Image-Text-to-Text
Transformers
Safetensors
English
medical
pathology
vision-language
reasoning
multimodal
vlm
How to use from
SGLangUse Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "jshhhh/PathReasoner-R1" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "jshhhh/PathReasoner-R1",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
PathReasoner-R1
Model weights for the paper PathReasoner-R1: Empowering Pathology Vision Language Models with Structured Diagnostic Reasoning.
Overview
PathReasoner-R1 is a pathology vision-language model trained to perform structured diagnostic reasoning over histopathology images. The model produces step-by-step reasoning grounded in a medical knowledge graph enabling clinically valid interpretations.
Model Details
- Base model: Qwen2.5-VL-7B
- Training data: PathReasoner Dataset
- Task: Pathology image understanding with structured reasoning
- Languages: English
License
This model is released under CC BY-NC 4.0 — free for academic and research use, not for commercial use or clinical deployment.
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Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "jshhhh/PathReasoner-R1" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jshhhh/PathReasoner-R1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'