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14 Commits

Author SHA1 Message Date
Mika Pi
0ce59c5965
Merge 8e69669007 into 60cfa728e2 2024-12-24 08:48:34 +03:00
Diego Devesa
60cfa728e2
ggml : use wstring for backend search paths (#10960)
ggml-ci
2024-12-24 04:05:27 +01:00
Diego Devesa
3327bb0f8d
ggml : fix arm enabled features check (#10961) 2024-12-24 04:05:17 +01:00
Diego Devesa
32d6ee6385
ggml : fix const usage in SSE path (#10962) 2024-12-23 20:25:52 +01:00
Xuan Son Nguyen
14b699ecde
server : fix missing model id in /model endpoint (#10957)
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* server : fix missing model id in /model endpoint

* fix ci
2024-12-23 12:52:25 +01:00
Xuan Son Nguyen
485dc01214
server : add system_fingerprint to chat/completion (#10917)
* server : add system_fingerprint to chat/completion

* update README
2024-12-23 12:02:44 +01:00
Radoslav Gerganov
86bf31cfe6
rpc-server : add support for the SYCL backend (#10934) 2024-12-23 10:39:30 +02:00
Yun Dou
b92a14a841
llama : support InfiniAI Megrez 3b (#10893)
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Python check requirements.txt / check-requirements (push) Has been cancelled
* Support InfiniAI Megrez 3b

* Fix tokenizer_clean_spaces for megrez
2024-12-23 01:35:44 +01:00
ymcki
6f0c9e034b
llama : support for Llama-3_1-Nemotron-51B (#10669)
* conflict resolution

* move comments after bracket to its own line
2024-12-23 01:22:33 +01:00
Eric Curtin
dab76c92cc
llama-run : include temperature option (#10899)
This commit updates the `examples/run/README.md` file to include a new
option for setting the temperature and updates the `run.cpp` file to
parse this option.

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
2024-12-23 01:21:40 +01:00
yuri@FreeBSD
7024d59e6a
ggml : fix run-time on FreeBSD in get_executable_path() (#10948) 2024-12-23 01:20:11 +01:00
Rudi Servo
7c0e285858
devops : add docker-multi-stage builds (#10832) 2024-12-22 23:22:58 +01:00
Billel Mokeddem
7ae33a616f
llama : add Falcon3 support (#10883)
* Add Falcon3 model support

* Add fix for adding bos to added special tokens

* Add comment explaining the logic behind the if statement

* Add a log message to better track the when the following line of code is triggered

* Update log to only print when input and output characters are different

* Fix handling pre-normalized tokens

* Refactoring
2024-12-23 00:09:58 +02:00
Mika Pi
8e69669007
Fix compilation on Pop!_OS 22.04 LTS CUDA
Downgrade the C++ version from 17 to 14, and also update the code to remove compilation warnings.
2024-12-15 00:43:30 -08:00
42 changed files with 1386 additions and 735 deletions

81
.devops/cpu.Dockerfile Normal file
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ARG UBUNTU_VERSION=22.04
FROM ubuntu:$UBUNTU_VERSION AS build
RUN apt-get update && \
apt-get install -y build-essential git cmake libcurl4-openssl-dev
WORKDIR /app
COPY . .
RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
cmake --build build -j $(nproc)
RUN mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib \;
RUN mkdir -p /app/full \
&& cp build/bin/* /app/full \
&& cp *.py /app/full \
&& cp -r gguf-py /app/full \
&& cp -r requirements /app/full \
&& cp requirements.txt /app/full \
&& cp .devops/tools.sh /app/full/tools.sh
## Base image
FROM ubuntu:$UBUNTU_VERSION AS base
RUN apt-get update \
&& apt-get install -y libgomp1 curl\
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
&& find /var/cache -type f -delete
COPY --from=build /app/lib/ /app
### Full
FROM base AS full
COPY --from=build /app/full /app
WORKDIR /app
RUN apt-get update \
&& apt-get install -y \
git \
python3 \
python3-pip \
&& pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
&& find /var/cache -type f -delete
ENTRYPOINT ["/app/tools.sh"]
### Light, CLI only
FROM base AS light
COPY --from=build /app/full/llama-cli /app
WORKDIR /app
ENTRYPOINT [ "/app/llama-cli" ]
### Server, Server only
FROM base AS server
ENV LLAMA_ARG_HOST=0.0.0.0
COPY --from=build /app/full/llama-server /app
WORKDIR /app
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/app/llama-server" ]

94
.devops/cuda.Dockerfile Normal file
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ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG CUDA_VERSION=12.6.0
# Target the CUDA build image
ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
FROM ${BASE_CUDA_DEV_CONTAINER} AS build
# CUDA architecture to build for (defaults to all supported archs)
ARG CUDA_DOCKER_ARCH=default
RUN apt-get update && \
apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
WORKDIR /app
COPY . .
RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
fi && \
cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release -j$(nproc)
RUN mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib \;
RUN mkdir -p /app/full \
&& cp build/bin/* /app/full \
&& cp *.py /app/full \
&& cp -r gguf-py /app/full \
&& cp -r requirements /app/full \
&& cp requirements.txt /app/full \
&& cp .devops/tools.sh /app/full/tools.sh
## Base image
FROM ${BASE_CUDA_RUN_CONTAINER} AS base
RUN apt-get update \
&& apt-get install -y libgomp1 curl\
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
&& find /var/cache -type f -delete
COPY --from=build /app/lib/ /app
### Full
FROM base AS full
COPY --from=build /app/full /app
WORKDIR /app
RUN apt-get update \
&& apt-get install -y \
git \
python3 \
python3-pip \
&& pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
&& find /var/cache -type f -delete
ENTRYPOINT ["/app/tools.sh"]
### Light, CLI only
FROM base AS light
COPY --from=build /app/full/llama-cli /app
WORKDIR /app
ENTRYPOINT [ "/app/llama-cli" ]
### Server, Server only
FROM base AS server
ENV LLAMA_ARG_HOST=0.0.0.0
COPY --from=build /app/full/llama-server /app
WORKDIR /app
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/app/llama-server" ]

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ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG CUDA_VERSION=12.6.0
# Target the CUDA build image
ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
FROM ${BASE_CUDA_DEV_CONTAINER} AS build
# CUDA architecture to build for (defaults to all supported archs)
ARG CUDA_DOCKER_ARCH=default
RUN apt-get update && \
apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
COPY requirements.txt requirements.txt
COPY requirements requirements
RUN pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt
WORKDIR /app
COPY . .
# Use the default CUDA archs if not specified
RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
fi && \
cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release -j$(nproc) && \
cp build/bin/* .
ENTRYPOINT ["/app/.devops/tools.sh"]

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ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG MUSA_VERSION=rc3.1.0
# Target the MUSA build image
ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
FROM ${BASE_MUSA_DEV_CONTAINER} AS build
# MUSA architecture to build for (defaults to all supported archs)
ARG MUSA_DOCKER_ARCH=default
RUN apt-get update && \
apt-get install -y build-essential cmake python3 python3-pip git libcurl4-openssl-dev libgomp1
COPY requirements.txt requirements.txt
COPY requirements requirements
RUN pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt
WORKDIR /app
COPY . .
# Use the default MUSA archs if not specified
RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
fi && \
cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release -j$(nproc) && \
cp build/bin/* .
ENTRYPOINT ["/app/.devops/tools.sh"]

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ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG ROCM_VERSION=5.6
# Target the CUDA build image
ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
FROM ${BASE_ROCM_DEV_CONTAINER} AS build
# Unless otherwise specified, we make a fat build.
# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
# This is mostly tied to rocBLAS supported archs.
ARG ROCM_DOCKER_ARCH="\
gfx803 \
gfx900 \
gfx906 \
gfx908 \
gfx90a \
gfx1010 \
gfx1030 \
gfx1100 \
gfx1101 \
gfx1102"
COPY requirements.txt requirements.txt
COPY requirements requirements
RUN pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt
WORKDIR /app
COPY . .
# Set nvcc architecture
ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
# Enable ROCm
ENV GGML_HIPBLAS=1
ENV CC=/opt/rocm/llvm/bin/clang
ENV CXX=/opt/rocm/llvm/bin/clang++
# Enable cURL
ENV LLAMA_CURL=1
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev
RUN make -j$(nproc)
ENTRYPOINT ["/app/.devops/tools.sh"]

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@ -1,38 +0,0 @@
ARG UBUNTU_VERSION=22.04
FROM ubuntu:$UBUNTU_VERSION AS build
RUN apt-get update && \
apt-get install -y build-essential git cmake libcurl4-openssl-dev
WORKDIR /app
COPY . .
RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
cmake --build build -j $(nproc) && \
mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib/ \;
FROM ubuntu:$UBUNTU_VERSION as runtime
WORKDIR /app
RUN apt-get update && \
apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1
COPY requirements.txt /app/requirements.txt
COPY requirements /app/requirements
COPY .devops/tools.sh /app/tools.sh
RUN pip install --upgrade pip setuptools wheel && \
pip install -r /app/requirements.txt
COPY --from=build /app/build/bin/ /app/
COPY --from=build /app/lib/ /app/
COPY --from=build /app/convert_hf_to_gguf.py /app/
COPY --from=build /app/gguf-py /app/gguf-py
ENV LC_ALL=C.utf8
ENTRYPOINT ["/app/tools.sh"]

91
.devops/intel.Dockerfile Normal file
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ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
## Build Image
FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
ARG GGML_SYCL_F16=OFF
RUN apt-get update && \
apt-get install -y git libcurl4-openssl-dev
WORKDIR /app
COPY . .
RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
echo "GGML_SYCL_F16 is set" \
&& export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
fi && \
echo "Building with dynamic libs" && \
cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
cmake --build build --config Release -j$(nproc)
RUN mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib \;
RUN mkdir -p /app/full \
&& cp build/bin/* /app/full \
&& cp *.py /app/full \
&& cp -r gguf-py /app/full \
&& cp -r requirements /app/full \
&& cp requirements.txt /app/full \
&& cp .devops/tools.sh /app/full/tools.sh
FROM intel/oneapi-basekit:$ONEAPI_VERSION AS base
RUN apt-get update \
&& apt-get install -y libgomp1 curl\
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
&& find /var/cache -type f -delete
### Full
FROM base AS full
COPY --from=build /app/lib/ /app
COPY --from=build /app/full /app
WORKDIR /app
RUN apt-get update \
&& apt-get install -y \
git \
python3 \
python3-pip \
&& pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
&& find /var/cache -type f -delete
ENTRYPOINT ["/app/tools.sh"]
### Light, CLI only
FROM base AS light
COPY --from=build /app/lib/ /app
COPY --from=build /app/full/llama-cli /app
WORKDIR /app
ENTRYPOINT [ "/app/llama-cli" ]
### Server, Server only
FROM base AS server
ENV LLAMA_ARG_HOST=0.0.0.0
COPY --from=build /app/lib/ /app
COPY --from=build /app/full/llama-server /app
WORKDIR /app
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/app/llama-server" ]

