At a Glance

Name: Instruction-based fine-tuning LLMs
Price: Free CAD
Rating: 4.0 (2 reviews)
Author: faranak_heidari, wojciech_fulmyk, joseph_santarcangelo, fateme_akbari

Learn to fine-tune large language models (LLMs) using instruction-based methods to improve their ability to follow commands and generate precise responses. This project covers creating templates for tasks like Q&A, summarization, code generation, and dialogue. By combining instructions and context, you'll train models for diverse applications. Gain experience with Hugging Face tools, apply Low-Rank Adaptation (LoRA), and use SFTTrainer for efficient, supervised fine-tuning. Perfect for data scientists building adaptable task-specific LLMs.

Imagine you’re developing a language model but struggling to get it to consistently follow instructions or produce the desired output. How can you teach the model to better understand and execute tasks across different scenarios? This project focuses on instruction-tuning for large language models (LLMs), a crucial method that trains models to respond accurately to specific instructions by leveraging structured data sets. By the end of this lab, you’ll learn how to create instruction-based data sets, apply various templates, and fine-tune models for improved performance. Whether you’re generating code, summarizing text, or building conversational agents, this lab equips you with essential tools to enhance LLM capabilities through effective instruction tuning.

Overview: What You'll Learn

In this hands-on project, you’ll explore instruction-tuning—a process that aligns language models with specific commands by structuring input-output examples. You’ll explore different template types that format instructions and responses, making models more responsive to task-specific requests.

Throughout the lab, you’ll leverage Hugging Face tools to fine-tune models, using Low-Rank Adaptation (LoRA) for efficient training. By structuring datasets into instruction-output pairs, you’ll gain practical experience in preparing models to handle diverse real-world applications.

Key Takeaways and Skills You'll Gain

By the end of this lab, you will:

Understand various instruction templates (Q&A, summarization, code generation, etc.) and how they enhance LLMs
Format data sets to align with instruction-response structures, preparing them for fine-tuning
Apply LoRA techniques to perform efficient model tuning without excessive computational cost
Utilize SFTTrainer to execute supervised fine-tuning for instruction-following tasks
Build a model capable of responding to diverse instructions, improving accuracy and output relevance in applications like chatbots, content creation tools, and AI assistants

These skills open doors to customizing LLMs for specific industries, enabling automation, enhanced productivity, and improved user interactions across various sectors.

What You’ll Need to Get Started

Basic Python programming knowledge
Familiarity with Hugging Face’s Transformers library
Web Browser: Use Chrome, Edge, Firefox, or Safari for development and testing.

Ready to Get Started?

Jump in and begin fine-tuning your own LLMs with structured templates. In just a few steps, you’ll transform your models to handle tasks with precision and adaptability!

Offered By: IBMSkillsNetwork

Instruction-based fine-tuning LLMs

At a Glance

Overview: What You'll Learn

Key Takeaways and Skills You'll Gain

What You’ll Need to Get Started

Ready to Get Started?