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Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

🚀 How to Use This Model for Inference

This model is fine-tuned using LoRA (PEFT) on Phi-4 (4-bit Unsloth). To use it, you need to:

Load the base model
Load the LoRA adapter
Run inference

📌 Install Required Libraries

Before running the code, make sure you have the necessary dependencies installed:

pip install unsloth peft transformers torch


from unsloth import FastLanguageModel
from peft import PeftModel
import torch

# Load the base model
base_model_name = "unsloth/Phi-4-unsloth-bnb-4bit"
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=base_model_name,
    max_seq_length=4096,  # Must match fine-tuning
    load_in_4bit=True,
)

# Load the fine-tuned LoRA adapter
lora_model_name = "Machlovi/Phi4_MedQA_USMLE_4_Options"
model = PeftModel.from_pretrained(model, lora_model_name)

# Run inference
input_text = "What are the symptoms of a heart attack?"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=100)

# Decode and print response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

💡 Notes

This model is quantized in 4-bit for efficiency.
Ensure max_seq_length matches the training configuration.
This model requires a GPU (CUDA) for inference.

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Training Details

Training Data

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Training Procedure

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Training Hyperparameters

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Evaluation


medmcqa = """< |im_start| > system 
You are a medical doctor answering real-world medical entrance exam questions. 
Based on your understanding of basic and clinical science, medical knowledge, and mechanisms underlying health, disease, 
patient care, and modes of therapy, answer the following multiplechoice question. 
Select one correct answer from A to D. Base your answer on the current and standard practices referenced in medical guidelines. < |im_end| >

< |im_start| > 
question: {}
options:{}
< |im_end| > 

< |im_start| >
answer:{}

📝 Example Inference

< |im_start| > system 
You are a medical doctor answering real-world medical entrance exam questions. 
Based on your understanding of basic and clinical science, medical knowledge, and mechanisms underlying health, disease, 
patient care, and modes of therapy, answer the following multiple-choice question. 
Select one correct answer from A to D. Base your answer on the current and standard practices referenced in medical guidelines. 
< |im_end| >

< |im_start| > 
question: A junior orthopaedic surgery resident is completing a carpal tunnel repair with the department chairman as the attending physician. 
During the case, the resident inadvertently cuts a flexor tendon. The tendon is repaired without complication. 
The attending tells the resident that the patient will do fine, and there is no need to report this minor complication that will not harm the patient, 
as he does not want to make the patient worry unnecessarily. He tells the resident to leave this complication out of the operative report. 
Which of the following is the correct next action for the resident to take?

options:
A. Disclose the error to the patient and put it in the operative report  
B. Tell the attending that he cannot fail to disclose this mistake  
C. Report the physician to the ethics committee  
D. Refuse to dictate the operative report  

< |im_end| >

< |im_start| >
answer: B. Tell the attending that he cannot fail to disclose this mistake
< |im_end| >

Testing Data, Factors & Metrics

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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