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ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG CUDA_VERSION=12.6.0
# Target the CUDA build image
ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
# Target the CUDA runtime image
ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
FROM ${BASE_CUDA_DEV_CONTAINER} AS build
# CUDA architecture to build for (defaults to all supported archs)
ARG CUDA_DOCKER_ARCH=default
RUN apt-get update && \
apt-get install -y build-essential git cmake
WORKDIR /app
COPY . .
# Use the default CUDA archs if not specified
RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
fi && \
cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-cli -j$(nproc) && \
mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib \;
FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
RUN apt-get update && \
apt-get install -y libgomp1
COPY --from=build /app/lib/ /
COPY --from=build /app/build/bin/llama-cli /
ENTRYPOINT [ "/llama-cli" ]

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ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
ARG GGML_SYCL_F16=OFF
RUN apt-get update && \
apt-get install -y git
WORKDIR /app
COPY . .
RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
echo "GGML_SYCL_F16 is set" && \
export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
fi && \
echo "Building with static libs" && \
cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \
${OPT_SYCL_F16} -DBUILD_SHARED_LIBS=OFF && \
cmake --build build --config Release --target llama-cli
FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime
COPY --from=build /app/build/bin/llama-cli /llama-cli
ENV LC_ALL=C.utf8
ENTRYPOINT [ "/llama-cli" ]

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ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG MUSA_VERSION=rc3.1.0
# Target the MUSA build image
ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
# Target the MUSA runtime image
ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
FROM ${BASE_MUSA_DEV_CONTAINER} AS build
# MUSA architecture to build for (defaults to all supported archs)
ARG MUSA_DOCKER_ARCH=default
RUN apt-get update && \
apt-get install -y build-essential git cmake
WORKDIR /app
COPY . .
# Use the default MUSA archs if not specified
RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
fi && \
cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-cli -j$(nproc) && \
mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib \;
FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime
RUN apt-get update && \
apt-get install -y libgomp1
COPY --from=build /app/lib/ /
COPY --from=build /app/build/bin/llama-cli /llama-cli
ENTRYPOINT [ "/llama-cli" ]

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ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG ROCM_VERSION=5.6
# Target the CUDA build image
ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
FROM ${BASE_ROCM_DEV_CONTAINER} AS build
# Unless otherwise specified, we make a fat build.
# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
# This is mostly tied to rocBLAS supported archs.
ARG ROCM_DOCKER_ARCH="\
gfx803 \
gfx900 \
gfx906 \
gfx908 \
gfx90a \
gfx1010 \
gfx1030 \
gfx1100 \
gfx1101 \
gfx1102"
COPY requirements.txt requirements.txt
COPY requirements requirements
RUN pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt
WORKDIR /app
COPY . .
# Set nvcc architecture
ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
# Enable ROCm
ENV GGML_HIPBLAS=1
ENV CC=/opt/rocm/llvm/bin/clang
ENV CXX=/opt/rocm/llvm/bin/clang++
RUN make -j$(nproc) llama-cli
ENTRYPOINT [ "/app/llama-cli" ]

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@ -1,27 +0,0 @@
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
# Install Vulkan SDK
RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
apt update -y && \
apt-get install -y vulkan-sdk
# Build it
WORKDIR /app
COPY . .
RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 && \
cmake --build build --config Release --target llama-cli
# Clean up
WORKDIR /
RUN cp /app/build/bin/llama-cli /llama-cli && \
rm -rf /app
ENV LC_ALL=C.utf8
ENTRYPOINT [ "/llama-cli" ]

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@ -1,29 +0,0 @@
ARG UBUNTU_VERSION=22.04
FROM ubuntu:$UBUNTU_VERSION AS build
RUN apt-get update && \
apt-get install -y build-essential git cmake libcurl4-openssl-dev
WORKDIR /app
COPY . .
RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
cmake --build build -j $(nproc) && \
mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib/ \;
FROM ubuntu:$UBUNTU_VERSION AS runtime
WORKDIR /app
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev libgomp1 curl
COPY --from=build /app/build/bin/llama-cli /app/
COPY --from=build /app/lib/ /app/
ENV LC_ALL=C.utf8
ENTRYPOINT [ "/app/llama-cli" ]

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@ -1,43 +0,0 @@
ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG CUDA_VERSION=12.6.0
# Target the CUDA build image
ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
# Target the CUDA runtime image
ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
FROM ${BASE_CUDA_DEV_CONTAINER} AS build
# CUDA architecture to build for (defaults to all supported archs)
ARG CUDA_DOCKER_ARCH=default
RUN apt-get update && \
apt-get install -y build-essential git cmake libcurl4-openssl-dev
WORKDIR /app
COPY . .
# Use the default CUDA archs if not specified
RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
export CMAKE_ARGS="-DCMAKE_CUDA_ARCHITECTURES=${CUDA_DOCKER_ARCH}"; \
fi && \
cmake -B build -DGGML_NATIVE=OFF -DGGML_CUDA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-server -j$(nproc) && \
mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib \;
FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev libgomp1 curl
COPY --from=build /app/lib/ /
COPY --from=build /app/build/bin/llama-server /llama-server
# Must be set to 0.0.0.0 so it can listen to requests from host machine
ENV LLAMA_ARG_HOST=0.0.0.0
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/llama-server" ]

View File

@ -1,34 +0,0 @@
ARG ONEAPI_VERSION=2025.0.0-0-devel-ubuntu22.04
FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
ARG GGML_SYCL_F16=OFF
RUN apt-get update && \
apt-get install -y git libcurl4-openssl-dev
WORKDIR /app
COPY . .
RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
echo "GGML_SYCL_F16 is set" && \
export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
fi && \
echo "Building with dynamic libs" && \
cmake -B build -DGGML_NATIVE=OFF -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
cmake --build build --config Release --target llama-server
FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev curl
COPY --from=build /app/build/bin/llama-server /llama-server
ENV LC_ALL=C.utf8
# Must be set to 0.0.0.0 so it can listen to requests from host machine
ENV LLAMA_ARG_HOST=0.0.0.0
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/llama-server" ]

View File

@ -1,43 +0,0 @@
ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG MUSA_VERSION=rc3.1.0
# Target the MUSA build image
ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
# Target the MUSA runtime image
ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
FROM ${BASE_MUSA_DEV_CONTAINER} AS build
# MUSA architecture to build for (defaults to all supported archs)
ARG MUSA_DOCKER_ARCH=default
RUN apt-get update && \
apt-get install -y build-essential git cmake libcurl4-openssl-dev
WORKDIR /app
COPY . .
# Use the default MUSA archs if not specified
RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
fi && \
cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release --target llama-server -j$(nproc) && \
mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib \;
FROM ${BASE_MUSA_RUN_CONTAINER} AS runtime
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev libgomp1 curl
COPY --from=build /app/lib/ /
COPY --from=build /app/build/bin/llama-server /llama-server
# Must be set to 0.0.0.0 so it can listen to requests from host machine
ENV LLAMA_ARG_HOST=0.0.0.0
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/llama-server" ]

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@ -1,54 +0,0 @@
ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG ROCM_VERSION=5.6
# Target the CUDA build image
ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
FROM ${BASE_ROCM_DEV_CONTAINER} AS build
# Unless otherwise specified, we make a fat build.
# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
# This is mostly tied to rocBLAS supported archs.
ARG ROCM_DOCKER_ARCH="\
gfx803 \
gfx900 \
gfx906 \
gfx908 \
gfx90a \
gfx1010 \
gfx1030 \
gfx1100 \
gfx1101 \
gfx1102"
COPY requirements.txt requirements.txt
COPY requirements requirements
RUN pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt
WORKDIR /app
COPY . .
# Set nvcc architecture
ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
# Enable ROCm
ENV GGML_HIPBLAS=1
ENV CC=/opt/rocm/llvm/bin/clang
ENV CXX=/opt/rocm/llvm/bin/clang++
# Must be set to 0.0.0.0 so it can listen to requests from host machine
ENV LLAMA_ARG_HOST=0.0.0.0
# Enable cURL
ENV LLAMA_CURL=1
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev curl
RUN make -j$(nproc) llama-server
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/app/llama-server" ]

View File

@ -1,31 +0,0 @@
ARG UBUNTU_VERSION=jammy
FROM ubuntu:$UBUNTU_VERSION AS build
# Install build tools
RUN apt update && apt install -y git build-essential cmake wget
# Install Vulkan SDK and cURL
RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
apt update -y && \
apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
# Build it
WORKDIR /app
COPY . .
RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \
cmake --build build --config Release --target llama-server
# Clean up
WORKDIR /
RUN cp /app/build/bin/llama-server /llama-server && \
rm -rf /app
ENV LC_ALL=C.utf8
# Must be set to 0.0.0.0 so it can listen to requests from host machine
ENV LLAMA_ARG_HOST=0.0.0.0
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/llama-server" ]

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@ -1,33 +0,0 @@
ARG UBUNTU_VERSION=22.04
FROM ubuntu:$UBUNTU_VERSION AS build
RUN apt-get update && \
apt-get install -y build-essential git cmake libcurl4-openssl-dev
WORKDIR /app
COPY . .
RUN cmake -S . -B build -DGGML_BACKEND_DL=ON -DGGML_NATIVE=OFF -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_CURL=ON -DCMAKE_BUILD_TYPE=Release && \
cmake --build build -j $(nproc) && \
mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib/ \;
FROM ubuntu:$UBUNTU_VERSION AS runtime
WORKDIR /app
RUN apt-get update && \
apt-get install -y libcurl4-openssl-dev libgomp1 curl
COPY --from=build /app/build/bin/llama-server /app/
COPY --from=build /app/lib/ /app/
ENV LC_ALL=C.utf8
# Must be set to 0.0.0.0 so it can listen to requests from host machine
ENV LLAMA_ARG_HOST=0.0.0.0
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/app/llama-server" ]

