Workshop: Master Fine-Tuning LLMs: Build Your Own Domain Model with GPT-2

Minimum 2 years of experience. Freshers and college students will not be permitted, so please excuse this event.

Lunch is on your own, will not be provided

## Workshop Overview

Ever wanted your own LLM — but without building one from scratch?

What if you could have a model that speaks your domain’s language — whether that’s finance, healthcare, legal, or even your own project data?
In this hands-on workshop, you’ll learn how to take a base model (like GPT-2) and fine-tune it into your own domain-specialized LLM. Forget generic answers — this is about crafting a model that works for you.

### What You’ll Learn

We’ll cover all three approaches to fine-tuning:

1. Full Fine-Tuning – Updating all weights of the model for maximum customization.

• When to use it (and when it’s overkill).
• GPU/compute requirements.
• Walkthrough: fine-tuning a GPT-like model on a custom dataset.

2. Parameter-Efficient Fine-Tuning (PEFT) – LoRA, QLoRA, adapters.

• Update only a small subset of weights (millions vs billions).
• Train on a laptop or low-cost GPU.
• Demo: LoRA fine-tuning for domain-specific chatbots.

3. Instruction Fine-Tuning & Alignment (SFT + RLHF) –

• Teach models to follow instructions, tone, and style.
• Use reward models and human feedback loops.
• Hands-on: building a mini RLHF pipeline.

### Who Should Attend

• ML Engineers & Data Scientists who want to go beyond prompt hacks.
• Developers exploring custom AI assistants for their company or product.
• Researchers curious about alignment & efficiency tricks.
• Startup founders wanting to differentiate their AI with domain-trained LLMs.

### Takeaways

• Hands-on code notebooks you can reuse.
• Understanding of trade-offs between the 3 approaches.
• A fine-tuned mini-LLM you can deploy.
• Confidence to choose the right method for your projects.
• ⚡ Seats are limited — this workshop is designed to be interactive and practical.

Sign up now and start bending LLMs to your will.

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## Prerequisites

• Basic Python knowledge.
• Laptop with Colab notebook access.
• Curiosity and enthusiasm (we'll teach you everything else from the basics!)