Fine-Tuning LLMs
Goals: Equip students with knowledge and practical skills in finetuning techniques accompanied by code examples. Discuss the approach to finetuning utilizing CPUs.
The section centers around fine-tuning LLMs, addressing their various aspects and methodologies. As the module progresses, the focus will be given to specialized instruction tuning techniques, namely SFT and and LoRA. It will examine domain-specific applications, ensuring a holistic understanding of fine-tuning techniques and their real-world implications.
- Techniques for Finetuning LLMs: The lesson highlights the challenges, particularly the resource intensity of traditional approaches. We will introduce instruction tuning methods like SFT, RLHF, and LoRA.
- Deep Dive into LoRA and SFT: This lesson offers an in-depth exploration of LoRA and SFT techniques. We will uncover the mechanics and underlying principles of these methods.
- Finetuning using LoRA and SFT: This lesson guides a practical application of LoRA and SFT to finetune an LLM to follow instructions, using data from the “LIMA: Less Is More for Alignment” paper.
- Finetuning using SFT for financial sentiment. This lesson navigates the nuances of leveraging SFT to optimize LLMs, specifically tailored to capture and interpret sentiments within the financial domain.
- Fine-Tuning using Cohere for Medical Data. In this lesson, we will adopt an entirely different method for fine-tuning a large language model, leveraging a service called Cohere. This lesson explores the procedure of fine-tuning a customized generative model using medical texts to extract information. The task, known as Named Entity Recognition (NER), empowers the models to identify various entities (such as names, locations, dates, etc.) within a text.
The lessons have equipped students with both the knowledge and the practical skills needed for fine-tuning LLMs effectively. As they advance, they carry with them the capability to deploy and optimize LLM for various domains.