Fine-tuning LLMs with Hugging Face SFT 🚀 | QLoRA | LLMOps

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. He also demonstrated how to fine-tune LLMs using Hugging Face SFT.
Topics that were covered:
✅ Specialized Fine-Tuning: Adapt LLMs for niche tasks using labeled data.
✅ Introduction to Instruction Tuning: Enhance LLM capabilities and controllability.
✅ Dataset Preparation: Format datasets for effective instruction tuning.
✅ 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.
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[@avishvijayaraghavan]

Great tutorial, appreciate it guys!

[@pravingaikwad1337]

What is the loss function used?

[@coldedkiller1125]

can you please share colab notebook
also is there a way to put path of dataset and model locally (like path form my pc ) ?

[@ShaiHaaron]

colab.research.google.com/drive/1-xGUUad2O3Y3V0-qBxES7JPD4pKB4FI9?usp=sharing