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BiMediX
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BiMediX-Bi

Text Generation
Transformers
PyTorch
English
Arabic
mixtral
medical
conversational
Inference Endpoints
text-generation-inference
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BiMediX-Bi / README.md
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metadata
language:
  - en
  - ar
metrics:
  - accuracy
pipeline_tag: text-generation
tags:
  - medical
license: cc-by-nc-sa-4.0

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.
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

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)