Text Generation
Transformers
PyTorch
Safetensors
mt5
text2text-generation
medical
multilingual
medic
Instructions to use HiTZ/Medical-mT5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HiTZ/Medical-mT5-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HiTZ/Medical-mT5-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("HiTZ/Medical-mT5-large") model = AutoModelForSeq2SeqLM.from_pretrained("HiTZ/Medical-mT5-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HiTZ/Medical-mT5-large with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HiTZ/Medical-mT5-large" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HiTZ/Medical-mT5-large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HiTZ/Medical-mT5-large
- SGLang
How to use HiTZ/Medical-mT5-large 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 "HiTZ/Medical-mT5-large" \ --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": "HiTZ/Medical-mT5-large", "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 "HiTZ/Medical-mT5-large" \ --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": "HiTZ/Medical-mT5-large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HiTZ/Medical-mT5-large with Docker Model Runner:
docker model run hf.co/HiTZ/Medical-mT5-large
Update README.md
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README.md
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medical domain data for English, Spanish, French, and Italian.
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- 📖 Paper: [Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain]()
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- 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
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## Citation
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We will soon release a paper, but, for now, you can use:
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```bibtext
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```
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medical domain data for English, Spanish, French, and Italian.
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</p>
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- 📖 Paper: [Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain](https://arxiv.org/abs/2404.07613)
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- 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
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## Citation
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```bibtext
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@misc{garcíaferrero2024medical,
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title={Medical mT5: An Open-Source Multilingual Text-to-Text LLM for The Medical Domain},
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author={Iker García-Ferrero and Rodrigo Agerri and Aitziber Atutxa Salazar and Elena Cabrio and Iker de la Iglesia and Alberto Lavelli and Bernardo Magnini and Benjamin Molinet and Johana Ramirez-Romero and German Rigau and Jose Maria Villa-Gonzalez and Serena Villata and Andrea Zaninello},
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year={2024},
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eprint={2404.07613},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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