BiMediX / BiMediX-Bi

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
• 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

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.

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)