LLM Fine-Tuning: When and How to Do It Right

Fine-tuning large language models can significantly improve performance for domain-specific tasks. This guide will help you understand when fine-tuning is the right choice and how to do it effectively.

Fine-Tuning vs. RAG: Making the Right Choice

Before diving into fine-tuning, it’s crucial to understand when it’s actually necessary.

Use RAG When:

You need up-to-date information
Your data changes frequently
You want source attribution
You have limited ML expertise

Use Fine-Tuning When:

You need consistent tone and style
Your task requires specialized knowledge
You want to reduce inference costs
You need offline capability

Types of Fine-Tuning

Full Fine-Tuning

Update all model parameters. Best for:

Maximum performance improvement
When you have substantial compute resources
Domain-specific models (legal, medical, etc.)

Parameter-Efficient Fine-Tuning (PEFT)

Methods like LoRA update only a small subset of parameters.

from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM

# Load base model
model = AutoModelForCausalLM.from_pretrained("gpt2")

# Configure LoRA
lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
)

# Apply LoRA
model = get_peft_model(model, lora_config)

Data Preparation

Quality data is crucial for successful fine-tuning.

Data Format

{
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is RAG?"},
    {"role": "assistant", "content": "RAG stands for..."}
  ]
}

Dataset Size Guidelines

Minimum: 50-100 examples for simple tasks
Recommended: 500-1000 examples for complex tasks
Ideal: 5000+ examples for production systems

Training Best Practices

Start with a good base model: GPT-4, Llama 2, or Mistral
Use validation sets: Monitor overfitting
Experiment with learning rates: Typically 1e-5 to 5e-5
Track metrics: Loss, perplexity, task-specific metrics
Save checkpoints: Don’t lose progress from crashes

Common Mistakes to Avoid

Overfitting: Model memorizes training data
Catastrophic forgetting: Model loses general capabilities
Insufficient data: Not enough examples for robust learning
Poor data quality: Noisy or inconsistent training examples

Cost Considerations

Fine-tuning isn’t free. Here’s what to expect:

GPT-3.5: ~$0.008 per 1K tokens
GPT-4: ~$0.03 per 1K tokens
Open-source models: Compute costs (GPU/TPU time)

Conclusion

Fine-tuning is a powerful technique when used appropriately. Start with RAG for most use cases, and consider fine-tuning when you need specialized behavior or consistent outputs.

Ready to fine-tune your first model? Contact us for expert guidance.