mirzayasirabdullahbaig07/Fine-Tuning-LLaMA-3.2-3B-Using-PEFT-LoRA
This project showcases parameter-efficient fine-tuning of the LLaMA 3.2 (3B) language model using PEFT (Parameter-Efficient Fine-Tuning) and LoRA (Low-Rank Adaptation). It is optimized for minimal resource usage and trained on a domain-specific dataset to enhance performance in specialized tasks.
This project helps AI developers adapt existing large language models to excel in specialized fields like healthcare. By taking an open-source language model (LLaMA 3.2 3B) and a domain-specific dataset (Medical Chain-of-Thought), it produces a fine-tuned model capable of generating more accurate and structured responses for medical reasoning tasks. An AI/ML engineer or data scientist working with language models in a specific industry would use this to improve model performance.
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Use this if you are an AI/ML engineer or data scientist needing to customize a large language model for better performance on structured, domain-specific text generation without extensive computational resources.
Not ideal if you are looking for a ready-to-use medical AI assistant or if you don't have experience with fine-tuning language models.
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Jun 01, 2025
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