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.
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)
Runs of BiMediX BiMediX-Eng on huggingface.co
124
Total runs
20
24-hour runs
39
3-day runs
54
7-day runs
83
30-day runs
More Information About BiMediX-Eng huggingface.co Model
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