color: hsl(311.1, 20.2%, 63.1%);
-
-
-
-/* ------------------------------------------------ */
---background-color-1:    var(--primary-color-1);
---background-color-2:    var(--primary-color-2);
---background-color-3:    var(--primary-color-3);
---background-color-4:    var(--primary-color-4);
-
---border-color-1:        var(--primary-color-2);
---border-color-2:        var(--primary-color-3);
---border-color-3:        var(--primary-color-4);
-
---border-focus-color:    var(--theme-nuance-color-2);
---border-focus-shadow:   var(--theme-nuance-color-1);
-
---text-color-plain:      var(--secondary-color-1);
---text-color-subtile-1:  var(--secondary-color-2);
---text-color-subtile-2:  var(--secondary-color-3);
-
---code-background-color: var(--secondary-color-2);
---code-text-color:       var(--primary-color-2);
-
---ui-range-thumb-color:  var(--theme-nuance-color-3);
---ui-range-thumb-border: var(--ui-ranger-thumb-color);
-
---textarea-border-color: var(--secondary-color-4);
-
---chat-id-color:        var(--theme-nuance-color-4);
-
-
-
-/* ------------------------------------------- */
---button-alert-text-hover:       var(--secondary-color-1);
---button-alert-color-hover:      var(--theme-yellow-color);
---button-alert-border-hover:     var(--theme-yellow-color);
-
---button-alert-text-active:      var(--secondary-color-1);
---button-alert-color-active:     var(--theme-orange-color);
---button-alert-border-active:    var(--theme-orange-color);
-
-
-
-/* ----------- PRIMARY BUTTONS --------------- */
-/* - button should immediately catch the eye - */
---button-primary-text:   var(--secondary-color-1);
---button-primary-color:  var(--theme-nuance-color-3);
---button-primary-border: var(--theme-nuance-color-3);
-
-
-/* ---------hover---------- */
---button-primary-text-hover:
-    hsl(217.5,
-    calc(var(--secondary-color-1-saturation) - 35%),
-    calc(var(--secondary-color-1-lightness)  + 30%));
-
---button-primary-color-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) -  2%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
---button-primary-border-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) -  2%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
-
-/* ---------active--------- */
---button-primary-text-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  + 35%));
-
---button-primary-color-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 10%),
-    calc(var(--theme-nuance-color-3-lightness)  - 25%));
-
---button-primary-border-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 10%),
-    calc(var(--theme-nuance-color-3-lightness)  - 25%));
-
-
-
-/* ---------- SECONDARY BUTTONS -------------- */
-/* these should NOT immediately catch the eye  */
---button-secondary-text:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  - 50%));
-
---button-secondary-color:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  + 10%));
-
---button-secondary-border:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  + 10%));
-
-
-/* ---------hover---------- */
---button-secondary-text-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  - 80%));
-
---button-secondary-color-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 22%),
-    calc(var(--theme-nuance-color-3-lightness)  +  1%));
-
---button-secondary-border-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 22%),
-    calc(var(--theme-nuance-color-3-lightness)  +  1%));
-
-
-/* ---------active--------- */
---button-secondary-text-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  + 25%));
-
---button-secondary-color-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 30%),
-    calc(var(--theme-nuance-color-3-lightness)  - 15%));
-
---button-secondary-border-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 30%),
-    calc(var(--theme-nuance-color-3-lightness)  - 15%));
-
-
-
-/* ---------- TERTIARY BUTTONS --------------- */
-/* ---------- disabled buttons --------------- */
---button-tertiary-text:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  -  5%));
-
---button-tertiary-color:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  + 20%));
-
---button-tertiary-border:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  + 20%));
-
-
-/* ---------hover---------- */
---button-tertiary-text-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  -  5%));
-
---button-tertiary-color-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  + 20%));
-
---button-tertiary-border-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  + 20%));
-
-}

