Text Generation
Transformers
Safetensors
medical-ai
llm
report-generation
hallucination-detection
fhir
clinical-nlp
Instructions to use mou11/medical-report-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mou11/medical-report-generator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mou11/medical-report-generator")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mou11/medical-report-generator", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mou11/medical-report-generator with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mou11/medical-report-generator" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mou11/medical-report-generator", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mou11/medical-report-generator
- SGLang
How to use mou11/medical-report-generator 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 "mou11/medical-report-generator" \ --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": "mou11/medical-report-generator", "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 "mou11/medical-report-generator" \ --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": "mou11/medical-report-generator", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mou11/medical-report-generator with Docker Model Runner:
docker model run hf.co/mou11/medical-report-generator
Create tokenizer_config.json
Browse files- tokenizer_config.json +17 -0
tokenizer_config.json
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{
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"add_prefix_space": null,
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"backend": "tokenizers",
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"extra_special_tokens": [],
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"is_local": false,
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"legacy": false,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "</s>",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "TokenizersBackend",
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"unk_token": "<unk>",
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"use_default_system_prompt": false
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}
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