Explore instruction fine-tuning and how to address catastrophic forgetting in large language models (LLMs). Learn how to enhance precision tasks with instruction fine-tuning, even with smaller models and resource constraints.
Discover strategies like task-specific multitask fine-tuning and parameter-efficient fine-tuning (PEFT) to tackle catastrophic forgetting. Focus on PEFT's memory efficiency and its impact on LLMs.
Learn about LoRA (Low-rank Adaptation) and QLoRA (Quantized Low-rank Adaptation), parameter-efficient fine-tuning techniques. Understand their benefits and differences.
Get hands-on experience with QLoRA implementation using Transformers and Bits & Bytes libraries. Includes model selection, training, saving, and sharing on the Hugging Face Hub. Instructions for model loading and text generation tasks are provided.
For a deeper dive into cutting-edge technology and to access all the technical knowledge, read our Medium Blog.
Colab walkthrough -
For a detailed understanding of PEFT,lora-Qlora technologies check out our blog post. It explains our approach in a clear and thorough manner.