examples/server/public/theme-snowstorm.css

@@ -1,251 +0,0 @@
-/* Author: Yazan Agha-Schrader */
-/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */
-
-.theme-snowstorm {
-
-/* ---------- PRIMARY COLORS ----------------- */
---primary-color-1: hsl(217.5, 26.7%, 94.1%);
-    --primary-color-1-hue:             217.5;
-    --primary-color-1-saturation:      26.7%;
-    --primary-color-1-lightness:       94.1%;
-
---primary-color-2: hsl(218.2, 26.8%, 92.0%);
-    --primary-color-2-hue:             218.2;
-    --primary-color-2-saturation:      26.8%;
-    --primary-color-2-lightness:       92.0%;
-
---primary-color-3: hsl(218.8, 27.9%, 88.0%);
-    --primary-color-3-hue:             218.8;
-    --primary-color-3-saturation:      27.9%;
-    --primary-color-3-lightness:       88.0%;
-
---primary-color-4: hsl(218.8, 18.3%, 81.8%);
-    --primary-color-4-hue:             218.8;
-    --primary-color-4-saturation:      18.3%;
-    --primary-color-4-lightness:       81.8%;
-
-
-/* ---------- SECONDARY COLORS --------------- */
---secondary-color-1: hsl(220.0, 16.4%, 21.6%);
-    --secondary-color-1-hue:             220.0;
-    --secondary-color-1-saturation:      16.4%;
-    --secondary-color-1-lightness:       21.6%;
-
---secondary-color-2: hsl(221.7, 16.3%, 27.6%);
-    --secondary-color-2-hue:             221.7;
-    --secondary-color-2-saturation:      16.3%;
-    --secondary-color-2-lightness:       27.6%;
-
---secondary-color-3: hsl(220.0, 16.8%, 31.6%);
-    --secondary-color-3-hue:             220.0;
-    --secondary-color-3-saturation:      16.8%;
-    --secondary-color-3-lightness:       31.6%;
-
---secondary-color-4: hsl(220.0, 16.5%, 35.7%);
-    --secondary-color-4-hue:             220.0;
-    --secondary-color-4-saturation:      16.5%;
-    --secondary-color-4-lightness:       35.7%;
-
-
-
-/* ----------- NUANCES COLORS ---------------- */
---theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%);
-    --theme-nuance-color-1-hue:             178.7;
-    --theme-nuance-color-1-saturation:      25.1%;
-    --theme-nuance-color-1-lightness:       64.9%;
-
---theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%);
-    --theme-nuance-color-2-hue:             193.3;
-    --theme-nuance-color-2-saturation:      43.4%;
-    --theme-nuance-color-2-lightness:       67.5%;
-
---theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%);
-    --theme-nuance-color-3-hue:             210.0;
-    --theme-nuance-color-3-saturation:      34.0%;
-    --theme-nuance-color-3-lightness:       63.1%;
-
---theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%);
-    --theme-nuance-color-4-hue:             213.1;
-    --theme-nuance-color-4-saturation:      32.0%;
-    --theme-nuance-color-4-lightness:       52.2%;
-
-
-
-/* ----------- ROYGP COLORS ------------------ */
---theme-red-color:    hsl(32.5, 80%, 50%);
---theme-orange-color: hsl(32.5, 70%, 45%);
---theme-yellow-color: hsl(40.0,   0.6%, 73.3%);
---theme-green-color:  hsl(92.4,  27.8%, 64.7%);
---theme-purple-color: hsl(311.1, 20.2%, 63.1%);
-
-
-
-/* ------------------------------------------- */
---background-color-1:    var(--primary-color-1);
---background-color-2:    var(--primary-color-2);
---background-color-3:    var(--primary-color-3);
---background-color-4:    var(--primary-color-4);
-
---border-color-1:        var(--primary-color-2);
---border-color-2:        var(--primary-color-3);
---border-color-3:        var(--primary-color-4);
-
---border-focus-color:    var(--theme-nuance-color-2);
---border-focus-shadow:   var(--theme-nuance-color-1);
-
---text-color-plain:      var(--secondary-color-1);
---text-color-subtile-1:  var(--secondary-color-2);
---text-color-subtile-2:  var(--secondary-color-3);
-
---code-background-color: var(--secondary-color-2);
---code-text-color:       var(--primary-color-2);
-
---ui-range-thumb-color:  var(--theme-nuance-color-3);
---ui-range-thumb-border: var(--ui-ranger-thumb-color);
-
---textarea-border-color: var(--secondary-color-4);
-
---chat-id-color:         var(--theme-nuance-color-4);
-
-
-
-/* ------------------------------------------- */
---button-alert-text-hover:       var(--primary-color-1);
---button-alert-color-hover:      var(--theme-orange-color);
---button-alert-border-hover:     var(--theme-orange-color);
-
---button-alert-text-active:      var(--primary-color-1);
---button-alert-color-active:     var(--theme-red-color);
---button-alert-border-active:    var(--theme-red-color);
-
-
-
-/* ----------- PRIMARY BUTTONS --------------- */
-/* - button should immediately catch the eye - */
