Arushp1/llama3-medquad-qlora

LLaMA-3 8B Instruct - MedQuad Medical QnA (QLoRA)

This model is a fine-tuned version of LLaMA-3 8B Instruct using QLoRA (4-bit quantization + LoRA adapters) on the MedQuad Medical QnA Dataset.
It is designed to answer medical domain questions across various categories like treatment, symptoms, causes, prevention, inheritance, etc.

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Model Details

Model Description

• Base model: meta-llama/Meta-Llama-3-8B-Instruct
• Fine-tuning method: QLoRA (4-bit quantization with LoRA adapters)
• Task: Medical Question Answering (Instruction-tuned style)
• Languages: English
• Framework: 🤗 Transformers, PEFT, TRL
• Quantization: 4-bit (nf4, bfloat16 compute)
• License: Llama 3 license

Developers

• Developed by: Arush Pettem
• Dataset: keivalya/MedQuad-MedicalQnADataset

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Model Sources

• Repository: [Your Hugging Face repo link]
• Paper: "MedQuAD: Medical Question Answering Dataset"
• Demo: (Optional if you make a Gradio Space)

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Uses

Direct Use

• Answering medical questions in categories such as treatment, symptoms, causes, prevention, outlook, etc.
• Educational and research purposes in healthcare QA systems.

Downstream Use

• Integration into healthcare chatbots.
• Fine-tuning on domain-specific sub-corpora (e.g., cardiology QnA).
• Evaluation for explainable AI in medical NLP.

Out-of-Scope Use

⚠️ This model is not a substitute for professional medical advice. It should not be used for clinical decision-making or diagnosis.

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Bias, Risks, and Limitations

• Bias: Model inherits potential biases from MedQuad and the LLaMA base model.
• Risks: Incorrect or incomplete medical answers may mislead users if used in real-world clinical contexts.
• Limitations: Trained on static QA pairs, so may not generalize to open-ended patient conversations.

Recommendations

• Use in controlled, educational, or research settings only.
• Always validate outputs with trusted medical sources.

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How to Get Started with the Model

'''https://huggingface.co/Arushp1/llama3-medquad-qlora'''

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model = AutoModelForCausalLM.from_pretrained("Arushp1/llama3-medquad-qlora")
tokenizer = AutoTokenizer.from_pretrained("Arushp1/llama3-medquad-qlora")

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

query = "What are the symptoms of asthma?"
print(pipe(query, max_new_tokens=100))