
A coarse-to-fine framework, CoFiTune, is proposed to strike a balance between the speciality and versatility of large language models by selectively fine-tuning specific modules within a defined layer range.


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optimizing-the-balance-between-speciality-and-versatility-in-large-language-models-through-coarse-to-fine-tuning


Optimizing the Balance Between Speciality and Versatility in Large Language Models Through Coarse-to-Fine Tuning


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Supervised fine-tuning is an effective method to customize pre-trained language models like Llama 3.1 for specific use cases, improving performance and adding new capabilities at a lower cost compared to using closed-source models.


A beginner’s guide to state-of-the-art supervised fine-tuning

Fine-Tune Llama 3.1 Ultra-Efficiently with Unsloth

efficiently-fine-tune-llama-3-1-with-unsloth-for-customized-language-models


Efficiently Fine-Tune Llama 3.1 with Unsloth for Customized Language Models
