transformers safetensors mixtral feature-extraction medical text-generation conversational ar arxiv:2402.13253 license:cc-by-nc-sa-4.0 text-generation-inference endpoints_compatible region:us
---
language:
- 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:** Arabic
- **Model Architecture:** [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
- **Training Data:** BiMed1.3M-Arabic, an arabic 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-Ara"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

text = "مرحبًا بيميديكس! لقد كنت أعاني من التعب المتزايد في الأسبوع الماضي."
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-Arabic.
- **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 the Arabic corpus.

### Model Performance

| **Model**                         | **CKG** | **CBio** | **CMed** | **MedGen** | **ProMed** | **Ana** | **MedMCQA** | **MedQA** | **PubmedQA** | **AVG** |
|-----------|------------|-----------|-----------|-------------|-------------|---------|-------------|-----------|--------------|---------|
| Jais-30B  | 52.1       | 50.7      | 40.5      | 49.0        | 39.3        | 43.0    | 37.0        | 28.8      | 74.6         | 46.1    |
| BiMediX (Arabic) | 60.0 | 54.9 | **55.5** | 58.0 | **58.1** | 49.6 | 46.0 | 40.2 | 76.6 | 55.4 |
| **BiMediX (Bilingual)** | **63.8** | **57.6** | 52.6 | **64.0** | 52.9 | **50.4** | **49.1** | **47.3** | **78.4** | **56.5** |

### 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)**