transformers pytorch mixtral text-generation medical conversational en ar arxiv:2402.13253 license:cc-by-nc-sa-4.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
language:
- en
- ar
license: cc-by-nc-sa-4.0
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, Arabic
- **Model Architecture:** [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
- **Training Data:** BiMed1.3M, 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-Bi"
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, 632 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 632 million tokens from the Arabic-English corpus, including 288 million tokens exclusively for English.

### Model Performance
- **Benchmarks:** Outperforms the baseline model and Jais-30B in medical evaluations.

| **Model**                         | **CKG** | **CBio** | **CMed** | **MedGen** | **ProMed** | **Ana** | **MedMCQA** | **MedQA** | **PubmedQA** | **AVG** |
|-----------------------------------|------------|-----------|-----------|-------------|-------------|---------|-------------|-----------|--------------|---------|
| Jais-30B | 57.4       | 55.2      | 46.2      | 55.0        | 46.0        | 48.9    | 40.2        | 31.0      | 75.5         | 50.6    |
| Mixtral-8x7B| 59.1       | 57.6      | 52.6      | 59.5        | 53.3        | 54.4    | 43.2        | 40.6      | 74.7         | 55.0    |
| **BiMediX (Bilingual)**           | **70.6**   | **72.2**  | **59.3**  | **74.0**    | **64.2**    | **59.6**| **55.8**    | **54.0**  | **78.6**     | **65.4**|

### 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)**