pittisl/GreenTrainer
Code for paper "Towards Green AI in Fine-tuning Large Language Models via Adaptive Backpropagation" (ICLR'24)
This project helps machine learning researchers and engineers fine-tune large language models (LLMs) more efficiently. It takes a pre-trained LLM and a dataset for fine-tuning, then outputs a fine-tuned model that maintains accuracy while significantly reducing computational costs. It is ideal for those working on deploying LLMs in resource-constrained environments.
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Use this if you are a machine learning researcher or MLOps engineer looking to reduce the computational footprint and energy consumption of fine-tuning large language models without sacrificing model performance.
Not ideal if you are an end-user without a technical background in machine learning and deep learning, as this tool requires familiarity with LLM fine-tuning concepts and environments.
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Language
Python
License
MIT
Category
Last pushed
Nov 17, 2023
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