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Google DeepMind: 05 Fine-Tune Your Model

Google via Google Skills

Overview

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Unleash the power of language models with fine-tuning. In this course, you will learn how to adjust a pre-trained model to a specific task. You will start with full-parameter fine-tuning using a small language model. To tune larger models like Gemma, you will learn parameter-efficient techniques with a focus on LoRA. Finally, you will be briefly introduced to reinforcement learning as an alternative to supervised fine-tuning (SFT). You will also explore how AI is imagined and made sense of in cultural contexts. You will consider why responsible AI is not just about technical safety but also about building governance systems that reflect community values and protect the public interest.

Syllabus

  • Introduction to fine-tuning
    • The importance of specialization
    • How will your model respond?
    • Learning objectives
    • How to get the most out of this course
    • Lab: Prompt Your SLM with Questions
    • Gemma: Fine-tuning the model for specific tasks
  • Formatting
    • Formatting
    • Lab: Format Text for Turn-Based Dialogue
    • Input and output formats
    • Knowledge check 1
  • Full-parameter fine-tuning
    • The advantages of fine-tuning
    • Full-parameter fine-tuning
    • Lab: Fine-Tune All The Parameters of Your SLM
    • When to stop fine-tuning
    • What makes a "good" flashcard?
    • Imagining AI in cultural contexts
    • Write your own piece of AI fiction
    • Knowledge check 2
  • Parameter-efficient fine-tuning
    • Foundation models
    • Lab: Full-Parameter Fine-Tuning of Gemma
    • Low-rank adaptation (LoRA)
    • Lab: Implement LoRA for Parameter-Efficient Fine-Tuning
    • Lab: Fine-Tuning Gemma3-1B with LoRA
    • Knowledge check 3
  • Opportunities and limitations of SFT
    • Opportunities and limitations of SFT
    • Reinforcement learning from human feedback
    • Knowledge check 4
  • Challenge
    • Foresight and governance
    • Designing a governance blueprint for your LLM
    • Knowledge check 5
  • Continue your journey
    • Summary
    • Looking forward
    • Additional resources and further reading
    • Glossary
    • Feedback
  • Your Next Steps
    • Claim credential

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