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