Text Generation
Transformers
Safetensors
English
cxrmate-2
chest X-ray report generation
radiology report generation
image captioning
chest X-ray
X-ray
radiology
cxrmate
cxrmate-ed
cxrmate-rrg24
report
radiology report
multimodal
patient data
mimic-cxr
custom_code
Instructions to use aehrc/cxrmate-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aehrc/cxrmate-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aehrc/cxrmate-2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("aehrc/cxrmate-2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use aehrc/cxrmate-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aehrc/cxrmate-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aehrc/cxrmate-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aehrc/cxrmate-2
- SGLang
How to use aehrc/cxrmate-2 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 "aehrc/cxrmate-2" \ --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": "aehrc/cxrmate-2", "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 "aehrc/cxrmate-2" \ --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": "aehrc/cxrmate-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aehrc/cxrmate-2 with Docker Model Runner:
docker model run hf.co/aehrc/cxrmate-2
Update README.md
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README.md
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print(f'Findings:\t{findings[0]}\nImpression:\t{impression[0]}')
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```
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[Scripts to create the ReXgradient, CheXpert Plus, and MIMC-CXR Hugging Face datasets will be released soon]
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## Generate from ReXgradient HuggingFace dataset:
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```python
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# Run prepare_chexpert_plus.py or prepare_mimic_cxr_jpg.py or prepare_rexgradient.py to create dataset:
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test_set = datasets.load_from_disk('/scratch3/nic261/database/cxrmate2/rexgradient_160k_dataset')['test']
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print(f'Findings:\t{findings[0]}\nImpression:\t{impression[0]}')
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```
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## Generate from ReXgradient HuggingFace dataset:
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**Scripts to create the ReXgradient, CheXpert Plus, and MIMC-CXR Hugging Face datasets will be released soon**
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```python
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# Run prepare_chexpert_plus.py or prepare_mimic_cxr_jpg.py or prepare_rexgradient.py to create dataset:
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test_set = datasets.load_from_disk('/scratch3/nic261/database/cxrmate2/rexgradient_160k_dataset')['test']
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