Model Card for Model ID
This is a LoRA adapter-based fine-tuned version of DeepSeek-R1-Distill-Llama-8B, optimized for Medical Question Answering (MedQA) using PEFT, LoRA adapters, and bnb-4bit quantization. The fine-tuning was performed on a curated dataset of 10k examples containing medical questions and answers from trusted sources.
Model Details
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How to Get Started with the Model
Use the code below to get started with the model.
1from unsloth import FastLanguageModel
2
3# Load model + adapters directly
4model, tokenizer = FastLanguageModel.from_pretrained("PM234/DeepSeek-R1-MedExpert-LoRA-8B-bnb4bit")
5
6# Prep for inference
7FastLanguageModel.for_inference(model)
8
9# Example:
10test_input = "Below is an instruction...\n### Instruction: Answer the following medical question.\n### Input: What is the primary source of energy for the human body?\n### Response:"
11inputs = tokenizer(test_input, return_tensors="pt").to("cuda")
12outputs = model.generate(**inputs, max_new_tokens=20)
13print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # "Glucose"
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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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