Deep Learning Lab
Guide
Module 8 · 7 lessons · 3.5 hours in class

Efficient Fine-Tuning and Data Engineering

How can we adapt pretrained models efficiently and evaluate whether adaptation is worthwhile?

You will learn

  • Adaptation strategies
  • Data quality & splits
  • Baseline ladder
  • Full fine-tuning
  • PEFT & LoRA
  • SFT & QLoRA
  • Evaluation & decision records

Hands-on: Compare pretrained inference, frozen features, full fine-tuning and LoRA on a shared IMDb protocol.

Learning route: Lessons 8.1–8.5 and 8.7 are classroom material. Lesson 8.6 extends the workflow to LLM instruction tuning and QLoRA; section 7 of 8.5 is an extension on other PEFT methods.

Lessons

  1. Lesson 8.1: Understanding Model Adaptation

    How can we adapt pretrained models to specific tasks without updating every parameter, and how do we prepare data for reliable adaptation?

    Classroom · ~31 min self-study · 9 sections · 11 code cells
    Not started Start

  2. Lesson 8.2: Preparing Data for Fine-Tuning

    How do we turn raw data into a dataset that adaptation can learn from reliably, and that gives honest evaluation numbers?

    Classroom · ~44 min self-study · 13 sections · 18 code cells · 1 lab
    Not started Start

  3. Lesson 8.3: Establishing Baselines

    What must an adapted model beat to be worth its cost, and how do we measure that fairly?

    Classroom · ~25 min self-study · 8 sections · 12 code cells
    Not started Start

  4. Lesson 8.4: The Fine-Tuning Workflow

    What exactly happens when we fine-tune a pretrained model, and how do we do it reliably?

    Classroom · ~40 min self-study · 12 sections · 20 code cells · 1 lab
    Not started Start

  5. Lesson 8.5: Parameter-Efficient Fine-Tuning (PEFT)

    Can we adapt a model just as well while training only a tiny fraction of its parameters?

    Classroom · ~43 min self-study · 12 sections · 19 code cells · 1 lab
    Not started Start

  6. Lesson 8.6: Fine-Tuning Large Language Models (Conceptual)

    How are large language models adapted to follow instructions, and what makes that practical on real hardware?

    Further learning · ~37 min self-study · 11 sections · 14 code cells · 1 lab
    Not started Start

  7. Lesson 8.7: Evaluating Adapted Models

    How do we know whether adaptation worked, and how do we decide whether to keep an adapted model?

    Classroom · ~37 min self-study · 12 sections · 18 code cells
    Not started Start

Module challenge

Earns the Module 8 badge

Module 8 challenge: Efficient Fine-Tuning and Data Engineering

8 scenario questions that mix ideas from all 7 lessons. One question at a time, an explanation after each, and your score at the end. Pass mark 75%, so 6 of 8.

not attempted

Best after you have practised all 7 lessons.