Fine-Tuning Explained

Base models are generalists. Fine-tuning makes them specialists—optimized for your specific use case.

What Is Fine-Tuning?

Fine-tuning takes a pre-trained model and trains it further on your specific data.

Analogy: A medical school graduate (pre-trained) completes a residency (fine-tuning) to become a specialist.

The model keeps its general knowledge but learns to excel at particular tasks or domains.

Why Fine-Tune?

1. Specialized Performance

Make the model better at your specific task:

• Medical diagnosis
• Legal document review
• Code in your company's style
• Customer service for your products

2. Consistent Behavior

Train specific response patterns:

• Always use your company's tone
• Follow particular output formats
• Incorporate domain terminology

3. Efficiency

A smaller fine-tuned model can outperform a larger general model on specific tasks.

Fine-tuned 7B model > Base 70B model (for your task)

4. Privacy

If you fine-tune and host locally, your data never leaves your infrastructure.

Fine-Tuning vs. Prompt Engineering

Prompt Engineering:

• Customize via instructions in the prompt
• Quick and easy
• Uses context window tokens
• No training required

Fine-Tuning:

• Customize by training on examples
• Takes time and resources
• Instructions are "baked in"
• Requires compute for training

Rule of thumb: Try prompting first. Fine-tune when prompts aren't enough.

Types of Fine-Tuning

Full Fine-Tuning

Update all model parameters.

• Pros: Maximum customization
• Cons: Expensive, needs lots of data, risk of "catastrophic forgetting"

LoRA (Low-Rank Adaptation)

Train small adapter layers while keeping base model frozen.

• Pros: Cheap, fast, can stack multiple adapters
• Cons: Slightly less flexibility
• Common choice: Most practical for most users

QLoRA

LoRA on quantized models.

• Pros: Even cheaper, runs on consumer hardware
• Cons: Some quality loss from quantization

The Fine-Tuning Process

1. Prepare Your Data

Create training examples in conversation format:

{
  "messages": [
    {"role": "system", "content": "You are a helpful legal assistant."},
    {"role": "user", "content": "Is this contract enforceable?"},
    {"role": "assistant", "content": "Based on the terms..."}
  ]
}

You need:

• Hundreds to thousands of examples
• High-quality, representative samples
• Properly formatted data

2. Choose Your Approach

• OpenAI fine-tuning: Easiest, upload data, pay per token
• Local with LoRA: Use Hugging Face libraries
• Cloud platforms: Together, Replicate, etc.

3. Train

• Set hyperparameters (learning rate, epochs)
• Monitor training loss
• Watch for overfitting

4. Evaluate

• Test on held-out examples
• Compare to base model
• Check for regressions

When Fine-Tuning Makes Sense

Good candidates:

• Consistent style/tone requirements
• Domain-specific terminology
• Structured output formats
• Tasks with clear right answers

Poor candidates:

• General knowledge tasks
• Tasks requiring reasoning about new information
• When you have < 100 examples
• When prompt engineering works fine

Common Pitfalls

Overfitting

Model memorizes training data instead of learning patterns. Fix: Use more diverse data, fewer epochs.

Catastrophic Forgetting

Model loses general capabilities. Fix: Include diverse examples, use LoRA instead of full fine-tuning.

Data Quality Issues

Garbage in, garbage out. Fix: Curate data carefully, remove inconsistent examples.

Cost Considerations

OpenAI fine-tuning:

• Training: ~$8/1M tokens (GPT-4o mini)
• Inference: 2x base model cost

Self-hosted (LoRA):

• GPU rental: $1-5/hour
• Storage: Minimal
• Inference: Free (pay only for compute)

The Bottom Line

Fine-tuning is powerful but not always necessary:

1. Start with prompt engineering
2. Try few-shot examples
3. Fine-tune if still not good enough

When you do fine-tune:

• Use LoRA for efficiency
• Invest in data quality
• Evaluate thoroughly

Next up: RAG Explained — Giving AI access to your data