Abhi0323/Fine-Tuning-LLaMA-2-with-QLORA-and-PEFT
This project enhances the LLaMA-2 model using Quantized Low-Rank Adaptation (QLoRA) and other parameter-efficient fine-tuning techniques to optimize its performance for specific NLP tasks. The improved model is demonstrated through a Streamlit application, showcasing its capabilities in real-time interactive settings.
This project helps AI engineers and machine learning researchers customize large language models for specific tasks without needing extensive computational resources. You start with a general LLaMA-2 model and a specialized dataset, and it produces a fine-tuned LLaMA-2 model, ready for deployment in applications. It's designed for those who want to adapt powerful language models to unique domains or applications.
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Use this if you need to adapt a LLaMA-2 model to understand or generate text for a very specific domain or task using limited computational resources.
Not ideal if you're looking for an off-the-shelf solution and don't have experience with machine learning model fine-tuning or deployment.
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Apr 18, 2024
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