Image-Text-to-Text
Transformers
Safetensors
English
medical
pathology
vision-language
reasoning
multimodal
vlm
Instructions to use jshhhh/PathReasoner-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jshhhh/PathReasoner-R1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="jshhhh/PathReasoner-R1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jshhhh/PathReasoner-R1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jshhhh/PathReasoner-R1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jshhhh/PathReasoner-R1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jshhhh/PathReasoner-R1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jshhhh/PathReasoner-R1
- SGLang
How to use jshhhh/PathReasoner-R1 with SGLang:
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 }'Use 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 }' - Docker Model Runner
How to use jshhhh/PathReasoner-R1 with Docker Model Runner:
docker model run hf.co/jshhhh/PathReasoner-R1
| license: cc-by-nc-4.0 | |
| language: | |
| - en | |
| pipeline_tag: image-text-to-text | |
| tags: | |
| - medical | |
| - pathology | |
| - vision-language | |
| - reasoning | |
| - multimodal | |
| - vlm | |
| library_name: transformers | |
| # 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](https://huggingface.co/datasets/jshhhh/PathReasoner) | |
| - **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**. |