108
.devops/musa.Dockerfile Normal file
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@ -0,0 +1,108 @@
ARG UBUNTU_VERSION=22.04
# This needs to generally match the container host's environment.
ARG MUSA_VERSION=rc3.1.0
# Target the MUSA build image
ARG BASE_MUSA_DEV_CONTAINER=mthreads/musa:${MUSA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
ARG BASE_MUSA_RUN_CONTAINER=mthreads/musa:${MUSA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
FROM ${BASE_MUSA_DEV_CONTAINER} AS build
# MUSA architecture to build for (defaults to all supported archs)
ARG MUSA_DOCKER_ARCH=default
RUN apt-get update && \
apt-get install -y \
build-essential \
cmake \
python3 \
python3-pip \
git \
libcurl4-openssl-dev \
libgomp1
COPY requirements.txt requirements.txt
COPY requirements requirements
RUN pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt
WORKDIR /app
COPY . .
# Use the default MUSA archs if not specified
RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
export CMAKE_ARGS="-DMUSA_ARCHITECTURES=${MUSA_DOCKER_ARCH}"; \
fi && \
cmake -B build -DGGML_NATIVE=OFF -DGGML_MUSA=ON -DLLAMA_CURL=ON ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \
cmake --build build --config Release -j$(nproc)
RUN mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib \;
RUN mkdir -p /app/full \
&& cp build/bin/* /app/full \
&& cp *.py /app/full \
&& cp -r gguf-py /app/full \
&& cp -r requirements /app/full \
&& cp requirements.txt /app/full \
&& cp .devops/tools.sh /app/full/tools.sh
## Base image
FROM ${BASE_MUSA_RUN_CONTAINER} AS base
RUN apt-get update \
&& apt-get install -y libgomp1 curl\
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
&& find /var/cache -type f -delete
COPY --from=build /app/lib/ /app
### Full
FROM base AS full
COPY --from=build /app/full /app
WORKDIR /app
RUN apt-get update \
&& apt-get install -y \
git \
python3 \
python3-pip \
&& pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
&& find /var/cache -type f -delete
ENTRYPOINT ["/app/tools.sh"]
### Light, CLI only
FROM base AS light
COPY --from=build /app/full/llama-cli /app
WORKDIR /app
ENTRYPOINT [ "/app/llama-cli" ]
### Server, Server only
FROM base AS server
ENV LLAMA_ARG_HOST=0.0.0.0
COPY --from=build /app/full/llama-server /app
WORKDIR /app
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/app/llama-server" ]

113
.devops/rocm.Dockerfile Normal file
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@ -0,0 +1,113 @@
ARG UBUNTU_VERSION=24.04
# This needs to generally match the container host's environment.
ARG ROCM_VERSION=6.3
ARG AMDGPU_VERSION=6.3
# Target the CUDA build image
ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
### Build image
FROM ${BASE_ROCM_DEV_CONTAINER} AS build
# Unless otherwise specified, we make a fat build.
# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
# This is mostly tied to rocBLAS supported archs.
# gfx803, gfx900, gfx1032, gfx1101, gfx1102,not officialy supported
# gfx906 is deprecated
#check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.2.4/reference/system-requirements.html
#ARG ROCM_DOCKER_ARCH='gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102'
ARG ROCM_DOCKER_ARCH=gfx1100
# Set nvcc architectured
ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
# Enable ROCm
# ENV CC=/opt/rocm/llvm/bin/clang
# ENV CXX=/opt/rocm/llvm/bin/clang++
RUN apt-get update \
&& apt-get install -y \
build-essential \
cmake \
git \
libcurl4-openssl-dev \
curl \
libgomp1
WORKDIR /app
COPY . .
RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DCMAKE_BUILD_TYPE=Release -DLLAMA_CURL=ON \
&& cmake --build build --config Release -j$(nproc)
RUN mkdir -p /app/lib \
&& find build -name "*.so" -exec cp {} /app/lib \;
RUN mkdir -p /app/full \
&& cp build/bin/* /app/full \
&& cp *.py /app/full \
&& cp -r gguf-py /app/full \
&& cp -r requirements /app/full \
&& cp requirements.txt /app/full \
&& cp .devops/tools.sh /app/full/tools.sh
## Base image
FROM ${BASE_ROCM_DEV_CONTAINER} AS base
RUN apt-get update \
&& apt-get install -y libgomp1 curl\
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
&& find /var/cache -type f -delete
COPY --from=build /app/lib/ /app
### Full
FROM base AS full
COPY --from=build /app/full /app
WORKDIR /app
RUN apt-get update \
&& apt-get install -y \
git \
python3-pip \
python3 \
python3-wheel\
&& pip install --break-system-packages --upgrade setuptools \
&& pip install --break-system-packages -r requirements.txt \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
&& find /var/cache -type f -delete
ENTRYPOINT ["/app/tools.sh"]
### Light, CLI only
FROM base AS light
COPY --from=build /app/full/llama-cli /app
WORKDIR /app
ENTRYPOINT [ "/app/llama-cli" ]
### Server, Server only
FROM base AS server
ENV LLAMA_ARG_HOST=0.0.0.0
COPY --from=build /app/full/llama-server /app
WORKDIR /app
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/app/llama-server" ]

88
.devops/vulkan.Dockerfile Normal file
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@ -0,0 +1,88 @@
ARG UBUNTU_VERSION=jammy
FROM ubuntu:$UBUNTU_VERSION AS build
# Install build tools
RUN apt update && apt install -y git build-essential cmake wget
# Install Vulkan SDK and cURL
RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
apt update -y && \
apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
# Build it
WORKDIR /app
COPY . .
RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \
cmake --build build --config Release -j$(nproc)
RUN mkdir -p /app/lib && \
find build -name "*.so" -exec cp {} /app/lib \;
RUN mkdir -p /app/full \
&& cp build/bin/* /app/full \
&& cp *.py /app/full \
&& cp -r gguf-py /app/full \
&& cp -r requirements /app/full \
&& cp requirements.txt /app/full \
&& cp .devops/tools.sh /app/full/tools.sh
## Base image
FROM ubuntu:$UBUNTU_VERSION AS base
RUN apt-get update \
&& apt-get install -y libgomp1 curl\
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
&& find /var/cache -type f -delete
COPY --from=build /app/lib/ /app
### Full
FROM base AS full
COPY --from=build /app/full /app
WORKDIR /app
RUN apt-get update \
&& apt-get install -y \
git \
python3 \
python3-pip \
&& pip install --upgrade pip setuptools wheel \
&& pip install -r requirements.txt \
&& apt autoremove -y \
&& apt clean -y \
&& rm -rf /tmp/* /var/tmp/* \
&& find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete \
&& find /var/cache -type f -delete
ENTRYPOINT ["/app/tools.sh"]
### Light, CLI only
FROM base AS light
COPY --from=build /app/full/llama-cli /app
WORKDIR /app
ENTRYPOINT [ "/app/llama-cli" ]
### Server, Server only
FROM base AS server
ENV LLAMA_ARG_HOST=0.0.0.0
COPY --from=build /app/full/llama-server /app
WORKDIR /app
HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
ENTRYPOINT [ "/app/llama-server" ]

