Fine-Tuning LLMs for Domain-Specific Applications
Pre-trained LLMs are powerful but often lack domain expertise. Fine-tuning adapts them to specialized business contexts, improving accuracy and relevance.
Why Fine-Tune?
- Domain terminology: Industry-specific jargon and concepts
- Compliance: Legal, medical, or regulatory requirements
- Performance: Better results than generic prompting
- Cost: Smaller fine-tuned models can match larger general models
Fine-Tuning Strategies
1. Full Fine-Tuning
Update all model parameters. Resource-intensive but maximum adaptation.
2. Parameter-Efficient Fine-Tuning (PEFT)
- LoRA: Low-Rank Adaptation
- Adapters: Small trainable layers
- Prefix Tuning: Learnable prompt vectors
3. Instruction Fine-Tuning
Train on input-output pairs with task instructions.
Practical Example: Medical Q&A System
python# Fine-tuning with Hugging Face from transformers import AutoModelForCausalLM, TrainingArguments from peft import LoraConfig, get_peft_model model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b") # LoRA configuration lora_config = LoraConfig(r=8, lora_alpha=16, target_modules=["q_proj", "v_proj"]) model = get_peft_model(model, lora_config) # Train on medical domain data trainer.train()
Data Preparation
Quality Over Quantity
- 1,000 high-quality examples > 10,000 noisy examples
- Diverse task coverage
- Domain expert validation
- Consistent formatting
Data Format
json{ "instruction": "Explain the diagnosis", "input": "Patient presents with...", "output": "Based on symptoms..." }
Evaluation Metrics
- Perplexity: Model confidence
- BLEU/ROUGE: Output quality
- Human evaluation: Real-world usefulness
- Domain-specific metrics: Medical accuracy, legal compliance
Deployment Considerations
Model Size vs. Performance
- 7B models: Good balance for most use cases
- 13B models: Better performance, higher cost
- Quantization: Reduce size with minimal accuracy loss
Inference Optimization
- Model quantization (4-bit, 8-bit)
- Flash Attention 2
- Batching and caching
- GPU optimization
Lessons Learned
- Start small: Use LoRA before full fine-tuning
- Validate thoroughly: Domain expert review is critical
- Monitor drift: Performance degrades over time
- Version control: Track model versions and training data
Cost Analysis
Fine-tuning a 7B model:
- Training: 4-8 hours on A100 GPU ($20-40)
- Data preparation: 20-40 hours of expert time
- Inference: 10x cheaper than GPT-4 API
Technologies: Python, Hugging Face, PyTorch, LoRA, Deepspeed, vLLM