Fine-Tuning LLMs
Customize LLMs for your specific use case through fine-tuning. This path covers the complete fine-tuning workflow from deciding whether to fine-tune, through dataset preparation, training with LoRA and QLoRA, evaluation, and deployment of fine-tuned models.
What You'll Learn
- Decide when fine-tuning is the right approach vs. prompting or RAG
- Prepare high-quality training datasets for fine-tuning
- Implement LoRA and QLoRA for efficient fine-tuning
- Set up training pipelines with proper hyperparameters
- Evaluate fine-tuned models rigorously
- Deploy fine-tuned models to production
- Handle common fine-tuning pitfalls and debugging
Course Lessons
When to Fine-Tune: Decision Framework
18 min readBuild a clear framework for deciding between prompting, RAG, and fine-tuning based on your specific use case, data, and requirements.
Dataset Preparation for Fine-Tuning
22 min readCreate high-quality training datasets — data collection, cleaning, formatting, annotation, and the minimum data requirements for effective fine-tuning.
LoRA and QLoRA: Efficient Fine-Tuning
25 min readUnderstand and implement LoRA and QLoRA — parameter-efficient fine-tuning methods that reduce GPU requirements by 10-100x.
Training Pipeline Setup
22 min readConfigure training with the right hyperparameters — learning rate, batch size, epochs, warmup, and the tools (Hugging Face, Axolotl, Unsloth) that make training easier.
Evaluating Fine-Tuned Models
20 min readRigorously evaluate your fine-tuned model — automated metrics, human evaluation, A/B testing, and detecting overfitting or catastrophic forgetting.
Read lesson →Deploying Fine-Tuned Models
18 min readTake your fine-tuned model to production — serving infrastructure, quantization for deployment, API design, and cost optimization.
Debugging Fine-Tuning Issues
15 min readTroubleshoot common fine-tuning problems including loss plateaus, quality degradation, mode collapse, and data contamination.
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