View File

@ -34,21 +34,14 @@ jobs:
strategy:
matrix:
config:
- { tag: "light", dockerfile: ".devops/llama-cli.Dockerfile", platforms: "linux/amd64,linux/arm64" }
- { tag: "server", dockerfile: ".devops/llama-server.Dockerfile", platforms: "linux/amd64,linux/arm64" }
- { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" }
- { tag: "light-cuda", dockerfile: ".devops/llama-cli-cuda.Dockerfile", platforms: "linux/amd64" }
- { tag: "server-cuda", dockerfile: ".devops/llama-server-cuda.Dockerfile", platforms: "linux/amd64" }
- { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" }
- { tag: "light-musa", dockerfile: ".devops/llama-cli-musa.Dockerfile", platforms: "linux/amd64" }
- { tag: "server-musa", dockerfile: ".devops/llama-server-musa.Dockerfile", platforms: "linux/amd64" }
- { tag: "full-musa", dockerfile: ".devops/full-musa.Dockerfile", platforms: "linux/amd64" }
# Multi-stage build
- { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: false}
- { tag: "cuda", dockerfile: ".devops/cuda.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
- { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
- { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
- { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, freediskspace: false}
# Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete
#- { tag: "light-rocm", dockerfile: ".devops/llama-cli-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
#- { tag: "server-rocm", dockerfile: ".devops/llama-server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
#- { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
- { tag: "light-intel", dockerfile: ".devops/llama-cli-intel.Dockerfile", platforms: "linux/amd64" }
- { tag: "server-intel", dockerfile: ".devops/llama-server-intel.Dockerfile", platforms: "linux/amd64" }
#- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, freediskspace: true }
steps:
- name: Check out the repo
uses: actions/checkout@v4
@ -56,10 +49,10 @@ jobs:
fetch-depth: 0 # preserve git history, so we can determine the build number
- name: Set up QEMU
uses: docker/setup-qemu-action@v2
uses: docker/setup-qemu-action@v3
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
uses: docker/setup-buildx-action@v3
- name: Log in to Docker Hub
uses: docker/login-action@v2
@ -79,25 +72,34 @@ jobs:
# determine tag name postfix (build number, commit hash)
if [[ "${{ env.GITHUB_BRANCH_NAME }}" == "master" ]]; then
TAG_POSTFIX="b${BUILD_NUMBER}"
TAG_POSTFIX="-b${BUILD_NUMBER}"
else
SAFE_NAME=$(echo "${{ env.GITHUB_BRANCH_NAME }}" | tr '/' '-')
TAG_POSTFIX="${SAFE_NAME}-${SHORT_HASH}"
TAG_POSTFIX="-${SAFE_NAME}-${SHORT_HASH}"
fi
# list all tags possible
TAGS=""
TAGS="${TAGS}ghcr.io/${REPO_OWNER}/${REPO_NAME}:${{ matrix.config.tag }},"
TAGS="${TAGS}ghcr.io/${REPO_OWNER}/${REPO_NAME}:${{ matrix.config.tag }}-${TAG_POSTFIX}"
echo "output_tags=$TAGS" >> $GITHUB_OUTPUT
echo "output_tags=$TAGS" # print out for debugging
if [[ "${{ matrix.config.tag }}" == "cpu" ]]; then
TYPE=""
else
TYPE="-${{ matrix.config.tag }}"
fi
PREFIX="ghcr.io/${REPO_OWNER}/${REPO_NAME}:"
FULLTAGS="${PREFIX}full${TYPE},${PREFIX}full${TYPE}${TAG_POSTFIX}"
LIGHTTAGS="${PREFIX}light${TYPE},${PREFIX}light${TYPE}${TAG_POSTFIX}"
SERVERTAGS="${PREFIX}server${TYPE},${PREFIX}server${TYPE}${TAG_POSTFIX}"
echo "full_output_tags=$FULLTAGS" >> $GITHUB_OUTPUT
echo "light_output_tags=$LIGHTTAGS" >> $GITHUB_OUTPUT
echo "server_output_tags=$SERVERTAGS" >> $GITHUB_OUTPUT
echo "full_output_tags=$FULLTAGS" # print out for debugging
echo "light_output_tags=$LIGHTTAGS" # print out for debugging
echo "server_output_tags=$SERVERTAGS" # print out for debugging
env:
GITHUB_BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
# https://github.com/jlumbroso/free-disk-space/tree/54081f138730dfa15788a46383842cd2f914a1be#example
- name: Free Disk Space (Ubuntu)
if: ${{ matrix.config.free_disk_space == true }}
uses: jlumbroso/free-disk-space@main
with:
# this might remove tools that are actually needed,
@ -113,13 +115,59 @@ jobs:
docker-images: true
swap-storage: true
- name: Build and push Docker image (tagged + versioned)
if: ${{ github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch' }}
- name: Build and push Full Docker image (tagged + versioned)
if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.full == true }}
uses: docker/build-push-action@v6
with:
context: .
push: true
platforms: ${{ matrix.config.platforms }}
# tag list is generated from step above
tags: ${{ steps.tag.outputs.output_tags }}
tags: ${{ steps.tag.outputs.full_output_tags }}
file: ${{ matrix.config.dockerfile }}
target: full
provenance: false
# using github experimental cache
cache-from: type=gha
cache-to: type=gha,mode=max
# return to this if the experimental github cache is having issues
#cache-to: type=local,dest=/tmp/.buildx-cache
#cache-from: type=local,src=/tmp/.buildx-cache
- name: Build and push Light Docker image (tagged + versioned)
if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.light == true }}
uses: docker/build-push-action@v6
with:
context: .
push: true
platforms: ${{ matrix.config.platforms }}
# tag list is generated from step above
tags: ${{ steps.tag.outputs.light_output_tags }}
file: ${{ matrix.config.dockerfile }}
target: light
provenance: false
# using github experimental cache
cache-from: type=gha
cache-to: type=gha,mode=max
# return to this if the experimental github cache is having issues
#cache-to: type=local,dest=/tmp/.buildx-cache
#cache-from: type=local,src=/tmp/.buildx-cache
- name: Build and push Server Docker image (tagged + versioned)
if: ${{ (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'workflow_dispatch') && matrix.config.server == true }}
uses: docker/build-push-action@v6
with:
context: .
push: true
platforms: ${{ matrix.config.platforms }}
# tag list is generated from step above
tags: ${{ steps.tag.outputs.server_output_tags }}
file: ${{ matrix.config.dockerfile }}
target: server
provenance: false
# using github experimental cache
cache-from: type=gha
cache-to: type=gha,mode=max
# return to this if the experimental github cache is having issues
#cache-to: type=local,dest=/tmp/.buildx-cache
#cache-from: type=local,src=/tmp/.buildx-cache

View File

@ -529,9 +529,19 @@ class Model:
else:
token: str = reverse_vocab[i]
if token in added_vocab:
# The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
# To avoid unexpected issues - we make sure to normalize non-normalized tokens
if not tokenizer.added_tokens_decoder[i].normalized:
previous_token = token
token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))
if previous_token != token:
logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
if tokenizer.added_tokens_decoder[i].special or self.does_token_look_special(token):
toktypes.append(gguf.TokenType.CONTROL)
else:
# NOTE: this was added for Gemma.
# Encoding and decoding the tokens above isn't sufficient for this case.
token = token.replace(b"\xe2\x96\x81".decode("utf-8"), " ") # pre-normalize user-defined spaces
toktypes.append(gguf.TokenType.USER_DEFINED)
else:
@ -575,6 +585,9 @@ class Model:
if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
# ref: https://huggingface.co/tiiuae/falcon-7b
res = "falcon"
if chkhsh == "9d032fcbd5501f4a38150912590928bfb36091efb5df11b8e2124b0390e3fb1e":
# ref: https://huggingface.co/tiiuae/Falcon3-7B-Base
res = "falcon3"
if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
# ref: https://huggingface.co/BAAI/bge-small-en-v1.5
res = "bert-bge"
@ -671,6 +684,9 @@ class Model:
if chkhsh == "ad851be1dba641f2e3711822f816db2c265f788b37c63b4e1aeacb9ee92de8eb":
# ref: https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct
res = "gigachat"
if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1":
# ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct
res = "megrez"
if res is None:
logger.warning("\n")
@ -1679,6 +1695,184 @@ class LlamaModel(Model):
raise ValueError(f"Unprocessed experts: {experts}")
@Model.register("DeciLMForCausalLM")
class DeciModel(Model):
model_arch = gguf.MODEL_ARCH.DECI
@staticmethod
def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
# DeciLM-specific code
intermediate_size = int(2 * ffn_mult * n_embd / 3)
return DeciModel._find_multiple(intermediate_size, 256)
@staticmethod
def _find_multiple(n: int, k: int) -> int:
# DeciLM-specific code
if n % k == 0:
return n
return n + k - (n % k)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
_block_configs: list[dict[str,Any]] = self.hparams["block_configs"]
assert self.block_count == len(_block_configs)
self._num_kv_heads = list()
self._num_heads = list()
_ffn_multipliers = list()
# ***linear attention layer***
# if n_heads_in_group is None and replace_with_linear is True
# then _num_kv_heads[il] is 0 and _num_heads[il] is num_attention_heads
# ***attention-free layer***
# if n_heads_in_group is None and replace_with_linear is False
# then _num_kv_heads[il] is 0 and _num_heads[il] is 0
# ***normal attention-layer***
# if n_heads_in_group is not None, then
# _num_kv_heads[il] is num_attention_head // n_heads_in_group and
# _num_heads[il] is num_attention_head
for il in range(len(_block_configs)):
if _block_configs[il]["attention"]["n_heads_in_group"] is None:
if _block_configs[il]["attention"]["replace_with_linear"] is True:
self._num_kv_heads.append(0)
self._num_heads.append(self.hparams["num_attention_heads"])
else:
self._num_kv_heads.append(0)
self._num_heads.append(0)
else:
self._num_kv_heads.append(self.hparams["num_attention_heads"] // _block_configs[il]["attention"]["n_heads_in_group"])
self._num_heads.append(self.hparams["num_attention_heads"])
_ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"])
assert self.block_count == len(self._num_kv_heads)
assert self.block_count == len(self._num_heads)
assert self.block_count == len(_ffn_multipliers)
assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
assert isinstance(self._num_heads, list) and isinstance(self._num_heads[0], int)
assert isinstance(_ffn_multipliers, list) and isinstance(_ffn_multipliers[0], float)
self._ffn_dims: list[int] = [
DeciModel._ffn_mult_to_intermediate_size(multiplier, self.hparams["hidden_size"])
for multiplier in _ffn_multipliers
]
def set_vocab(self):
# Please change tokenizer_config.json of Llama-3_1-Nemotron-51B's
# eos_token from '|eot_id|' to '|end_of_text|'
if self.hparams.get("vocab_size", 128256) == 128256:
tokens, toktypes, tokpre = self.get_vocab_base()
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_tokenizer_pre(tokpre)
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(
self.dir_model, load_merges=True,
special_token_types = ['bos', 'eos', 'eom', 'eot']
)
special_vocab._set_special_token("bos", 128000)
special_vocab._set_special_token("eos", 128001)
special_vocab._set_special_token("eom", 128008)
special_vocab._set_special_token("eot", 128009)
special_vocab.add_to_gguf(self.gguf_writer)
else:
# DeciLM-7B
self._set_vocab_llama_hf()
# self._set_vocab_gpt2()
def set_gguf_parameters(self):
if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
assert self.block_count == len(self._num_kv_heads)
assert self.block_count == len(self._num_heads)
assert self.block_count == len(self._ffn_dims)
self.gguf_writer.add_head_count_kv(self._num_kv_heads)
self.gguf_writer.add_head_count(self._num_heads)
self.gguf_writer.add_feed_forward_length(self._ffn_dims)
self.gguf_writer.add_block_count(self.block_count)
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_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_key_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_value_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_file_type(self.ftype)
else: # DeciLM-7B
super().set_gguf_parameters()
if "num_key_value_heads_per_layer" in self.hparams: # DeciLM-7B
self._num_kv_heads: list[int] = self.hparams["num_key_value_heads_per_layer"]
assert self.block_count == len(self._num_kv_heads)
self.gguf_writer.add_head_count_kv(self._num_kv_heads)
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if "head_dim" in hparams:
rope_dim = hparams["head_dim"]
else:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
if self.hparams["rope_scaling"].get("type") == "linear":
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
@staticmethod
def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
if n_head_kv is not None and n_head != n_head_kv:
n_head = n_head_kv
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape))
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
n_head = self.hparams["num_attention_heads"]
if bid is not None:
if "num_key_value_heads_per_layer" in self.hparams:
n_kv_head = self.hparams["num_key_value_heads_per_layer"][bid]
elif "block_configs" in self.hparams:
n_kv_head = self._num_kv_heads[bid]
n_head = self._num_heads[bid]
else:
n_kv_head = self.hparams.get("num_key_value_heads")
else:
n_kv_head = self.hparams.get("num_key_value_heads")
if name.endswith(("q_proj.weight", "q_proj.bias")):
data_torch = DeciModel.permute(data_torch, n_head, n_head)
if name.endswith(("k_proj.weight", "k_proj.bias")):
data_torch = DeciModel.permute(data_torch, n_head, n_kv_head)
return [(self.map_tensor_name(name), data_torch)]
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
if rope_scaling.get("rope_type", '').lower() == "llama3":
base = self.hparams.get("rope_theta", 10000.0)
dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
factor = rope_scaling.get("factor", 8.0)
low_freq_factor = rope_scaling.get("low_freq_factor", 1.0)
high_freq_factor = rope_scaling.get("high_freq_factor", 4.0)
old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
low_freq_wavelen = old_context_len / low_freq_factor
high_freq_wavelen = old_context_len / high_freq_factor
assert low_freq_wavelen != high_freq_wavelen
rope_factors = []
for freq in freqs:
wavelen = 2 * math.pi / freq
if wavelen < high_freq_wavelen:
rope_factors.append(1)
elif wavelen > low_freq_wavelen:
rope_factors.append(factor)
else:
smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
rope_factors.append(1 / ((1 - smooth) / factor + smooth))
yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
def prepare_tensors(self):
super().prepare_tensors()
@Model.register("BitnetForCausalLM")
class BitnetModel(Model):
model_arch = gguf.MODEL_ARCH.BITNET

