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--- |
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license: cc-by-4.0 |
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datasets: |
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- Henrychur/MMedC |
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language: |
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- en |
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- zh |
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- ja |
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- fr |
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- ru |
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- es |
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tags: |
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- medical |
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--- |
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# MMedLM |
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[💻Github Repo](https://github.com/MAGIC-AI4Med/MMedLM) [🖨️arXiv Paper](https://arxiv.org/abs/2402.13963) |
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The official model weights for "Towards Building Multilingual Language Model for Medicine". |
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## Introduction |
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This repo contains MMedLM 2-1.8B , a multilingual medical foundation model with 1.8 billion parameters. MMedLM 2-1.8B builds upon the foundation of InternLM 2-1.8B and has been further pretrained on MMedC, a comprehensive multilingual medical corpus. This further pretraining enhances the model's medical-domain knowledge. |
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With an auto-regressive continues training on MMedC, MMedLM 2-1.8B can exceed the performance of most 7B models, including InternLM and LLaMA 2. |
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The model underwent further pretraining on MMedC with the following hyperparameters: |
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- Iterations: 15000 |
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- Global batch size: 512 |
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- Cutoff length: 2048 |
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- Learning rate: 2e-5 |
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The model can be loaded as follows: |
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```py |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("Henrychur/MMedLM2-1.8B", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("Henrychur/MMedLM2-1.8B", torch_dtype=torch.float16, trust_remote_code=True) |
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``` |
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- Note that this is a foundation model that has not undergone instruction fine-tuning. |
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## News |
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[2023.3.1] We release [MMedLM 2-1.8B](https://huggingface.co/Henrychur/MMedLM2-1.8B), a 1.8B light-weight model based on InternLM 2-1.8B. With an auto-regressive continues training on MMedC, MMedLM 2-1.8B can exceed the performance of most 7B models, including InternLM and LLaMA 2. |
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[2024.2.21] Our pre-print paper is released ArXiv. Dive into our findings [here](https://arxiv.org/abs/2402.13963). |
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[2024.2.20] We release [MMedLM](https://huggingface.co/Henrychur/MMedLM) and [MMedLM 2](https://huggingface.co/Henrychur/MMedLM2). With an auto-regressive continues training on MMedC, these models achieves superior performance compared to all other open-source models, even rivaling GPT-4 on MMedBench. |
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[2023.2.20] We release [MMedC](https://huggingface.co/datasets/Henrychur/MMedC), a multilingual medical corpus containing 25.5B tokens. |
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[2023.2.20] We release [MMedBench](https://huggingface.co/datasets/Henrychur/MMedBench), a new multilingual medical multi-choice question-answering |
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benchmark with rationale. Check out the leaderboard [here](https://henrychur.github.io/MultilingualMedQA/). |
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## Evaluation on MMedBench |
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The further pretrained MMedLM 2 showcast it's great performance in medical domain across different language. |
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| Method | Size | Year | MMedC | MMedBench | English | Chinese | Japanese | French | Russian | Spanish | Avg. | |
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|------------------|------|---------|-----------|-----------|----------------|----------------|----------------|----------------|----------------|----------------|----------------| |
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| GPT-3.5 | - | 2022.12 | ✗ | ✗ | 56.88 | 52.29 | 34.63 | 32.48 | 66.36 | 66.06 | 51.47 | |
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| GPT-4 | - | 2023.3 | ✗ | ✗ | 78.00 | 75.07 | 72.91 | 56.59 | 83.62 | 85.67 | 74.27 | |
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| Gemini-1.0 pro | - | 2024.1 | ✗ | ✗ | 53.73 | 60.19 | 44.22 | 29.90 | 73.44 | 69.69 | 55.20 | |
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| BLOOMZ | 7B | 2023.5 | ✗ | trainset | 43.28 | 58.06 | 32.66 | 26.37 | 62.89 | 47.34 | 45.10 | |
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| InternLM | 7B | 2023.7 | ✗ | trainset | 44.07 | 64.62 | 37.19 | 24.92 | 58.20 | 44.97 | 45.67 | |
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| Llama 2 | 7B | 2023.7 | ✗ | trainset | 43.36 | 50.29 | 25.13 | 20.90 | 66.80 | 47.10 | 42.26 | |
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| MedAlpaca | 7B | 2023.3 | ✗ | trainset | 46.74 | 44.80 | 29.64 | 21.06 | 59.38 | 45.00 | 41.11 | |
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| ChatDoctor | 7B | 2023.4 | ✗ | trainset | 43.52 | 43.26 | 25.63 | 18.81 | 62.50 | 43.44 | 39.53 | |
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| PMC-LLaMA | 7B | 2023.4 | ✗ | trainset | 47.53 | 42.44 | 24.12 | 20.74 | 62.11 | 43.29 | 40.04 | |
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| Mistral | 7B | 2023.10 | ✗ | trainset | 61.74 | 71.10 | 44.72 | 48.71 | 74.22 | 63.86 | 60.73 | |
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| InternLM 2 | 1.8B | 2024.2 | ✗ | trainset |38.49 |64.1 |32.16|18.01|53.91|36.83|40.58| |
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| InternLM 2 | 7B | 2024.2 | ✗ | trainset | 57.27 | 77.55 | 47.74 | 41.00 | 68.36 | 59.59 | 58.59 | |
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| MMedLM (Ours) | 7B | - | ✓ | trainset | 49.88 | 70.49 | 46.23 | 36.66 | 72.27 | 54.52 | 55.01 | |
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| MMedLM 2(Ours) | 7B | - | ✓ | trainset | 61.74 | 80.01 | 61.81 | 52.09 | 80.47 | 67.65 | 67.30 | |
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| MMedLM 2(Ours) | 1.8B | - | ✓ | trainset | 45.40 | 66.78 | 42.21 | 25.56 | 69.14 | 43.40 | 48.75 | |
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- GPT and Gemini is evluated under zero-shot setting through API |
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- Open-source models first undergo training on the trainset of MMedBench before evaluate. |
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## Contact |
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If you have any question, please feel free to contact qiupengcheng@pjlab.org.cn. |
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## Citation |
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``` |
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@misc{qiu2024building, |
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title={Towards Building Multilingual Language Model for Medicine}, |
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author={Pengcheng Qiu and Chaoyi Wu and Xiaoman Zhang and Weixiong Lin and Haicheng Wang and Ya Zhang and Yanfeng Wang and Weidi Xie}, |
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year={2024}, |
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eprint={2402.13963}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
