meditron-7b
Maintainer: epfl-llm
204
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model Overview
meditron-7b is a 7 billion parameter model adapted to the medical domain from the Llama-2-7B model. It was developed by the EPFL LLM Team through continued pretraining on a curated medical corpus, including PubMed articles, abstracts, a new dataset of medical guidelines, and general domain data from RedPajama-v1. meditron-7b outperforms Llama-2-7B and PMC-Llama on multiple medical reasoning tasks.
The larger meditron-70b model follows a similar approach, scaling up to 70 billion parameters. It outperforms Llama-2-70B, GPT-3.5 (text-davinci-003), and Flan-PaLM on medical benchmarks.
Model Inputs and Outputs
Inputs
- Text-only data: The model takes textual input only, with a context length of up to 2,048 tokens for
meditron-7band 4,096 tokens formeditron-70b.
Outputs
- Text generation: The model generates text as output. It is not designed for other output modalities like images or structured data.
Capabilities
The meditron models demonstrate strong performance on a variety of medical reasoning tasks, including medical exam question answering, supporting differential diagnosis, and providing disease information. Their medical domain-specific pretraining allows them to encode and apply relevant medical knowledge more effectively than general language models.
What Can I Use It For?
The meditron models are being made available for further testing and assessment as AI assistants to enhance clinical decision-making and improve access to large language models in healthcare. Potential use cases include:
- Medical exam question answering
- Supporting differential diagnosis
- Providing disease information (symptoms, causes, treatments)
- General health information queries
However, the maintainers advise against deploying these models directly in medical applications without extensive testing and alignment with specific use cases, as they have not yet been adapted to deliver medical knowledge appropriately, safely, or within professional constraints.
Things to Try
While it is possible to use the meditron models to generate text, which can be useful for experimentation, the maintainers strongly recommend against using the models directly for production or work that may impact people. Instead, they suggest exploring the use of the models in a more controlled and interactive way, such as by deploying them with a high-throughput and memory-efficient inference engine and a user interface that supports chat and text generation.
The maintainers have provided a deployment guide using the FastChat platform with the vLLM inference engine, and have collected generations for qualitative analysis through the BetterChatGPT interactive UI.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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