---
title: "Me LLaMA — paper by Xie et al. (2024) — summary and ideas"
description: "Earlier open medical models seldom combined continual pre-training with instruction tuning, rarely… 45 references, cited by 25 papers here."
authors: ["Qianqian Xie", "Qingyu Chen", "Aokun Chen", "Cheng Peng", "Yan Hu", "Fongci Lin", "Xueqing Peng", "Jimin Huang", "Jeffrey Zhang", "Vipina Keloth", "Xinyu Zhou", "Lingfei Qian", "Huan He", "Dennis Shung", "Lucila Ohno-Machado", "Yonghui Wu", "Hua Xu", "Jiang Bian"]
year: 2024
venue: "arXiv:2402.12749"
source: "https://arxiv.org/abs/2402.12749"
doi: "10.48550/arxiv.2402.12749"
arxiv: "2402.12749"
licence: "CC BY-SA 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by-sa/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xie-2024-me-llama-foundation-large-language/"
---
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# Me LLaMA: Foundation Large Language Models for Medical Applications

**Authors:** [Qianqian Xie](https://md.research.elamur.ai/free/en/authors/qianqian-xie/), [Qingyu Chen](https://md.research.elamur.ai/free/en/authors/qingyu-chen/), Aokun Chen, [Cheng Peng](https://md.research.elamur.ai/free/en/authors/cheng-peng/), [Yan Hu](https://md.research.elamur.ai/free/en/authors/yan-hu/), Fongci Lin, [Xueqing Peng](https://md.research.elamur.ai/free/en/authors/xueqing-peng/), [Jimin Huang](https://md.research.elamur.ai/free/en/authors/jimin-huang/), Jeffrey Zhang, Vipina Keloth, [Xinyu Zhou](https://md.research.elamur.ai/free/en/authors/xinyu-zhou/), [Lingfei Qian](https://md.research.elamur.ai/free/en/authors/lingfei-qian/), Huan He, Dennis Shung, Lucila Ohno-Machado, [Yonghui Wu](https://md.research.elamur.ai/free/en/authors/yonghui-wu/), [Hua Xu](https://md.research.elamur.ai/free/en/authors/hua-xu/) and [Jiang Bian](https://md.research.elamur.ai/free/en/authors/jiang-bian/) · **Year:** 2024 · **Venue:** arXiv:2402.12749 · **Licence:** [CC BY-SA 4.0 (arXiv)](http://creativecommons.org/licenses/by-sa/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2402.12749) · [PDF](https://arxiv.org/pdf/2402.12749) · [DOI 10.48550/arxiv.2402.12749](https://doi.org/10.48550/arxiv.2402.12749) · [arXiv 2402.12749](https://arxiv.org/abs/2402.12749) · [OpenAlex](https://openalex.org/W4392019992)

**Citations:** 8 citations (OpenAlex, 2026-10-14) · [25](https://md.research.elamur.ai/free/en/papers/xie-2024-me-llama-foundation-large-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
General-domain open LLMs such as LLaMA often lack specialized medical knowledge. Commercial systems like ChatGPT and GPT-4 are closed and hard to customize. Earlier open medical models seldom combined continual pre-training with instruction tuning, rarely drew on clinical notes and were evaluated mostly on QA. The authors present Me-LLaMA, a family of 13B and 70B models built on LLaMA2. The base models receive further pre-training on a 129B-token mixture of biomedical papers, de-identified clinical notes and general text. Chat variants are then instruction-tuned on roughly 214K samples. Evaluation covers six text-analysis task types across 12 benchmarks, plus diagnosis of complex clinical cases scored by automatic metrics and human reviewers. Me-LLaMA beats LLaMA2 and prior open medical LLMs in most zero-shot and supervised settings. After task-specific tuning, it exceeds ChatGPT on 7 of 8 datasets and GPT-4 on 5 of 8. Its diagnostic quality is close to both commercial systems. The study shows that domain data is decisive and releases reusable medical foundation models under a data use agreement.

## Key points
- The pre-training corpus has 129B tokens and is the largest in the medical domain. It includes over 3M PubMed Central full texts and over 15M PubMed abstracts (from the Pile), MIMIC-III/MIMIC-IV/MIMIC-CXR notes and a RedPajama subset, mixed at a 15:1:4 ratio of biomedical, clinical and general data.
- The instruction set has 214,595 samples from sources such as Alpaca, Dolly, ShareGPT, MedInstruct, MEDIQA, LiveQA, the UMLS knowledge graph, clinical guidelines and MIMIC.
- Training the 70B models took more than 100,000 A100 GPU hours. The 13B model matched the 70B one on most datasets.
- Tasks include QA, NER, relation extraction, classification, summarization and NLI. Clinical diagnosis was tested on 70 NEJM cases.
- The models are available on PhysioNet under a credentialed DUA, and the code is at GitHub BIDS-Xu-Lab/Me-LLaMA. Limitations include a 4096-token context and possible factual errors or bias.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Yale University](https://md.research.elamur.ai/free/en/universities/yale-university/) — University, New Haven, United States · 95 works · 12,765 citations
- [University of Florida](https://md.research.elamur.ai/free/en/universities/university-of-florida/) — University, Gainesville, United States · 33 works · 364 citations
- [The University of Texas Health Science Center at Houston](https://md.research.elamur.ai/free/en/universities/the-university-of-texas-health-science-center-at-houston/) — University, Houston, United States · 5 works · 36 citations

Also on the byline: [University of Florida Health](https://ror.org/04tk2gy88).

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## Cite

Cite as: Qianqian Xie, Qingyu Chen, Aokun Chen, Cheng Peng, Yan Hu, Fongci Lin, Xueqing Peng, Jimin Huang, Jeffrey Zhang, Vipina Keloth, Xinyu Zhou, Lingfei Qian, Huan He, Dennis Shung, Lucila Ohno-Machado, Yonghui Wu, Hua Xu and Jiang Bian. 2024. Me LLaMA: Foundation Large Language Models for Medical Applications. arXiv:2402.12749.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{xie2024me,
  title = {{Me LLaMA: Foundation Large Language Models for Medical Applications}},
  author = {Qianqian Xie, Qingyu Chen, Aokun Chen, Cheng Peng, Yan Hu, Fongci Lin, Xueqing Peng, Jimin Huang, Jeffrey Zhang, Vipina Keloth, Xinyu Zhou, Lingfei Qian, Huan He, Dennis Shung, Lucila Ohno-Machado, Yonghui Wu, Hua Xu and Jiang Bian},
  year = {2024},
  doi = {10.48550/arxiv.2402.12749},
  eprint = {2402.12749},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2402.12749}
}
```

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## References

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*Updated 31 October 2026*