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Serve pepekberbulu Q4_K_M GGUF live via llama-server (fast CPU inference)
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# Serve an already-GGUF model with prebuilt llama.cpp binaries (no compile, no convert -> fast).
FROM python:3.11-slim
ENV DEBIAN_FRONTEND=noninteractive \
PYTHONUNBUFFERED=1 \
HF_HOME=/home/user/.cache/huggingface \
HF_HUB_ENABLE_HF_TRANSFER=1
RUN apt-get update && apt-get install -y --no-install-recommends \
curl ca-certificates libgomp1 \
&& rm -rf /var/lib/apt/lists/* \
&& useradd -m -u 1000 user
# ---- Prebuilt llama.cpp CPU binaries: llama-server + shared libs (NO build) ----
RUN mkdir -p /opt/llamabin && cd /opt/llamabin \
&& curl -fsSL -o l.tar.gz \
https://github.com/ggml-org/llama.cpp/releases/download/b9670/llama-b9670-bin-ubuntu-x64.tar.gz \
&& tar xzf l.tar.gz --strip-components=1 \
&& rm l.tar.gz \
&& chmod +x /opt/llamabin/llama-server
ENV LD_LIBRARY_PATH=/opt/llamabin \
PATH=/opt/llamabin:$PATH
# ---- Python deps: downloader + proxy only (no torch/transformers needed) ----
RUN pip install --no-cache-dir \
huggingface_hub hf_transfer \
"fastapi>=0.110" "uvicorn[standard]>=0.27" requests
WORKDIR /home/user/app
COPY app.py .
COPY chat_template.jinja .
RUN chown -R user:user /home/user
USER user
EXPOSE 7860
CMD ["python", "app.py"]