---button-primary-text:   var(--secondary-color-1);
---button-primary-color:  var(--theme-nuance-color-3);
---button-primary-border: var(--theme-nuance-color-3);
-
-
-/* ---------hover---------- */
---button-primary-text-hover:
-    hsl(217.5,
-    calc(var(--secondary-color-1-saturation) + 35%),
-    calc(var(--secondary-color-1-lightness)  - 30%));
-
---button-primary-color-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) -  2%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
---button-primary-border-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) -  2%),
-    calc(var(--theme-nuance-color-3-lightness)  - 10%));
-
-
-/* ---------active--------- */
---button-primary-text-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  + 35%));
-
---button-primary-color-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 10%),
-    calc(var(--theme-nuance-color-3-lightness)  - 25%));
-
---button-primary-border-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 10%),
-    calc(var(--theme-nuance-color-3-lightness)  - 25%));
-
-
-
-/* ---------- SECONDARY BUTTONS -------------- */
-/* these should NOT immediately catch the eye  */
---button-secondary-text:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  - 50%));
-
---button-secondary-color:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  + 10%));
-
---button-secondary-border:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  + 10%));
-
-
-/* ---------hover---------- */
---button-secondary-text-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 20%),
-    calc(var(--theme-nuance-color-3-lightness)  - 80%));
-
---button-secondary-color-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 22%),
-    calc(var(--theme-nuance-color-3-lightness)  +  1%));
-
---button-secondary-border-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 22%),
-    calc(var(--theme-nuance-color-3-lightness)  +  1%));
-
-
-/* ---------active--------- */
---button-secondary-text-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) + 40%),
-    calc(var(--theme-nuance-color-3-lightness)  - 55%));
-
---button-secondary-color-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 30%),
-    calc(var(--theme-nuance-color-3-lightness)  -  5%));
-
---button-secondary-border-active:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 30%),
-    calc(var(--theme-nuance-color-3-lightness)  -  5%));
-
-
-
-/* ---------- TERTIARY BUTTONS --------------- */
-/* ---------- disabled buttons --------------- */
---button-tertiary-text:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  -  5%));
-
---button-tertiary-color:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  + 20%));
-
---button-tertiary-border:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  + 20%));
-
-/* ---------hover---------- */
---button-tertiary-text-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  -  5%));
-
---button-tertiary-color-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  + 20%));
-
---button-tertiary-border-hover:
-    hsl(210,
-    calc(var(--theme-nuance-color-3-saturation) - 40%),
-    calc(var(--theme-nuance-color-3-lightness)  + 20%));
-
-}

examples/server/public_simplechat/datautils.mjs

@@ -1,266 +0,0 @@
-//@ts-check
-// Helpers to work with different data types
-// by Humans for All
-//
-
-/**
- * Given the limited context size of local LLMs and , many a times when context gets filled
- * between the prompt and the response, it can lead to repeating text garbage generation.
- * And many a times setting penalty wrt repeatation leads to over-intelligent garbage
- * repeatation with slight variations. These garbage inturn can lead to overloading of the
- * available model context, leading to less valuable response for subsequent prompts/queries,
- * if chat history is sent to ai model.
- *
- * So two simple minded garbage trimming logics are experimented below.
- * * one based on progressively-larger-substring-based-repeat-matching-with-partial-skip and
- * * another based on char-histogram-driven garbage trimming.
- *   * in future characteristic of histogram over varying lengths could be used to allow for
- *     a more aggressive and adaptive trimming logic.
- */
-
-
-/**
- * Simple minded logic to help remove repeating garbage at end of the string.
- * The repeatation needs to be perfectly matching.
- *
- * The logic progressively goes on probing for longer and longer substring based
- * repeatation, till there is no longer repeatation. Inturn picks the one with
- * the longest chain.
