DeepSeek-R1-MedExpert-LoRA-8B-bnb4bit

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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

Model Description

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Uses

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Direct Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

Use the code below to get started with the model.

from unsloth import FastLanguageModel

# Load model + adapters directly
model, tokenizer = FastLanguageModel.from_pretrained("PM234/DeepSeek-R1-MedExpert-LoRA-8B-bnb4bit")

# Prep for inference
FastLanguageModel.for_inference(model)

# Example:
test_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:"
inputs = tokenizer(test_input, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))  # "Glucose"

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Summary

Model Examination [optional]

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Environmental Impact

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

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Technical Specifications [optional]

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Citation [optional]

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Glossary [optional]

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Framework versions

• PEFT 0.14.0