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