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.
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.