Hong-Lab-UMN-ECE/RoSTE
[ICML 2025] Official code for the paper "RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models"
This project helps machine learning engineers and researchers fine-tune large language models (LLMs) more efficiently while maintaining high performance. You input a pre-trained LLM and a dataset for a specific task, like summarization, and it outputs a more compact, fine-tuned LLM ready for deployment. This is ideal for those working on deploying LLMs in resource-constrained environments.
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Use this if you need to fine-tune a large language model for a specific task and want to reduce its memory footprint and computational cost without significant performance loss.
Not ideal if you are looking for a general-purpose LLM fine-tuning library without specific concerns about quantization or efficiency.
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Last pushed
May 29, 2025
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