ht-docker-ai/Dockerfile_nanonets_vllm_gpu_VRAM10GB

# Nanonets-OCR2-3B Vision Language Model
# Based on Qwen2.5-VL-3B, fine-tuned for document OCR (Oct 2025 release)
# Improvements over OCR-s: better semantic tagging, LaTeX equations, flowcharts
# ~12-16GB VRAM with 30K context, outputs structured markdown with semantic tags
#
# Build: docker build -f Dockerfile_nanonets_vllm_gpu_VRAM10GB -t nanonets-ocr .
# Run:   docker run --gpus all -p 8000:8000 -v ht-huggingface-cache:/root/.cache/huggingface nanonets-ocr

FROM vllm/vllm-openai:latest

LABEL maintainer="Task Venture Capital GmbH <hello@task.vc>"
LABEL description="Nanonets-OCR2-3B - Document OCR optimized Vision Language Model"
LABEL org.opencontainers.image.source="https://code.foss.global/host.today/ht-docker-ai"

# Environment configuration
ENV MODEL_NAME="nanonets/Nanonets-OCR2-3B"
ENV HOST="0.0.0.0"
ENV PORT="8000"
ENV MAX_MODEL_LEN="30000"
ENV GPU_MEMORY_UTILIZATION="0.9"

# Expose OpenAI-compatible API port
EXPOSE 8000

# Health check - vLLM exposes /health endpoint
HEALTHCHECK --interval=30s --timeout=10s --start-period=120s --retries=5 \
    CMD curl -f http://localhost:8000/health || exit 1

# Start vLLM server with Nanonets-OCR2-3B model
CMD ["--model", "nanonets/Nanonets-OCR2-3B", \
     "--trust-remote-code", \
     "--max-model-len", "30000", \
     "--host", "0.0.0.0", \
     "--port", "8000"]