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--- |
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license: cc-by-nc-sa-4.0 |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: text-generation |
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tags: |
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- medical |
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--- |
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## Model Card for BiMediX-Bilingual |
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### Model Details |
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- **Name:** BiMediX |
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- **Version:** 1.0 |
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- **Type:** Bilingual Medical Mixture of Experts Large Language Model (LLM) |
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- **Languages:** English |
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- **Model Architecture:** [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) |
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- **Training Data:** BiMed1.3M-English, a bilingual dataset with diverse medical interactions. |
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### Intended Use |
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- **Primary Use:** Medical interactions in both English and Arabic. |
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- **Capabilities:** MCQA, closed QA and chats. |
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## Getting Started |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_id = "BiMediX/BiMediX-Eng" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id) |
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text = "Hello BiMediX! I've been experiencing increased tiredness in the past week." |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=500) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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### Training Procedure |
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- **Dataset:** BiMed1.3M-English, million healthcare specialized tokens. |
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- **QLoRA Adaptation:** Implements a low-rank adaptation technique, incorporating learnable low-rank adapter weights into the experts and the routing network. This results in training about 4% of the original parameters. |
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- **Training Resources:** The model underwent training on approximately 288 million tokens from the BiMed1.3M-English corpus. |
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### Model Performance |
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- **Benchmarks:** Demonstrates superior performance compared to baseline models in medical benchmarks. This enhancement is attributed to advanced training techniques and a comprehensive dataset, ensuring the model's adeptness in handling complex medical queries and providing accurate information in the healthcare domain. |
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| **Model** | **CKG** | **CBio** | **CMed** | **MedGen** | **ProMed** | **Ana** | **MedMCQA** | **MedQA** | **PubmedQA** | **AVG** | |
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|-----------------------|------------|-----------|-----------|-------------|-------------|---------|-------------|-----------|--------------|---------| |
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| PMC-LLaMA-13B | 63.0 | 59.7 | 52.6 | 70.0 | 64.3 | 61.5 | 50.5 | 47.2 | 75.6 | 60.5 | |
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| Med42-70B | 75.9 | 84.0 | 69.9 | 83.0 | 78.7 | 64.4 | 61.9 | 61.3 | 77.2 | 72.9 | |
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| Clinical Camel-70B | 69.8 | 79.2 | 67.0 | 69.0 | 71.3 | 62.2 | 47.0 | 53.4 | 74.3 | 65.9 | |
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| Meditron-70B | 72.3 | 82.5 | 62.8 | 77.8 | 77.9 | 62.7 | **65.1** | 60.7 | 80.0 | 71.3 | |
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| **BiMediX** | **78.9** | **86.1** | **68.2** | **85.0** | **80.5** | **74.1**| 62.7 | **62.8** | **80.2** | **75.4** | |
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### Safety and Ethical Considerations |
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- **Potential issues**: hallucinations, toxicity, stereotypes. |
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- **Usage:** Research purposes only. |
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### Accessibility |
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- **Availability:** [BiMediX GitHub Repository](https://github.com/mbzuai-oryx/BiMediX). |
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- arxiv.org/abs/2402.13253 |
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### Authors |
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Sara Pieri, Sahal Shaji Mullappilly, Fahad Shahbaz Khan, Rao Muhammad Anwer Salman Khan, Timothy Baldwin, Hisham Cholakkal |
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**Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)** |
