LLM Learning Portal

Fine-tuning: Introduction

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What is Fine-tuning?

Fine-tuning is the process of further training a pre-trained model on a specific dataset to adapt it for particular tasks or behaviors.

Why Fine-tune?

  • Adapt a general-purpose model to specialized domains
  • Improve performance on specific tasks
  • Align model outputs with desired formats or styles
  • Reduce harmful or undesired behaviors
  • Incorporate new knowledge not present during pre-training

Fine-tuning vs. Pre-training

Aspect Pre-training Fine-tuning
Data volume Massive (TB+) Limited (MB-GB)
Compute required Enormous (months) Moderate (hours-days)
Cost $1-100M+ $10-10,000
Learning rate Higher Lower (to avoid catastrophic forgetting)
Objective General capabilities Specific behaviors/knowledge
Fine-tuning leverages transfer learning: knowledge learned during pre-training is transferred and adapted to new tasks.

Types of Fine-tuning

Supervised Fine-tuning (SFT)

Training on examples of desired inputs and outputs, typically created by human experts

Example: Providing pairs of prompts and high-quality responses

Reinforcement Learning from Human Feedback (RLHF)

Using human preferences to train a reward model that guides model optimization

Example: Comparing two responses and training the model to prefer the better one

Instruction Fine-tuning

Teaching models to follow natural language instructions in a consistent format

Example: Converting diverse NLP tasks into instruction-following format

Domain Adaptation

Tailoring a model to specific fields like medicine, law, or technical domains

Example: Training on medical literature to improve healthcare applications

Parameter-Efficient Fine-tuning (PEFT)

Methods that update only a small subset of parameters, saving compute and memory

Example: LoRA, adapters, prompt tuning, and prefix tuning

The Fine-tuning Process

Pre-trained Model
Task-specific Data
Fine-tuning Method
Hyperparameters
Fine-tuning Process
Specialized Model

Key Steps in Fine-tuning

  1. Data Preparation

    Create high-quality, task-specific training examples

  2. Method Selection

    Choose appropriate fine-tuning approach based on resources and goals

  3. Hyperparameter Tuning

    Set learning rate, batch size, epochs, etc. to optimize training

  4. Training

    Update model weights on the training dataset

  5. Evaluation

    Test performance on validation data and adjust as needed

  6. Deployment

    Implement the fine-tuned model in your application

Careful fine-tuning data selection is critical: garbage in = garbage out!

Fine-tuning Outcomes

Improved Task Performance

Can achieve SOTA results on specific tasks

Potential Knowledge Loss

May forget general capabilities if not careful

Unique Model Behavior

Tailored to your specific requirements