- *
- * @param {string} sIn
- * @param {number} maxSubL
- * @param {number} maxMatchLenThreshold
- */
-export function trim_repeat_garbage_at_end(sIn, maxSubL=10, maxMatchLenThreshold=40) {
-    let rCnt = [0];
-    let maxMatchLen = maxSubL;
-    let iMML = -1;
-    for(let subL=1; subL < maxSubL; subL++) {
-        rCnt.push(0);
-        let i;
-        let refS = sIn.substring(sIn.length-subL, sIn.length);
-        for(i=sIn.length; i > 0; i -= subL) {
-            let curS = sIn.substring(i-subL, i);
-            if (refS != curS) {
-                let curMatchLen = rCnt[subL]*subL;
-                if (maxMatchLen < curMatchLen) {
-                    maxMatchLen = curMatchLen;
-                    iMML = subL;
-                }
-                break;
-            }
-            rCnt[subL] += 1;
-        }
-    }
-    console.debug("DBUG:DU:TrimRepeatGarbage:", rCnt);
-    if ((iMML == -1) || (maxMatchLen < maxMatchLenThreshold)) {
-        return {trimmed: false, data: sIn};
-    }
-    console.debug("DBUG:TrimRepeatGarbage:TrimmedCharLen:", maxMatchLen);
-    let iEnd = sIn.length - maxMatchLen;
-    return { trimmed: true, data: sIn.substring(0, iEnd) };
-}
-
-
-/**
- * Simple minded logic to help remove repeating garbage at end of the string, till it cant.
- * If its not able to trim, then it will try to skip a char at end and then trim, a few times.
- * This ensures that even if there are multiple runs of garbage with different patterns, the
- * logic still tries to munch through them.
- *
- * @param {string} sIn
- * @param {number} maxSubL
- * @param {number | undefined} [maxMatchLenThreshold]
- */
-export function trim_repeat_garbage_at_end_loop(sIn, maxSubL, maxMatchLenThreshold, skipMax=16) {
-    let sCur = sIn;
-    let sSaved = "";
-    let iTry = 0;
-    while(true) {
-        let got = trim_repeat_garbage_at_end(sCur, maxSubL, maxMatchLenThreshold);
-        if (got.trimmed != true) {
-            if (iTry == 0) {
-                sSaved = got.data;
-            }
-            iTry += 1;
-            if (iTry >= skipMax) {
-                return sSaved;
-            }
-            got.data = got.data.substring(0,got.data.length-1);
-        } else {
-            iTry = 0;
-        }
-        sCur = got.data;
-    }
-}
-
-
-/**
- * A simple minded try trim garbage at end using histogram driven characteristics.
- * There can be variation in the repeatations, as long as no new char props up.
- *
- * This tracks the chars and their frequency in a specified length of substring at the end
- * and inturn checks if moving further into the generated text from the end remains within
- * the same char subset or goes beyond it and based on that either trims the string at the
- * end or not. This allows to filter garbage at the end, including even if there are certain
- * kind of small variations in the repeated text wrt position of seen chars.
- *
- * Allow the garbage to contain upto maxUniq chars, but at the same time ensure that
- * a given type of char ie numerals or alphabets or other types dont cross the specified
- * maxType limit. This allows intermixed text garbage to be identified and trimmed.
- *
- * ALERT: This is not perfect and only provides a rough garbage identification logic.
- * Also it currently only differentiates between character classes wrt english.
