titocosta / meditron

Meditron-7B-v1.0 from Meditron's open-source suite of medical LLMs.

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Meditron-7B-v1.0 from Meditron’s open-source suite of medical LLMs. Supports streaming.

Model card below, github at https://github.com/epfLLM/meditron

Meditron is a suite of open-source medical Large Language Models (LLMs). Meditron-7B is a 7 billion parameters model adapted to the medical domain from Llama-2-7B through continued pretraining on a comprehensively curated medical corpus, including selected PubMed articles, abstracts, a new dataset of internationally-recognized medical guidelines, and general domain data from RedPajama-v1. Meditron-7B, finetuned on relevant training data, outperforms Llama-2-7B and PMC-Llama on multiple medical reasoning tasks.

Advisory Notice While Meditron is designed to encode medical knowledge from sources of high-quality evidence, it is not yet adapted to deliver this knowledge appropriately, safely, or within professional actionable constraints. We recommend against deploying Meditron in medical applications without extensive use-case alignment, as well as additional testing, specifically including randomized controlled trials in real-world practice settings. Model Details Developed by: EPFL LLM Team Model type: Causal decoder-only transformer language model Language(s): English (mainly) Model License: LLAMA 2 COMMUNITY LICENSE AGREEMENT Code License: APACHE 2.0 LICENSE Continue-pretrained from model: Llama-2-7B Context length: 2K tokens Input: Text-only data Output: Model generates text only Status: This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we enhance model’s performance. Knowledge Cutoff: August 2023 Model Sources Repository: epflLLM/meditron Trainer: epflLLM/Megatron-LLM Paper: MediTron-70B: Scaling Medical Pretraining for Large Language Models Uses Meditron-7B is being made available for further testing and assessment as an AI assistant to enhance clinical decision-making and enhance access to an LLM for healthcare use. Potential use cases may include but are not limited to:

Medical exam question answering Supporting differential diagnosis Disease information (symptoms, cause, treatment) query General health information query