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Fine-Tuning Open Weight Large Language Models

Published by O'Reilly Media, Inc.

Intermediate to advanced content levelIntermediate to advanced

Get the most out of BERT, Llama, and Mistral and tailor them to your needs

Course outcomes

  • Learn how to work with LLMs
  • Discover your appropriate base LLM
  • Understand how to fine-tune models

Course description

The release of Meta's Llama model has proven to be the big bang of open source large language models, and a lot of work has been invested by the ML community to fine-tune the model to specific needs for tasks such as question answering or for chatbots.

Join expert Christian Winkler to get a structured and accessible introduction to fine-tuning open source LLMs. You’ll fine-tune your own model and adapt the base model to your functional needs (like question answering or domain-specific vocabulary) with code presented in the course along with a GitHub repo. You’ll discover how these models can also excel on less powerful hardware by using new approaches to quantization.

What you’ll learn and how you can apply it

  • Understand the different types of LLMs and their limitations
  • Get an insight into transfer learning and how it relates to fine-tuning
  • Understand the challenges related to fine-tuning
  • Perform fine-tuning for the different types of LLMs
  • Know about possible hardware scenarios

This live event is for you because...

  • You’re a data scientist, ML engineer, or NLP developer.
  • You want to adapt LLMs to your specific needs.
  • ChatGPT is not enough for you.

Prerequisites

  • Working knowledge of Python and Jupyter Notebooks
  • Basic understanding of machine learning
  • Familiarity with Hugging Face Transformers (helpful but not required)

Recommended preparation:

  • Set up a Google Colab account (for local installations a powerful GPU is necessary)

Recommended follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Short introduction to LLMs (30 minutes)

  • Group discussion: Which open-source LLMs are you using?
  • Presentation: BERT-like models (classification, inference, embeddings); GPT-like models (text generation); transfer learning (basis for fine-tuning)
  • Q&A

Fine-tuning BERT models (60 minutes)

  • Group discussion: Who has fine-tuned models? How long does it take to fine-tune models?
  • Presentation: Classification; embeddings
  • Hands-on exercises: Fine-tune a BERT model; fine-tune an embeddings model with SBERT
  • Q&A

Fine-tuning GPT models (120 minutes)

  • Presentation: LoRA and PEFT; fine-tuning with Hugging Face transformers; fine-tuning with Axolotl and Unsloth frameworks
  • Hands-on exercises: Fine-tune using the Hugging Face software; use Axolotl and Unsloth frameworks
  • Q&A

Hardware (30 minutes)

  • Group discussion: Where do you want to run your models?; Which hardware is most suitable for your usage scenario?
  • Presentation: Running on CPUs; running on GPUs (local or cloud); running on Apple hardware
  • Q&A

Your Instructor

Christian Winkler

Christian Winkler has worked with large amounts of data for several decades, and specializes in advanced analytics. As a professor at the Technical University of Applied Science in Nuremberg, he concentrates his research on optimizing user experience using modern data-based approaches. He researches and publishes articles about data management and large language models and is a regular contributor to machine learning conferences.

Skills covered

  • Large Language Models (LLMs)
  • Prompt Engineering