“Large Language Models (LLMs) have revolutionized the AI industry, providing unprecedented precision, expanding the possibilities of artificial intelligence. However the pre-trained LLMs may not always meet the specific requirements of an organization, hence there is always a need to Fine tune the LLMs to tailor these models to your unique tasks and requirements.”

This comprehensive course is designed to equip you with the skills of LLM Fine tuning technique. It starts with a thorough introduction to the fundamentals of fine tuning while highlighting its critical role to make the LLM models adapt to your specific data. Then we will dive into Hands-on sessions covering the entire Fine-tuning workflow to Fine tune OpenAI GPT model. Through practical sessions, you’ll step-by-step learn to prepare & format datasets, execute OpenAI fine tuning processes, and evaluate the model outcomes.

By the end of this course, you will be proficient in Fine tuning the OpenAI’s GPT model to meet specific organizational needs and start your career journey as LLM Fine tuning engineer.

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What in nutshell is included in the course ?

[Theory]

• We’ll start with LLM and LLM Fine tuning’s core basics and fundamentals.

• Discuss Why is Fine tuning needed, How it works, Workflow of Fine tuning and the Steps involved in it.

• Different types of LLM Fine tuning techniques including RLHF, PEFT, LoRA, QLoRA, Standard, Sequential and Instructions based LLM Fine tuning.

• Best practices for LLM Fine tuning.

[Practicals]

• Get a detailed walkthrough of OpenAI Dashboard and Playground to get a holistic understanding of wide range of tools that OpenAI offers for Generative AI.

• Follow the OpenAI Fine tuning workflow in practical sessions covering Exploratory Data Analysis (EDA), Data preprocessing, Data formatting, Creating fine tuning job, Evaluation.

• Understand the OpenAI specialized JSONL format that it accepts for training & test data, and learn about 3 important roles – System, User, Assistant.

• Calculate the Token count and Fine tuning cost in advance using Tiktoken library.

• Gain Hands-on experience in fine tuning OpenAI’s GPT model on a custom dataset using Python through step-by-step practical sessions.

• Assess the accuracy and performance of the fine-tuned model compared to the base pre-trained model to evaluate the impact of fine-tuning.

Who this course is for:

• 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.