metadata
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
- 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
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.
- 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)