35 lines
1.3 KiB
Plaintext
35 lines
1.3 KiB
Plaintext
# Nanonets-OCR2-3B Vision Language Model
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# Based on Qwen2.5-VL-3B, fine-tuned for document OCR (Oct 2025 release)
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# Improvements over OCR-s: better semantic tagging, LaTeX equations, flowcharts
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# ~12-16GB VRAM with 30K context, outputs structured markdown with semantic tags
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#
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# Build: docker build -f Dockerfile_nanonets_vllm_gpu_VRAM10GB -t nanonets-ocr .
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# Run: docker run --gpus all -p 8000:8000 -v ht-huggingface-cache:/root/.cache/huggingface nanonets-ocr
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FROM vllm/vllm-openai:latest
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LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
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LABEL description="Nanonets-OCR2-3B - Document OCR optimized Vision Language Model"
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LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"
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# Environment configuration
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ENV MODEL_NAME="nanonets/Nanonets-OCR2-3B"
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ENV HOST="0.0.0.0"
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ENV PORT="8000"
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ENV MAX_MODEL_LEN="30000"
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ENV GPU_MEMORY_UTILIZATION="0.9"
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# Expose OpenAI-compatible API port
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EXPOSE 8000
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# Health check - vLLM exposes /health endpoint
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HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=5 \
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CMD curl -f http://localhost:8000/health || exit 1
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# Start vLLM server with Nanonets-OCR2-3B model
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CMD ["--model", "nanonets/Nanonets-OCR2-3B", \
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"--trust-remote-code", \
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"--max-model-len", "30000", \
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"--host", "0.0.0.0", \
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"--port", "8000"]
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