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Fine-Tuning LLMs: A Practical Guide

Master adaptive AI by tailoring large language models to your specific tasks and data.

4.7
(1,250)
1,250 studentsEnglish

Created by Rebecca Hollis · AI Strategy Advisor, ex-McKinsey

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$699$1,049Save 33%
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This course includes

  • 15 video lessons
  • 3h 42m of content
  • Downloadable resources
  • Full lifetime access
  • Certificate of completion
  • 30-day money-back guarantee

What you'll learn

  • Select appropriate base LLMs for fine-tuning.
  • Prepare and preprocess custom datasets for training.
  • Implement efficient fine-tuning techniques like LoRA.
  • Configure training parameters for optimal results.
  • Evaluate fine-tuned model performance quantitatively and qualitatively.
  • Understand and mitigate common fine-tuning pitfalls.
  • Deploy fine-tuned models for specific applications.
  • Interpret model behaviour changes post-tuning.

About this course

Stop wrestling with generic LLMs. This course dives deep into the practicalities of fine-tuning, moving beyond theory to hands-on implementation. We'll cover everything from selecting the right base model and preparing your dataset to executing the training process and evaluating performance. You'll learn to imbue models with specialized knowledge and stylistic nuances, making them truly your own. Forget the black box. We demystify the process, showing you how to leverage techniques like LoRA and QLoRA for efficient adaptation without astronomical compute costs. You'll gain the confidence to iterate on your models, troubleshoot common issues, and deploy them effectively for real-world applications, whether it's for nuanced customer support or domain-specific content generation. This isn't about building an LLM from scratch; it's about intelligently adapting existing powerful models. We focus on the practical skills and decision-making required to get the best results, saving you time and resources. By the end, you'll have a robust understanding of how to make LLMs work precisely how you need them to.

Course content

5 modules · 15 lessons · 3h 42m

  • When Generic LLMs Fall ShortPreview
    15m
  • Core Concepts: Transfer Learning & AdaptationPreview
    18m
  • Choosing Your Base Model
    20m

Requirements

  • Solid understanding of Python programming.
  • Familiarity with deep learning concepts and frameworks (PyTorch/TensorFlow).
  • Experience with transformers library or similar.

Your instructor

R

Rebecca Hollis

AI Strategy Advisor, ex-McKinsey

Rebecca advises boards on where AI creates real value — and where it quietly burns money.