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Awesome GitHub RepositoriesParameter-Efficient Tuning Techniques
Methods for optimizing large neural networks by training only a small subset of model weights to reduce resource requirements.
Distinguishing note: Specifically targets efficiency and hardware optimization, distinct from full-model fine-tuning.
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Awesome Parameter-Efficient Tuning Techniques GitHub Repositories
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- datawhalechina/self-llm
datawhalechina/self-llm
28,285View on GitHubThis project is an open-source educational resource providing structured, step-by-step guides for fine-tuning large language models. It focuses on adapting pre-trained transformer-based causal models to custom datasets, enabling users to transfer specific writing styles or domain knowledge into generative AI models. The repository distinguishes itself by emphasizing parameter-efficient training techniques, specifically low-rank adaptation. By providing practical implementations for updating only a small subset of model weights, it allows for the customization of massive neural networks on con
Optimizing the performance of massive neural networks on consumer-grade hardware by training only a small subset of model weights.
Jupyter Notebookchatglmchatglm3gemma-2b-it
