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
File size: 929 Bytes
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"auto_map": {
"AutoProcessor": "processing_cxrmate2.CXRMate2Processor"
},
"convert_to_rgb": false,
"embeddings_per_image": 128,
"generate_findings_token": "<|generate_findings|>",
"generate_impression_token": "<|generate_impression|>",
"image_token": "<|image|>",
"max_generated_tokens": 320,
"max_train_images_per_study": 5,
"mimic_cxr_normalisation": true,
"processor_class": "CXRMate2Processor",
"token_type_to_token": {
"comparison": "<|comparison_token_type|>",
"findings": "<|findings_token_type|>",
"history": "<|history_token_type|>",
"image": "<|image_token_type|>",
"impression": "<|impression_token_type|>",
"indication": "<|indication_token_type|>",
"prior_findings": "<|prior_findings_token_type|>",
"prior_image": "<|prior_image_token_type|>",
"prior_impression": "<|prior_impression_token_type|>",
"technique": "<|technique_token_type|>"
}
}
|