View File

@ -72,6 +72,7 @@ models = [
{"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
{"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
{"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
{"name": "falcon3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", },
{"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
{"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
{"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
@ -105,6 +106,7 @@ models = [
{"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", },
{"name": "roberta-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
{"name": "gigachat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
{"name": "megrez", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
]

View File

@ -12,6 +12,10 @@
#include "ggml-vulkan.h"
#endif
#ifdef GGML_USE_SYCL
#include "ggml-sycl.h"
#endif
#include "ggml-rpc.h"
#ifdef _WIN32
# include <windows.h>
@ -91,6 +95,12 @@ static ggml_backend_t create_backend() {
if (!backend) {
fprintf(stderr, "%s: ggml_backend_vulkan_init() failed\n", __func__);
}
#elif GGML_USE_SYCL
fprintf(stderr, "%s: using SYCL backend\n", __func__);
backend = ggml_backend_sycl_init(0); // init device 0
if (!backend) {
fprintf(stderr, "%s: ggml_backend_sycl_init() failed\n", __func__);
}
#endif
// if there aren't GPU Backends fallback to CPU backend
@ -106,6 +116,8 @@ static void get_backend_memory(size_t * free_mem, size_t * total_mem) {
ggml_backend_cuda_get_device_memory(0, free_mem, total_mem);
#elif GGML_USE_VULKAN
ggml_backend_vk_get_device_memory(0, free_mem, total_mem);
#elif GGML_USE_SYCL
ggml_backend_sycl_get_device_memory(0, free_mem, total_mem);
#else
#ifdef _WIN32
MEMORYSTATUSEX status;

View File

@ -19,6 +19,8 @@ Options:
Context size (default: 2048)
-n, --ngl <value>
Number of GPU layers (default: 0)
--temp <value>
Temperature (default: 0.8)
-v, --verbose, --log-verbose
Set verbosity level to infinity (i.e. log all messages, useful for debugging)
-h, --help

View File

@ -55,29 +55,51 @@ static int printe(const char * fmt, ...) {
class Opt {
public:
int init(int argc, const char ** argv) {
ctx_params = llama_context_default_params();
model_params = llama_model_default_params();
context_size_default = ctx_params.n_batch;
ngl_default = model_params.n_gpu_layers;
common_params_sampling sampling;
temperature_default = sampling.temp;
if (argc < 2) {
printe("Error: No arguments provided.\n");
print_help();
return 1;
}
// Parse arguments
if (parse(argc, argv)) {
printe("Error: Failed to parse arguments.\n");
help();
print_help();
return 1;
}
// If help is requested, show help and exit
if (help_) {
help();
if (help) {
print_help();
return 2;
}
ctx_params.n_batch = context_size >= 0 ? context_size : context_size_default;
model_params.n_gpu_layers = ngl >= 0 ? ngl : ngl_default;
temperature = temperature >= 0 ? temperature : temperature_default;
return 0; // Success
}
llama_context_params ctx_params;
llama_model_params model_params;
std::string model_;
std::string user_;
int context_size_ = -1, ngl_ = -1;
bool verbose_ = false;
std::string user;
int context_size = -1, ngl = -1;
float temperature = -1;
bool verbose = false;
private:
bool help_ = false;
int context_size_default = -1, ngl_default = -1;
float temperature_default = -1;
bool help = false;
bool parse_flag(const char ** argv, int i, const char * short_opt, const char * long_opt) {
return strcmp(argv[i], short_opt) == 0 || strcmp(argv[i], long_opt) == 0;
@ -89,6 +111,17 @@ class Opt {
}
option_value = std::atoi(argv[++i]);
return 0;
}
int handle_option_with_value(int argc, const char ** argv, int & i, float & option_value) {
if (i + 1 >= argc) {
return 1;
}
option_value = std::atof(argv[++i]);
return 0;
}
@ -96,18 +129,22 @@ class Opt {
bool options_parsing = true;
for (int i = 1, positional_args_i = 0; i < argc; ++i) {
if (options_parsing && (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0)) {
if (handle_option_with_value(argc, argv, i, context_size_) == 1) {
if (handle_option_with_value(argc, argv, i, context_size) == 1) {
return 1;
}
} else if (options_parsing && (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "--ngl") == 0)) {
if (handle_option_with_value(argc, argv, i, ngl_) == 1) {
if (handle_option_with_value(argc, argv, i, ngl) == 1) {
return 1;
}
} else if (options_parsing && strcmp(argv[i], "--temp") == 0) {
if (handle_option_with_value(argc, argv, i, temperature) == 1) {
return 1;
}
} else if (options_parsing &&
(parse_flag(argv, i, "-v", "--verbose") || parse_flag(argv, i, "-v", "--log-verbose"))) {
verbose_ = true;
verbose = true;
} else if (options_parsing && parse_flag(argv, i, "-h", "--help")) {
help_ = true;
help = true;
return 0;
} else if (options_parsing && strcmp(argv[i], "--") == 0) {
options_parsing = false;
@ -120,16 +157,16 @@ class Opt {
model_ = argv[i];
} else if (positional_args_i == 1) {
++positional_args_i;
user_ = argv[i];
user = argv[i];
} else {
user_ += " " + std::string(argv[i]);
user += " " + std::string(argv[i]);
}
}
return 0;
}
void help() const {
void print_help() const {
printf(
"Description:\n"
" Runs a llm\n"
@ -142,6 +179,8 @@ class Opt {
" Context size (default: %d)\n"
" -n, --ngl <value>\n"
" Number of GPU layers (default: %d)\n"
" --temp <value>\n"
" Temperature (default: %.1f)\n"
" -v, --verbose, --log-verbose\n"
" Set verbosity level to infinity (i.e. log all messages, useful for debugging)\n"
" -h, --help\n"
@ -170,7 +209,7 @@ class Opt {
" llama-run file://some-file3.gguf\n"
" llama-run --ngl 999 some-file4.gguf\n"
" llama-run --ngl 999 some-file5.gguf Hello World\n",
llama_context_default_params().n_batch, llama_model_default_params().n_gpu_layers);
context_size_default, ngl_default, temperature_default);
}
};
@ -495,12 +534,12 @@ class LlamaData {
return 1;
}
context = initialize_context(model, opt.context_size_);
context = initialize_context(model, opt);
if (!context) {
return 1;
}
sampler = initialize_sampler();
sampler = initialize_sampler(opt);
return 0;
}
@ -619,14 +658,12 @@ class LlamaData {
// Initializes the model and returns a unique pointer to it
llama_model_ptr initialize_model(Opt & opt) {
ggml_backend_load_all();
llama_model_params model_params = llama_model_default_params();
model_params.n_gpu_layers = opt.ngl_ >= 0 ? opt.ngl_ : model_params.n_gpu_layers;
resolve_model(opt.model_);
printe(
"\r%*s"
"\rLoading model",
get_terminal_width(), " ");
llama_model_ptr model(llama_load_model_from_file(opt.model_.c_str(), model_params));
llama_model_ptr model(llama_load_model_from_file(opt.model_.c_str(), opt.model_params));
if (!model) {
printe("%s: error: unable to load model from file: %s\n", __func__, opt.model_.c_str());
}
@ -636,10 +673,8 @@ class LlamaData {
}
// Initializes the context with the specified parameters
llama_context_ptr initialize_context(const llama_model_ptr & model, const int n_ctx) {
llama_context_params ctx_params = llama_context_default_params();
ctx_params.n_ctx = ctx_params.n_batch = n_ctx >= 0 ? n_ctx : ctx_params.n_batch;
llama_context_ptr context(llama_new_context_with_model(model.get(), ctx_params));
llama_context_ptr initialize_context(const llama_model_ptr & model, const Opt & opt) {
llama_context_ptr context(llama_new_context_with_model(model.get(), opt.ctx_params));
if (!context) {
printe("%s: error: failed to create the llama_context\n", __func__);
}
@ -648,10 +683,10 @@ class LlamaData {
}
// Initializes and configures the sampler
llama_sampler_ptr initialize_sampler() {
llama_sampler_ptr initialize_sampler(const Opt & opt) {
llama_sampler_ptr sampler(llama_sampler_chain_init(llama_sampler_chain_default_params()));
llama_sampler_chain_add(sampler.get(), llama_sampler_init_min_p(0.05f, 1));
llama_sampler_chain_add(sampler.get(), llama_sampler_init_temp(0.8f));
llama_sampler_chain_add(sampler.get(), llama_sampler_init_temp(opt.temperature));
llama_sampler_chain_add(sampler.get(), llama_sampler_init_dist(LLAMA_DEFAULT_SEED));
return sampler;
@ -798,9 +833,9 @@ static int apply_chat_template_with_error_handling(LlamaData & llama_data, const
}
// Helper function to handle user input
static int handle_user_input(std::string & user_input, const std::string & user_) {
if (!user_.empty()) {
user_input = user_;
static int handle_user_input(std::string & user_input, const std::string & user) {
if (!user.empty()) {
user_input = user;
return 0; // No need for interactive input
}
@ -832,17 +867,17 @@ static bool is_stdout_a_terminal() {
}
// Function to tokenize the prompt
static int chat_loop(LlamaData & llama_data, const std::string & user_) {
static int chat_loop(LlamaData & llama_data, const std::string & user) {
int prev_len = 0;
llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get()));
static const bool stdout_a_terminal = is_stdout_a_terminal();
while (true) {
// Get user input
std::string user_input;
while (handle_user_input(user_input, user_)) {
while (handle_user_input(user_input, user)) {
}
add_message("user", user_.empty() ? user_input : user_, llama_data);
add_message("user", user.empty() ? user_input : user, llama_data);
int new_len;
if (apply_chat_template_with_error_handling(llama_data, true, new_len) < 0) {
return 1;
@ -854,7 +889,7 @@ static int chat_loop(LlamaData & llama_data, const std::string & user_) {
return 1;
}
if (!user_.empty()) {
if (!user.empty()) {
break;
}
@ -869,7 +904,7 @@ static int chat_loop(LlamaData & llama_data, const std::string & user_) {
static void log_callback(const enum ggml_log_level level, const char * text, void * p) {
const Opt * opt = static_cast<Opt *>(p);
if (opt->verbose_ || level == GGML_LOG_LEVEL_ERROR) {
if (opt->verbose || level == GGML_LOG_LEVEL_ERROR) {
printe("%s", text);
}
}
@ -890,11 +925,11 @@ int main(int argc, const char ** argv) {
}
if (!is_stdin_a_terminal()) {
if (!opt.user_.empty()) {
opt.user_ += "\n\n";
if (!opt.user.empty()) {
opt.user += "\n\n";
}
opt.user_ += read_pipe_data();
opt.user += read_pipe_data();
}
llama_log_set(log_callback, &opt);
@ -903,7 +938,7 @@ int main(int argc, const char ** argv) {
return 1;
}
if (chat_loop(llama_data, opt.user_)) {
if (chat_loop(llama_data, opt.user)) {
return 1;
}

