YuanheZ/LoRA-One
LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently (ICML2025 Oral)
This project helps machine learning engineers and researchers to fine-tune large language models (LLMs) more efficiently. It takes a pre-trained LLM and a specific dataset, then applies a theoretically-grounded method to quickly adapt the model for new tasks like natural language understanding, mathematical reasoning, or code generation. The output is a fine-tuned LLM that performs better on specialized tasks with significantly less computational effort.
Use this if you are a machine learning practitioner looking for a provably efficient way to adapt large language models to new, specific tasks without extensive full-gradient computations.
Not ideal if you need a solution for models other than large language models or are not familiar with the concepts of fine-tuning and low-rank adaptation.
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Python
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Last pushed
Oct 22, 2025
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