Instructions to use solvrays/svg-doc-m1.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use solvrays/svg-doc-m1.gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf solvrays/svg-doc-m1.gguf # Run inference directly in the terminal: llama cli -hf solvrays/svg-doc-m1.gguf
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf solvrays/svg-doc-m1.gguf # Run inference directly in the terminal: llama cli -hf solvrays/svg-doc-m1.gguf
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf solvrays/svg-doc-m1.gguf # Run inference directly in the terminal: ./llama-cli -hf solvrays/svg-doc-m1.gguf
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf solvrays/svg-doc-m1.gguf # Run inference directly in the terminal: ./build/bin/llama-cli -hf solvrays/svg-doc-m1.gguf
Use Docker
docker model run hf.co/solvrays/svg-doc-m1.gguf
- Llama new
- LM Studio
- Jan
- Ollama
How to use solvrays/svg-doc-m1.gguf with Ollama:
ollama run hf.co/solvrays/svg-doc-m1.gguf
- Unsloth Desktop
- Docker Model Runner
How to use solvrays/svg-doc-m1.gguf with Docker Model Runner:
docker model run hf.co/solvrays/svg-doc-m1.gguf
- Lemonade
How to use solvrays/svg-doc-m1.gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull solvrays/svg-doc-m1.gguf
Run and chat with the model
lemonade run user.svg-doc-m1.gguf-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Download .devops/cuda.Dockerfile from solvrays/svg-doc-m1.gguf: direct link, hf CLI and curl.
- Browser
- Download file 2.55 kB
-
https://huggingface.co/solvrays/svg-doc-m1.gguf/resolve/main/.devops/cuda.Dockerfile
- Command line
-
hf download hf://solvrays/svg-doc-m1.gguf/.devops/cuda.Dockerfile
-
curl -L -o cuda.Dockerfile https://huggingface.co/solvrays/svg-doc-m1.gguf/resolve/main/.devops/cuda.Dockerfile
2.55 kB
| ARG UBUNTU_VERSION=22.04 | |
| # This needs to generally match the container host's environment. | |
| ARG CUDA_VERSION=12.4.0 | |
| # 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 -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DLLAMA_BUILD_TESTS=OFF ${CMAKE_ARGS} -DCMAKE_EXE_LINKER_FLAGS=-Wl,--allow-shlib-undefined . && \ | |
| cmake --build build --config Release -j$(nproc) | |
| RUN mkdir -p /app/lib && \ | |
| 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" ] | |