View File

@ -724,7 +724,8 @@ This endpoint is public (no API key check). By default, it is read-only. To make
},
"total_slots": 1,
"model_path": "../models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf",
"chat_template": "..."
"chat_template": "...",
"build_info": "b(build number)-(build commit hash)"
}
```

View File

@ -595,10 +595,11 @@ struct server_task_result_cmpl_final : server_task_result {
std::time_t t = std::time(0);
json res = json {
{"choices", json::array({choice})},
{"created", t},
{"model", oaicompat_model},
{"object", "chat.completion"},
{"choices", json::array({choice})},
{"created", t},
{"model", oaicompat_model},
{"system_fingerprint", build_info},
{"object", "chat.completion"},
{"usage", json {
{"completion_tokens", n_decoded},
{"prompt_tokens", n_prompt_tokens},
@ -632,11 +633,12 @@ struct server_task_result_cmpl_final : server_task_result {
};
json ret = json {
{"choices", json::array({choice})},
{"created", t},
{"id", oaicompat_cmpl_id},
{"model", oaicompat_model},
{"object", "chat.completion.chunk"},
{"choices", json::array({choice})},
{"created", t},
{"id", oaicompat_cmpl_id},
{"model", oaicompat_model},
{"system_fingerprint", build_info},
{"object", "chat.completion.chunk"},
{"usage", json {
{"completion_tokens", n_decoded},
{"prompt_tokens", n_prompt_tokens},
@ -761,11 +763,12 @@ struct server_task_result_cmpl_partial : server_task_result {
}
json ret = json {
{"choices", choices},
{"created", t},
{"id", oaicompat_cmpl_id},
{"model", oaicompat_model},
{"object", "chat.completion.chunk"}
{"choices", choices},
{"created", t},
{"id", oaicompat_cmpl_id},
{"model", oaicompat_model},
{"system_fingerprint", build_info},
{"object", "chat.completion.chunk"}
};
if (timings.prompt_n >= 0) {
@ -3476,6 +3479,7 @@ int main(int argc, char ** argv) {
{ "total_slots", ctx_server.params_base.n_parallel },
{ "model_path", ctx_server.params_base.model },
{ "chat_template", llama_get_chat_template(ctx_server.model) },
{ "build_info", build_info },
};
res_ok(res, data);
@ -3697,7 +3701,7 @@ int main(int argc, char ** argv) {
{"object", "list"},
{"data", {
{
{"id", params.model_alias},
{"id", params.model_alias.empty() ? params.model : params.model_alias},
{"object", "model"},
{"created", std::time(0)},
{"owned_by", "llamacpp"},

View File

@ -31,6 +31,7 @@ def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_conte
})
assert res.status_code == 200
assert "cmpl" in res.body["id"] # make sure the completion id has the expected format
assert res.body["system_fingerprint"].startswith("b")
assert res.body["model"] == model if model is not None else server.model_alias
assert res.body["usage"]["prompt_tokens"] == n_prompt
assert res.body["usage"]["completion_tokens"] == n_predicted
@ -63,6 +64,7 @@ def test_chat_completion_stream(system_prompt, user_prompt, max_tokens, re_conte
last_cmpl_id = None
for data in res:
choice = data["choices"][0]
assert data["system_fingerprint"].startswith("b")
assert "gpt-3.5" in data["model"] # DEFAULT_OAICOMPAT_MODEL, maybe changed in the future
if last_cmpl_id is None:
last_cmpl_id = data["id"]
@ -92,6 +94,7 @@ def test_chat_completion_with_openai_library():
seed=42,
temperature=0.8,
)
assert res.system_fingerprint is not None and res.system_fingerprint.startswith("b")
assert res.choices[0].finish_reason == "length"
assert res.choices[0].message.content is not None
assert match_regex("(Suddenly)+", res.choices[0].message.content)

View File

@ -56,6 +56,8 @@ static T json_value(const json & body, const std::string & key, const T & defaul
}
}
const static std::string build_info("b" + std::to_string(LLAMA_BUILD_NUMBER) + "-" + LLAMA_COMMIT);
//
// tokenizer and input processing utils
//

View File

@ -196,7 +196,11 @@ option(GGML_BUILD_EXAMPLES "ggml: build examples" ${GGML_STANDALONE})
set(CMAKE_C_STANDARD 11)
set(CMAKE_C_STANDARD_REQUIRED true)
set(CMAKE_CXX_STANDARD 17)
if (GGML_SYCL)
set(CMAKE_CXX_STANDARD 17)
else()
set(CMAKE_CXX_STANDARD 14)
endif()
set(CMAKE_CXX_STANDARD_REQUIRED true)
set(THREADS_PREFER_PTHREAD_FLAG ON)

View File

@ -234,6 +234,7 @@ function(ggml_add_backend_library backend)
# write the shared library to the output directory
set_target_properties(${backend} PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
target_compile_definitions(${backend} PRIVATE GGML_BACKEND_DL)
add_dependencies(ggml ${backend})
else()
add_library(${backend} ${ARGN})
target_link_libraries(ggml PUBLIC ${backend})

View File

@ -66,6 +66,26 @@
#include "ggml-kompute.h"
#endif
// disable C++17 deprecation warning for std::codecvt_utf8
#if defined(__clang__)
# pragma clang diagnostic push
# pragma clang diagnostic ignored "-Wdeprecated-declarations"
#endif
static std::wstring utf8_to_utf16(const std::string & str) {
std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
return converter.from_bytes(str);
}
static std::string utf16_to_utf8(const std::wstring & str) {
std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
return converter.to_bytes(str);
}
#if defined(__clang__)
# pragma clang diagnostic pop
#endif
#ifdef _WIN32
using dl_handle = std::remove_pointer_t<HMODULE>;
@ -88,11 +108,6 @@ static dl_handle * dl_load_library(const std::wstring & path) {
return handle;
}
static dl_handle * dl_load_library(const std::string & path) {
std::wstring_convert<std::codecvt_utf8_utf16<wchar_t>> converter;
return dl_load_library(converter.from_bytes(path));
}
static void * dl_get_sym(dl_handle * handle, const char * name) {
DWORD old_mode = SetErrorMode(SEM_FAILCRITICALERRORS);
SetErrorMode(old_mode | SEM_FAILCRITICALERRORS);
@ -114,8 +129,8 @@ struct dl_handle_deleter {
}
};
static void * dl_load_library(const std::string & path) {
dl_handle * handle = dlopen(path.c_str(), RTLD_NOW | RTLD_LOCAL);
static void * dl_load_library(const std::wstring & path) {
dl_handle * handle = dlopen(utf16_to_utf8(path).c_str(), RTLD_NOW | RTLD_LOCAL);
return handle;
}
@ -202,11 +217,11 @@ struct ggml_backend_registry {
devices.push_back(device);
}
ggml_backend_reg_t load_backend(const char * path, bool silent) {
ggml_backend_reg_t load_backend(const std::wstring & path, bool silent) {
dl_handle_ptr handle { dl_load_library(path) };
if (!handle) {
if (!silent) {
GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path);
GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(path).c_str());
}
return nullptr;
}
@ -214,7 +229,7 @@ struct ggml_backend_registry {
auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
if (score_fn && score_fn() == 0) {
if (!silent) {
GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, path);
GGML_LOG_INFO("%s: backend %s is not supported on this system\n", __func__, utf16_to_utf8(path).c_str());
}
return nullptr;
}
@ -222,7 +237,7 @@ struct ggml_backend_registry {
auto backend_init_fn = (ggml_backend_init_t) dl_get_sym(handle.get(), "ggml_backend_init");
if (!backend_init_fn) {
if (!silent) {
GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, path);
GGML_LOG_ERROR("%s: failed to find ggml_backend_init in %s\n", __func__, utf16_to_utf8(path).c_str());
}
return nullptr;
}
@ -231,16 +246,16 @@ struct ggml_backend_registry {
if (!reg || reg->api_version != GGML_BACKEND_API_VERSION) {
if (!silent) {
if (!reg) {
GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n", __func__, path);
GGML_LOG_ERROR("%s: failed to initialize backend from %s: ggml_backend_init returned NULL\n", __func__, utf16_to_utf8(path).c_str());
} else {
GGML_LOG_ERROR("%s: failed to initialize backend from %s: incompatible API version (backend: %d, current: %d)\n",
__func__, path, reg->api_version, GGML_BACKEND_API_VERSION);
__func__, utf16_to_utf8(path).c_str(), reg->api_version, GGML_BACKEND_API_VERSION);
}
}
return nullptr;
}
GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), path);
GGML_LOG_INFO("%s: loaded %s backend from %s\n", __func__, ggml_backend_reg_name(reg), utf16_to_utf8(path).c_str());
register_backend(reg, std::move(handle));
@ -376,14 +391,14 @@ ggml_backend_t ggml_backend_init_best(void) {
// Dynamic loading
ggml_backend_reg_t ggml_backend_load(const char * path) {
return get_reg().load_backend(path, false);
return get_reg().load_backend(utf8_to_utf16(path), false);
}
void ggml_backend_unload(ggml_backend_reg_t reg) {
get_reg().unload_backend(reg, true);
}
static std::string get_executable_path() {
static std::wstring get_executable_path() {
#if defined(__APPLE__)
// get executable path
std::vector<char> path;
@ -401,13 +416,17 @@ static std::string get_executable_path() {
if (last_slash != std::string::npos) {
base_path = base_path.substr(0, last_slash);
}
return base_path + "/";
#elif defined(__linux__)
return utf8_to_utf16(base_path + "/");
#elif defined(__linux__) || defined(__FreeBSD__)
std::string base_path = ".";
std::vector<char> path(1024);
while (true) {
// get executable path
# if defined(__linux__)
ssize_t len = readlink("/proc/self/exe", path.data(), path.size());
# elif defined(__FreeBSD__)
ssize_t len = readlink("/proc/curproc/file", path.data(), path.size());
# endif
if (len == -1) {
break;
}
@ -423,57 +442,63 @@ static std::string get_executable_path() {
path.resize(path.size() * 2);
}
return base_path + "/";
return utf8_to_utf16(base_path + "/");
#elif defined(_WIN32)
std::vector<char> path(MAX_PATH);
DWORD len = GetModuleFileNameA(NULL, path.data(), path.size());
std::vector<wchar_t> path(MAX_PATH);
DWORD len = GetModuleFileNameW(NULL, path.data(), path.size());
if (len == 0) {
return "";
return {};
}
std::string base_path(path.data(), len);
std::wstring base_path(path.data(), len);
// remove executable name
auto last_slash = base_path.find_last_of('\\');
if (last_slash != std::string::npos) {
