# MinerU deployment notes

- Source: `tools/MinerU`, commit `79d6d8d79fb8f3ddba5cc34c07a16f0ec36f56c7`
- Environment: `tools/.envs/mineru` (Python 3.12)
- Versions: MinerU 3.4.4, PyTorch 2.11.0+cu130, vLLM 0.20.2
- Model cache: `tools/model_cache`
- Tested hardware: 8 x NVIDIA H100 NVL 96GB; driver 595.71.05
- Smoke output: `mineru_test_output/smoke_meshgpt`
- Smoke log: `mineru_test_output/logs/smoke_meshgpt.log`

Highest-quality local command for research PDFs:

```bash
CUDA_VISIBLE_DEVICES=0 \
HF_HOME=/data/chenxing/code/docs/tools/model_cache \
MODELSCOPE_CACHE=/data/chenxing/code/docs/tools/model_cache/modelscope \
/data/chenxing/code/docs/tools/.envs/mineru/bin/mineru \
  -p INPUT.pdf -o OUTPUT \
  -b hybrid-engine --effort high --image-analysis true \
  --formula true --table true
```

Eight-GPU batch conversion (one persistent process and model instance per shard):

```bash
./mineru_convert_8gpu.sh papers/pdfs papers/markdown
```

`hybrid-engine` is preferable for born-digital papers: native text extraction limits
OCR/VLM hallucination, while the high-effort VLM path analyzes figures/charts and
retains formulas and tables. Outputs include Markdown, extracted images, content-list
JSON, middle JSON, and a layout PDF for visual QA.