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README.md

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+
+---
+
+license: llama3
+datasets:
+- Henrychur/MMedC
+language:
+- en
+- zh
+- ja
+- fr
+- ru
+- es
+tags:
+- medical
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/MMed-Llama-3-8B-GGUF
+This is quantized version of [Henrychur/MMed-Llama-3-8B](https://huggingface.co/Henrychur/MMed-Llama-3-8B) created using llama.cpp
+
+# Original Model Card
+
+# 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 MMed-Llama 3, a multilingual medical foundation model with 8 billion parameters. MMed-Llama 3 builds upon the foundation of Llama 3 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: 8192
+- Learning rate: 2e-5
+  
+The model can be loaded as follows:
+```py
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("Henrychur/MMed-Llama-3-8B")
+model = AutoModelForCausalLM.from_pretrained("Henrychur/MMed-Llama-3-8B", torch_dtype=torch.float16)
+```
+
+-  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 |
+|MMed-Llama 3(Ours)|8B |-      | ✓ | trainset  |  66.06| 79.25 | 61.81 | 55.63 | 75.39 | 68.38 | 67.75 |
+- 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 [email protected].
+
+## 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}
+}
+```