base_path = base_path.substr(0, last_slash);
}
return base_path + "\\";
return base_path + L"\\";
#else
return {};
#endif
}
static std::string backend_filename_prefix() {
static std::wstring backend_filename_prefix() {
#ifdef _WIN32
return "ggml-";
return L"ggml-";
#else
return "libggml-";
return L"libggml-";
#endif
}
static std::string backend_filename_suffix() {
static std::wstring backend_filename_suffix() {
#ifdef _WIN32
return ".dll";
return L".dll";
#else
return ".so";
return L".so";
#endif
}
static std::wstring path_separator() {
#ifdef _WIN32
return L"\\";
#else
return L"/";
#endif
}
static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent, const char * user_search_path) {
// enumerate all the files that match [lib]ggml-name-*.[so|dll] in the search paths
// TODO: search system paths
std::string file_prefix = backend_filename_prefix() + name + "-";
std::vector<std::string> search_paths;
std::wstring file_prefix = backend_filename_prefix() + utf8_to_utf16(name) + L"-";
std::vector<std::wstring> search_paths;
if (user_search_path == nullptr) {
search_paths.push_back("./");
search_paths.push_back(L"." + path_separator());
search_paths.push_back(get_executable_path());
} else {
#if defined(_WIN32)
search_paths.push_back(std::string(user_search_path) + "\\");
#else
search_paths.push_back(std::string(user_search_path) + "/");
#endif
search_paths.push_back(utf8_to_utf16(user_search_path) + path_separator());
}
int best_score = 0;
std::string best_path;
std::wstring best_path;
namespace fs = std::filesystem;
for (const auto & search_path : search_paths) {
@ -483,27 +508,27 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
for (const auto & entry : dir_it) {
if (entry.is_regular_file()) {
std::string filename = entry.path().filename().string();
std::string ext = entry.path().extension().string();
std::wstring filename = entry.path().filename().wstring();
std::wstring ext = entry.path().extension().wstring();
if (filename.find(file_prefix) == 0 && ext == backend_filename_suffix()) {
dl_handle_ptr handle { dl_load_library(entry.path().c_str()) };
dl_handle_ptr handle { dl_load_library(entry.path().wstring()) };
if (!handle && !silent) {
GGML_LOG_ERROR("%s: failed to load %s\n", __func__, entry.path().string().c_str());
GGML_LOG_ERROR("%s: failed to load %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
}
if (handle) {
auto score_fn = (ggml_backend_score_t) dl_get_sym(handle.get(), "ggml_backend_score");
if (score_fn) {
int s = score_fn();
#ifndef NDEBUG
GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, entry.path().string().c_str(), s);
GGML_LOG_DEBUG("%s: %s score: %d\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str(), s);
#endif
if (s > best_score) {
best_score = s;
best_path = entry.path().string();
best_path = entry.path().wstring();
}
} else {
if (!silent) {
GGML_LOG_INFO("%s: failed to find ggml_backend_score in %s\n", __func__, entry.path().string().c_str());
GGML_LOG_INFO("%s: failed to find ggml_backend_score in %s\n", __func__, utf16_to_utf8(entry.path().wstring()).c_str());
}
}
}
@ -515,15 +540,15 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
if (best_score == 0) {
// try to load the base backend
for (const auto & search_path : search_paths) {
std::string path = search_path + backend_filename_prefix() + name + backend_filename_suffix();
std::wstring path = search_path + backend_filename_prefix() + utf8_to_utf16(name) + backend_filename_suffix();
if (fs::exists(path)) {
return get_reg().load_backend(path.c_str(), silent);
return get_reg().load_backend(path, silent);
}
}
return nullptr;
}
return get_reg().load_backend(best_path.c_str(), silent);
return get_reg().load_backend(best_path, silent);
}
void ggml_backend_load_all() {

View File

@ -135,14 +135,20 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
endif()
# show enabled features
if (CMAKE_HOST_SYSTEM_NAME STREQUAL "Windows")
set(FEAT_INPUT_FILE "NUL")
else()
set(FEAT_INPUT_FILE "/dev/null")
endif()
execute_process(
COMMAND ${CMAKE_C_COMPILER} ${ARCH_FLAGS} -dM -E -
INPUT_FILE "/dev/null"
INPUT_FILE ${FEAT_INPUT_FILE}
OUTPUT_VARIABLE ARM_FEATURE
RESULT_VARIABLE ARM_FEATURE_RESULT
)
if (ARM_FEATURE_RESULT)
message(FATAL_ERROR "Failed to get ARM features")
message(WARNING "Failed to get ARM features")
else()
foreach(feature DOTPROD SVE MATMUL_INT8 FMA FP16_VECTOR_ARITHMETIC)
string(FIND "${ARM_FEATURE}" "__ARM_FEATURE_${feature} 1" feature_pos)
@ -317,6 +323,11 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
target_compile_definitions(${GGML_CPU_NAME} PRIVATE ${ARCH_DEFINITIONS})
if (GGML_BACKEND_DL)
if (GGML_NATIVE)
# the feature check relies on ARCH_DEFINITIONS, but it is not set with GGML_NATIVE
message(FATAL_ERROR "GGML_NATIVE is not compatible with GGML_BACKEND_DL, consider using GGML_CPU_ALL_VARIANTS")
endif()
# The feature detection code is compiled as a separate target so that
# it can be built without the architecture flags
# Since multiple variants of the CPU backend may be included in the same

View File

@ -986,7 +986,7 @@ inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) {
#define GGML_F16_STEP 32
#define GGML_F16_EPR 4
static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) {
static inline __m128 __sse_f16x4_load(const ggml_fp16_t * x) {
float tmp[4];
tmp[0] = GGML_FP16_TO_FP32(x[0]);
@ -997,7 +997,7 @@ static inline __m128 __sse_f16x4_load(ggml_fp16_t *x) {
return _mm_loadu_ps(tmp);
}
static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) {
static inline void __sse_f16x4_store(ggml_fp16_t * x, __m128 y) {
float arr[4];
_mm_storeu_ps(arr, y);

View File

@ -2693,6 +2693,8 @@ bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size) {
}
return true;
#else
(void)buffer;
(void)size;
return false;
#endif
}

View File

@ -221,6 +221,7 @@ class GGUFType:
class MODEL_ARCH(IntEnum):
LLAMA = auto()
DECI = auto()
FALCON = auto()
BAICHUAN = auto()
GROK = auto()
@ -402,6 +403,7 @@ class MODEL_TENSOR(IntEnum):
MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.LLAMA: "llama",
MODEL_ARCH.DECI: "deci",
MODEL_ARCH.FALCON: "falcon",
MODEL_ARCH.BAICHUAN: "baichuan",
MODEL_ARCH.GROK: "grok",
@ -602,6 +604,26 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
],
MODEL_ARCH.DECI: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_GATE_INP,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.FFN_GATE_EXP,
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
],
MODEL_ARCH.GROK: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
@ -1448,6 +1470,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
MODEL_ARCH.DECI: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
MODEL_ARCH.BAICHUAN: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,

View File

@ -198,6 +198,7 @@ class TensorNameMap:
"transformer.h.{bid}.self_attention.dense", # falcon
"h.{bid}.self_attention.dense", # bloom
"model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron olmoe olmo2
"model.layers.{bid}.self_attn.linear_attn", # deci
"layers.{bid}.attention.wo", # llama-pth
"encoder.layer.{bid}.attention.output.dense", # bert
"transformer.h.{bid}.attn.out_proj", # gpt-j

View File

@ -146,6 +146,7 @@ static std::string format(const char * fmt, ...) {
enum llm_arch {
LLM_ARCH_LLAMA,
LLM_ARCH_DECI,
LLM_ARCH_FALCON,
LLM_ARCH_BAICHUAN,
LLM_ARCH_GROK,
@ -203,6 +204,7 @@ enum llm_arch {
static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_LLAMA, "llama" },
{ LLM_ARCH_DECI, "deci" },
{ LLM_ARCH_FALCON, "falcon" },
{ LLM_ARCH_GROK, "grok" },
{ LLM_ARCH_GPT2, "gpt2" },
@ -674,6 +676,32 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
{
LLM_ARCH_DECI,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
{
LLM_ARCH_BAICHUAN,
{
@ -1673,6 +1701,7 @@ enum llm_chat_template {
LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN,
LLM_CHAT_TEMPLATE_MISTRAL_V7,
LLM_CHAT_TEMPLATE_PHI_3,
LLM_CHAT_TEMPLATE_FALCON_3,
LLM_CHAT_TEMPLATE_ZEPHYR,
LLM_CHAT_TEMPLATE_MONARCH,
LLM_CHAT_TEMPLATE_GEMMA,
@ -1691,6 +1720,7 @@ enum llm_chat_template {
LLM_CHAT_TEMPLATE_RWKV_WORLD,
LLM_CHAT_TEMPLATE_GRANITE,
LLM_CHAT_TEMPLATE_GIGACHAT,
LLM_CHAT_TEMPLATE_MEGREZ,
LLM_CHAT_TEMPLATE_UNKNOWN,
};
@ -1705,6 +1735,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
{ "mistral-v3-tekken", LLM_CHAT_TEMPLATE_MISTRAL_V3_TEKKEN },
{ "mistral-v7", LLM_CHAT_TEMPLATE_MISTRAL_V7 },
{ "phi3", LLM_CHAT_TEMPLATE_PHI_3 },
{ "falcon3", LLM_CHAT_TEMPLATE_FALCON_3 },
{ "zephyr", LLM_CHAT_TEMPLATE_ZEPHYR },
{ "monarch", LLM_CHAT_TEMPLATE_MONARCH },
{ "gemma", LLM_CHAT_TEMPLATE_GEMMA },
@ -1723,6 +1754,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
{ "rwkv-world", LLM_CHAT_TEMPLATE_RWKV_WORLD },
{ "granite", LLM_CHAT_TEMPLATE_GRANITE },
{ "gigachat", LLM_CHAT_TEMPLATE_GIGACHAT },
{ "megrez", LLM_CHAT_TEMPLATE_MEGREZ },
};
static llm_arch llm_arch_from_string(const std::string & name) {
@ -5692,7 +5724,7 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_DECI || model.arch == LLM_ARCH_FALCON) {
if (hparams.n_rot != hparams.n_embd_head_k) {
throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
}
@ -5732,6 +5764,15 @@ static void llm_load_hparams(
}
}
} break;
case LLM_ARCH_DECI:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_7B; break;
case 80: model.type = e_model::MODEL_70B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_MINICPM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@ -6562,7 +6603,8 @@ static void llm_load_vocab(
} else if (
tokenizer_pre == "llama3" ||
tokenizer_pre == "llama-v3" ||
tokenizer_pre == "llama-bpe") {
tokenizer_pre == "llama-bpe"||
tokenizer_pre == "falcon3") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
vocab.tokenizer_ignore_merges = true;
