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X-iZhang
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Med-CXRGen-I

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Transformers
PyTorch
Safetensors
libra
text-generation
RRG
Radiology Report Generation
Chest X-ray
Multimodal Large Language Models
Automated Chest X-ray Report Generation
Model card Files
xet
 Community
1
How to use from
vLLM
Install from pip and serve model 
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "X-iZhang/Med-CXRGen-I"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "X-iZhang/Med-CXRGen-I",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker 
docker model run hf.co/X-iZhang/Med-CXRGen-I
Quick Links

Med-CXRGen-I Model Card

Task: Radiology Report Generation – Impression section (RRG Shared Task)

Paper and Resources

For details on Med-CXRGen-I, including its architecture, training strategy, and evaluation—please refer to the following resources:

How to Cite ✒️

If you use this model in academic or research contexts, please cite:

@inproceedings{zhang-etal-2024-gla,
    title = "Gla-{AI}4{B}io{M}ed at {RRG}24: Visual Instruction-tuned Adaptation for Radiology Report Generation",
    author = "Zhang, Xi  and
      Meng, Zaiqiao  and
      Lever, Jake  and
      Ho, Edmond S.L.",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Miwa, Makoto  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.bionlp-1.54/",
    doi = "10.18653/v1/2024.bionlp-1.54",
    pages = "624--634",
}
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