LLM Fine Tuning Fundamentals + Fine tune OpenAI GPT model

Learn the fundamentals of LLM and LLM Fine Tuning. Hands-On sessions to Fine tune OpenAI GPT model with custom data.

Created by: J Garg - Real Time Learning
Data Engineering, Analytics and Cloud Trainer

What Will I Learn?

• Explore the fundamentals of LLM and LLM Fine tuning including - Why Fine tuning is needed, How it works, Applications, etc.
• Learn the complete workflow, architecture, essential steps involved for Fine tuning Large language models (LLMs).
• Different types of LLM Fine tuning techniques including RLHF, PEFT, LoRA, QLoRA, Standard, Sequential and Instructions based LLM Fine tuning.
• Get a comprehensive walkthrough of OpenAI Dashboard and Playground to get a holistic understanding of wide range of tools that OpenAI offers for Generative AI.
• Prepare the dataset as per OpenAI accepted JSONL format for Fine-tuning GPT models, while also calculating the fine-tuning cost in advance.
• Learn step-by-step through Hands-on sessions on how to fine tune OpenAI's GPT model on custom dataset using Python.
• Evaluate and compare the fine tune model with the base pre-trained GPT model to to assess improvements in accuracy and performance.
• Discover Best practices for LLM fine-tuning.

Requirements

• Basic Python knowledge.

Target audience

• Data scientists who want to learn fundamentals of LLM Fine-tuning.
• OpenAI users who want to Fine tune OpenAI GPT models with custom data.
• Machine learning engineers who want to enter into LLM domain.
• Generative AI engineers.