- *
- * @param {string} sIn
- * @param {number} maxType
- * @param {number} maxUniq
- * @param {number} maxMatchLenThreshold
- */
-export function trim_hist_garbage_at_end(sIn, maxType, maxUniq, maxMatchLenThreshold) {
-    if (sIn.length < maxMatchLenThreshold) {
-        return { trimmed: false, data: sIn };
-    }
-    let iAlp = 0;
-    let iNum = 0;
-    let iOth = 0;
-    // Learn
-    let hist = {};
-    let iUniq = 0;
-    for(let i=0; i<maxMatchLenThreshold; i++) {
-        let c = sIn[sIn.length-1-i];
-        if (c in hist) {
-            hist[c] += 1;
-        } else {
-            if(c.match(/[0-9]/) != null) {
-                iNum += 1;
-            } else if(c.match(/[A-Za-z]/) != null) {
-                iAlp += 1;
-            } else {
-                iOth += 1;
-            }
-            iUniq += 1;
-            if (iUniq >= maxUniq) {
-                break;
-            }
-            hist[c] = 1;
-        }
-    }
-    console.debug("DBUG:TrimHistGarbage:", hist);
-    if ((iAlp > maxType) || (iNum > maxType) || (iOth > maxType)) {
-        return { trimmed: false, data: sIn };
-    }
-    // Catch and Trim
-    for(let i=0; i < sIn.length; i++) {
-        let c = sIn[sIn.length-1-i];
-        if (!(c in hist)) {
-            if (i < maxMatchLenThreshold) {
-                return { trimmed: false, data: sIn };
-            }
-            console.debug("DBUG:TrimHistGarbage:TrimmedCharLen:", i);
-            return { trimmed: true, data: sIn.substring(0, sIn.length-i+1) };
-        }
-    }
-    console.debug("DBUG:TrimHistGarbage:Trimmed fully");
-    return { trimmed: true, data: "" };
-}
-
-/**
- * Keep trimming repeatedly using hist_garbage logic, till you no longer can.
- * This ensures that even if there are multiple runs of garbage with different patterns,
- * the logic still tries to munch through them.
- *
- * @param {any} sIn
- * @param {number} maxType
- * @param {number} maxUniq
- * @param {number} maxMatchLenThreshold
- */
-export function trim_hist_garbage_at_end_loop(sIn, maxType, maxUniq, maxMatchLenThreshold) {
-    let sCur = sIn;
-    while (true) {
-        let got = trim_hist_garbage_at_end(sCur, maxType, maxUniq, maxMatchLenThreshold);
-        if (!got.trimmed) {
-            return got.data;
-        }
-        sCur = got.data;
-    }
-}
-
-/**
- * Try trim garbage at the end by using both the hist-driven-garbage-trimming as well as
- * skip-a-bit-if-reqd-then-repeat-pattern-based-garbage-trimming, with blind retrying.
- * @param {string} sIn
- */
-export function trim_garbage_at_end(sIn) {
-    let sCur = sIn;
-    for(let i=0; i<2; i++) {
-        sCur = trim_hist_garbage_at_end_loop(sCur, 8, 24, 72);
-        sCur = trim_repeat_garbage_at_end_loop(sCur, 32, 72, 12);
-    }
-    return sCur;
-}
-
-
-/**
- * NewLines array helper.
- * Allow for maintaining a list of lines.
- * Allow for a line to be builtup/appended part by part.
- */
-export class NewLines {
-
-    constructor() {
-        /** @type {string[]} */
-        this.lines = [];
-    }
-
-    /**
-     * Extracts lines from the passed string and inturn either
-     * append to a previous partial line or add a new line.
-     * @param {string} sLines
-     */
-    add_append(sLines) {
-        let aLines = sLines.split("\n");
-        let lCnt = 0;
-        for(let line of aLines) {
-            lCnt += 1;
-            // Add back newline removed if any during split
-            if (lCnt < aLines.length) {
-                line += "\n";
-            } else {
-                if (sLines.endsWith("\n")) {
-                    line += "\n";
-                }
-            }
-            // Append if required
-            if (lCnt == 1) {
-                let lastLine = this.lines[this.lines.length-1];
-                if (lastLine != undefined) {
-                    if (!lastLine.endsWith("\n")) {
-                        this.lines[this.lines.length-1] += line;
-                        continue;
-                    }
-                }
-            }
-            // Add new line
-            this.lines.push(line);
-        }
-    }
-
-    /**
-     * Shift the oldest/earliest/0th line in the array. [Old-New|Earliest-Latest]
-     * Optionally control whether only full lines (ie those with newline at end) will be returned
-     * or will a partial line without a newline at end (can only be the last line) be returned.
-     * @param {boolean} bFullWithNewLineOnly
-     */
-    shift(bFullWithNewLineOnly=true) {
-        let line = this.lines[0];
-        if (line == undefined) {
-            return undefined;
-        }
-        if ((line[line.length-1] != "\n") && bFullWithNewLineOnly){
-            return undefined;
-        }
-        return this.lines.shift();
-    }
-
-}