| name | llm-fine-tuning |
| description | Fine-tune large language models efficiently using LoRA, QLoRA, and PEFT methods. Use for domain adaptation, instruction tuning, task-specific optimization, and parameter-efficient training of LLMs. |
LLM Fine-Tuning
Fine-tune large language models efficiently using parameter-efficient methods.
Overview
LLM fine-tuning adapts pre-trained models to specific tasks or domains. This skill covers efficient techniques like LoRA, QLoRA, and full fine-tuning strategies.
Quick Reference
| Scenario | Recommended Approach | Reference File |
|---|
| Efficient fine-tuning with limited resources | LoRA, QLoRA, PEFT methods | /references/peft-methods.md |
| Full model fine-tuning | Supervised fine-tuning, instruction tuning | /references/full-finetuning.md |
| Alignment and safety | RLHF, DPO, preference optimization | /references/alignment.md |
Core Principles
- Parameter Efficiency - Update small subset of parameters
- Task Adaptation - Specialize model for specific use case
- Data Quality - High-quality training data is critical
- Evaluation - Rigorous testing on held-out data
- Monitoring - Track metrics to prevent degradation
Fine-Tuning Methods
LoRA (Low-Rank Adaptation)
Inject trainable low-rank matrices into model layers.
Advantages:
- 0.1-1% of parameters trainable
- Fast training
- Multiple adapters for one base model
- No inference latency
Hyperparameters:
- Rank (r): 4-64 typical
- Alpha: Scaling factor
- Target modules: Which layers to adapt
QLoRA (Quantized LoRA)
LoRA with 4-bit quantized base model.
Benefits:
- Fine-tune 65B model on single GPU
- Minimal performance loss
- Extreme memory efficiency
Requirements:
- bitsandbytes library
- NVIDIA GPU with compute capability ≥ 7.0
Full Fine-Tuning
Update all model parameters.
When to Use:
- Abundant compute resources
- Significant domain shift
- Maximum performance needed
Challenges:
- High memory requirements
- Risk of catastrophic forgetting
- Expensive
Training Process
1. Data Preparation
- Collect high-quality examples
- Format as instruction-response pairs
- Split into train/validation/test
- Typical size: 1K-100K examples
2. Model Selection
- Choose base model (Llama, Mistral, GPT)
- Consider size vs performance trade-off
- Check license for commercial use
3. Training
- Set hyperparameters (LR, batch size, epochs)
- Monitor training and validation loss
- Use gradient accumulation for large batches
- Apply early stopping
4. Evaluation
- Test on held-out data
- Human evaluation for quality
- Compare to base model
- Check for regressions
Using the Reference Files
/references/peft-methods.md — LoRA, QLoRA, Prefix Tuning, Adapter layers, implementation with Hugging Face PEFT library.
/references/full-finetuning.md — Supervised fine-tuning, instruction tuning, data preparation, training strategies, and hyperparameter tuning.
/references/alignment.md — RLHF (Reinforcement Learning from Human Feedback), DPO (Direct Preference Optimization), safety considerations.
Best Practices
- Start with LoRA/QLoRA for efficiency
- Use high-quality, diverse training data
- Monitor validation loss to prevent overfitting
- Use learning rate warm-up
- Apply gradient clipping
- Save checkpoints regularly
- Evaluate thoroughly before deployment
- Document training process
Common Pitfalls to Avoid
- Insufficient or low-quality training data
- Overfitting to small datasets
- Learning rate too high (catastrophic forgetting)
- Not using validation set
- Ignoring base model capabilities
- Training too many epochs
- Not testing on diverse inputs
- Deploying without human evaluation