vocab.tokenizer_add_bos = true;
@ -6663,6 +6705,9 @@ static void llm_load_vocab(
} else if (
tokenizer_pre == "minerva-7b") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MINERVA;
} else if (
tokenizer_pre == "megrez") {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
} else {
throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
}
@ -7936,6 +7981,68 @@ static bool llm_load_tensors(
}
}
} break;
case LLM_ARCH_DECI:
{
model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
model.output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
// if output is NULL, init from the input tok embed
if (model.output == NULL) {
model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
}
for (int i = 0; i < n_layer; ++i) {
auto & layer = model.layers[i];
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(i);
const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
const int64_t n_embd_gqa = hparams.n_embd_v_gqa(i);
const int64_t n_ff = hparams.n_ff(i);
const int64_t n_head = hparams.n_head(i);
const int64_t n_head_kv = hparams.n_head_kv(i);
if (n_head_kv == 0 && n_head > 0) {
// linear attention for DeciLMCausalModel
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
}
else if (n_head_kv > 0) {
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
}
// optional bias tensors
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
}
else {
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
}
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
// optional MLP bias
layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
}
} break;
case LLM_ARCH_MINICPM3:
{
const int64_t n_embd_head_qk_rope = hparams.n_rot;
@ -11305,6 +11412,167 @@ struct llm_build_context {
return gf;
}
struct ggml_cgraph * build_deci() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
// mutable variable, needed during the last layer of the computation to skip unused tokens
int32_t n_tokens = this->n_tokens;
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_head == hparams.n_rot);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
const int64_t n_head_kv = hparams.n_head_kv(il);
const int64_t n_head = hparams.n_head(il);
if (n_head == 0) {
// attention-free layer of Llama-3_1-Nemotron-51B
cur = inpL;
} else {
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);
}
if (n_head > 0 && n_head_kv == 0) {
// "linear attention" of Llama-3_1-Nemotron-51B
cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
cb(cur, "wo", il);
} else if (n_head > 0) {
// self-attention
// rope freq factors for llama3; may return nullptr for llama2 and other models
struct ggml_tensor * rope_factors = build_rope_factors(il);
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
if (model.layers[il].bq) {
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
cb(Qcur, "Qcur", il);
}
struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
if (model.layers[il].bk) {
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
cb(Kcur, "Kcur", il);
}
struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
if (model.layers[il].bv) {
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
cb(Vcur, "Vcur", il);
}
Qcur = ggml_rope_ext(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
Kcur = ggml_rope_ext(
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, lctx, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
n_tokens = n_outputs;
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
// For Granite architecture
if (hparams.f_residual_scale) {
cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
}
// modified to support attention-free layer of Llama-3_1-Nemotron-51B
struct ggml_tensor * ffn_inp = cur;
if (n_head > 0) {
ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
}
// feed-forward network
if (model.layers[il].ffn_gate_inp == nullptr) {
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, lctx, cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
}
// For Granite architecture
if (hparams.f_residual_scale) {
cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
cur = lctx.cvec.apply_to(ctx0, cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm, NULL,
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
// For Granite architecture
if (hparams.f_logit_scale) {
cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
}
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_baichuan() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
@ -17419,6 +17687,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_llama();
} break;
case LLM_ARCH_DECI:
{
result = llm.build_deci();
} break;
case LLM_ARCH_BAICHUAN:
{
result = llm.build_baichuan();
@ -20794,6 +21066,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
// use what we call a normal RoPE, operating on pairs of consecutive head values
case LLM_ARCH_LLAMA:
case LLM_ARCH_DECI:
case LLM_ARCH_BAICHUAN:
case LLM_ARCH_STARCODER:
case LLM_ARCH_PLAMO:
@ -22615,6 +22888,8 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
}
} else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) {
return LLM_CHAT_TEMPLATE_PHI_3;
} else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) {
return LLM_CHAT_TEMPLATE_FALCON_3;
} else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) {
return LLM_CHAT_TEMPLATE_ZEPHYR;
} else if (tmpl_contains("bos_token + message['role']")) {
@ -22661,6 +22936,8 @@ static llm_chat_template llama_chat_detect_template(const std::string & tmpl) {
return LLM_CHAT_TEMPLATE_GRANITE;
} else if (tmpl_contains("message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1]")) {
return LLM_CHAT_TEMPLATE_GIGACHAT;
} else if (tmpl_contains("<|role_start|>")) {
return LLM_CHAT_TEMPLATE_MEGREZ;
}
return LLM_CHAT_TEMPLATE_UNKNOWN;
}
@ -22767,6 +23044,15 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<|assistant|>\n";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_FALCON_3) {
// Falcon 3
for (auto message : chat) {
std::string role(message->role);
ss << "<|" << role << "|>\n" << message->content << "\n";
}
if (add_ass) {
ss << "<|assistant|>\n";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_ZEPHYR) {
// zephyr template
for (auto message : chat) {
@ -23010,6 +23296,16 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "assistant<|role_sep|>";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_MEGREZ) {
// Megrez template
for (auto message : chat) {
std::string role(message->role);
ss << "<|role_start|>" << role << "<|role_end|>" << message->content << "<|turn_end|>";
}
if (add_ass) {
ss << "<|role_start|>assistant<|role_end|>";
}
} else {
// template not supported
return -1;

View File

@ -77,6 +77,8 @@ int main(void) {
"{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + '[/INST]' }}{% elif message['role'] == 'system' %}{{ '[SYSTEM_PROMPT] ' + message['content'] + '[/SYSTEM_PROMPT]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + message['content'] + eos_token }}{% else %}{{ raise_exception('Only user, system and assistant roles are supported!') }}{% endif %}{% endfor %}",
// ai-sage/GigaChat-20B-A3B-instruct
"{% if messages[0]['role'] == 'system' -%}\n {%- set loop_messages = messages[1:] -%}\n {%- set system_message = bos_token + messages[0]['content'] + additional_special_tokens[1] -%}\n{%- else -%}\n {%- set loop_messages = messages -%}\n {%- set system_message = bos_token + '' -%}\n{%- endif -%}\n{%- for message in loop_messages %}\n {% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n {{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}\n {% endif %}\n \n {%- if loop.index0 == 0 -%}\n {{ system_message -}}\n {%- endif -%}\n {%- if message['role'] == 'user' -%}\n {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n {{ 'available functions' + additional_special_tokens[0] + additional_special_tokens[2] + additional_special_tokens[3] + additional_special_tokens[1] -}}\n {%- endif -%}\n {%- if message['role'] == 'assistant' -%}\n {{ message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1] -}}\n {%- endif -%}\n {%- if loop.last and add_generation_prompt -%}\n {{ 'assistant' + additional_special_tokens[0] -}}\n {%- endif -%}\n{%- endfor %}",
// Infinigence/Megrez-3B-Instruct
u8"{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|role_start|>system<|role_end|>你是Megrez-3B-Instruct将针对用户的问题给出详细的、积极的回答。<|turn_end|>' }}{% endif %}{{ '<|role_start|>' + message['role'] + '<|role_end|>' + message['content'] + '<|turn_end|>' }}{% endfor %}{% if add_generation_prompt %}{{ '<|role_start|>assistant<|role_end|>' }}{% endif %}"
};
std::vector<std::string> expected_output = {
// teknium/OpenHermes-2.5-Mistral-7B
@ -133,6 +135,8 @@ int main(void) {
"[SYSTEM_PROMPT] You are a helpful assistant[/SYSTEM_PROMPT][INST] Hello[/INST] Hi there</s>[INST] Who are you[/INST] I am an assistant </s>[INST] Another question[/INST]",
// ai-sage/GigaChat-20B-A3B-instruct
"<s>You are a helpful assistant<|message_sep|>user<|role_sep|>Hello<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>Hi there<|message_sep|>user<|role_sep|>Who are you<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|> I am an assistant <|message_sep|>user<|role_sep|>Another question<|message_sep|>available functions<|role_sep|>[]<|message_sep|>assistant<|role_sep|>",
// Infinigence/Megrez-3B-Instruct
"<|role_start|>system<|role_end|>You are a helpful assistant<|turn_end|><|role_start|>user<|role_end|>Hello<|turn_end|><|role_start|>assistant<|role_end|>Hi there<|turn_end|><|role_start|>user<|role_end|>Who are you<|turn_end|><|role_start|>assistant<|role_end|> I am an assistant <|turn_end|><|role_start|>user<|role_end|>Another question<|turn_end|><|role_start|>assistant<|role_end|>",
};
std::vector<char> formatted_chat(1024);
int32_t res;