Fine-Tuning & Customization FAQs

Question 1

When should I choose fine-tuning over RAG?

Accepted Answer

Choose fine-tuning for deep specialization, specific output styles, or format control where prompt engineering and RAG have failed to capture necessary nuances or domain-specific styles.

Question 2

What is the difference between LoRA and QLoRA?

Accepted Answer

LoRA uses low-rank adapters to update weights efficiently, while QLoRA adds 4-bit quantization to drastically reduce memory usage, allowing large models to be trained on consumer-grade GPUs.

Question 3

Does this skill support Unsloth?

Accepted Answer

Yes, it includes implementation patterns for Unsloth, providing up to 2x faster training speeds and significantly reduced VRAM usage compared to standard transformers methods.

Question 4

How many examples do I need for effective fine-tuning?

Accepted Answer

For most domain-specific tasks, it is generally recommended to have at least 1,000 high-quality training examples to ensure the model captures the desired patterns without overfitting.

Question 5

What is DPO alignment used for?

Accepted Answer

Direct Preference Optimization (DPO) is used to align model behavior with human preferences by training on a dataset of 'chosen' and 'rejected' responses to the same prompt.

Fine-Tuning & Customization

Key Features

Use Cases

Fine-Tuning & Customization

Key Features

Use Cases