Fine-Tuning LLMs
Master the art of adapting foundation models to your specific use cases
Overview
Fine-tuning transforms general-purpose language models into specialized tools for your domain. This section covers everything from deciding when to fine-tune to advanced techniques like RLHF and DPO, with practical code examples and interview-focused content.
Learning Objectives
By completing this module, you will be able to:
- Evaluate when fine-tuning is the right approach vs prompting or RAG
- Implement full fine-tuning and parameter-efficient methods (LoRA, QLoRA)
- Design instruction-tuning datasets and preference data pipelines
- Apply RLHF and DPO to align models with human preferences
- Diagnose common failure modes like overfitting and catastrophic forgetting
Module Structure
Modules
| Module | Description | Key Topics |
|---|---|---|
| When to Fine-Tune | Decision framework for customization strategies | Prompt engineering vs RAG vs fine-tuning |
| Full Fine-Tuning | Complete model weight updates | Training loops, catastrophic forgetting |
| PEFT Methods | Parameter-efficient fine-tuning | LoRA, QLoRA, AdaLoRA, IA3, adapters |
| Data Preparation | Dataset quality and curation | Deduplication, synthetic data, mixing |
| Instruction Tuning | Teaching models to follow instructions | FLAN, T0, template design |
| RLHF & DPO | Human preference alignment | Reward modeling, PPO, DPO |
| Evaluation | Measuring fine-tuning success | Overfitting, benchmarks, A/B testing |
Fine-Tuning Landscape
Quick Decision Matrix
| Approach | Data Needed | Compute Cost | Use When |
|---|---|---|---|
| Prompting | None | $ | Task is expressible in natural language |
| RAG | Documents | $$ | Need external/dynamic knowledge |
| LoRA/PEFT | 1K-100K examples | $$ | Style/format changes, domain adaptation |
| Full Fine-Tuning | 10K-1M examples | $$$$ | Fundamental capability changes |
| RLHF/DPO | Preference pairs | $$$$$ | Alignment with human values |
Interview Focus Areas
Common Interview Topics
- Trade-off Analysis: When to use which customization approach
- Technical Implementation: LoRA math, training loops, hyperparameters
- Failure Modes: Catastrophic forgetting, overfitting, mode collapse
- Data Quality: How data issues manifest in fine-tuned models
- Alignment: RLHF vs DPO, reward hacking, preference data quality
Prerequisites
Before diving into fine-tuning, ensure familiarity with:
- Transformer architecture fundamentals
- PyTorch basics (tensors, autograd, nn.Module)
- Basic NLP concepts (tokenization, embeddings)
- Familiarity with HuggingFace ecosystem
Tools & Libraries
| Library | Purpose |
|---|---|
transformers | Model loading, tokenizers, Trainer API |
peft | LoRA, AdaLoRA, IA3 implementations |
trl | RLHF, DPO, reward modeling |
datasets | Data loading and preprocessing |
accelerate | Distributed training |
bitsandbytes | Quantization for QLoRA |
Summary
| Concept | Key Insight |
|---|---|
| Fine-Tuning Purpose | Adapt general models to specific tasks/domains |
| PEFT Advantage | 10-1000x fewer trainable parameters |
| Data Quality | More important than data quantity |
| Evaluation | Must test beyond training distribution |
| Alignment | RLHF/DPO for human preference optimization |
Sources
- Hu et al., "LoRA: Low-Rank Adaptation of Large Language Models" (2021)
- Dettmers et al., "QLoRA: Efficient Finetuning of Quantized LLMs" (2023)
- Ouyang et al., "Training language models to follow instructions with human feedback" (2022)
- Rafailov et al., "Direct Preference Optimization" (2023)
- Wei et al., "Finetuned Language Models Are Zero-Shot Learners" (FLAN) (2021)