In this session, Harpreet from Deci AI talked about the nuances of supervised fine-tuning, instruction tuning, and the powerful techniques that bridge the gap between model objectives and user-specific requirements.

Topics that were covered:

✅ Specialized Fine-Tuning: Adapt LLMs for niche tasks using labeled data.

✅ Introduction to Instruction Tuning: Enhance LLM capabilities and controllability.

✅ BitsAndBytes & Model Quantization: Optimize memory and speed with the BitsAndBytes library.

✅ PEFT & LoRA: Understand the benefits of the PEFT library from HuggingFace and the role of LoRA in fine-tuning.

✅ TRL Library Overview: Delve into the TRL (Transformers Reinforcement Learning) library’s functionalities.