XavierSpycy/hands-on-lora
Explore practical fine-tuning of LLMs with Hands-on Lora. Dive into examples that showcase efficient model adaptation across diverse tasks.
This project helps machine learning engineers adapt large language models (LLMs) for specific tasks without needing to retrain the entire model. It takes an existing LLM and a smaller dataset tailored to a new task, producing a more specialized version of the LLM that performs better on that particular task. This is for machine learning practitioners and researchers who need to efficiently customize LLMs for diverse applications.
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Use this if you are a machine learning engineer looking to fine-tune large language models for specific downstream tasks like text generation or named entity recognition, with limited computational resources.
Not ideal if you are a non-technical user seeking a ready-to-use application, as this project requires deep understanding of machine learning and model training.
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Apache-2.0
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Last pushed
Oct 24, 2024
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