This module will guide you through instruction tuning language models. Instruction tuning involves adapting pre-trained models to specific tasks by further training them on task-specific datasets. This process helps models improve their performance on targeted tasks.
In this module, we will explore two topics: 1) Chat Templates and 2) Supervised Fine-Tuning.
Chat templates structure interactions between users and AI models, ensuring consistent and contextually appropriate responses. They include components like system prompts and role-based messages. For more detailed information, refer to the Chat Templates section.
Supervised Fine-Tuning (SFT) is a critical process for adapting pre-trained language models to specific tasks. It involves training the model on a task-specific dataset with labeled examples. For a detailed guide on SFT, including key steps and best practices, see the Supervised Fine-Tuning page.
For hands-on practice, please refer to the exercises page.
SFTTrainer in TRL