19 Tips to Better AI Fine Tuning

Summary

This video provides an in-depth look at fine-tuning language models, explaining the power and nuances of this technique. It covers the difference between fine-tuning and other methods like Retrieval Augmented Generation (RAG), details the steps and tools involved, and highlights when fine-tuning is the best approach. The video also emphasizes the importance of high-quality training data, model selection, and using tools like Axolotl, unsloth, and MLX to streamline the process. Whether you're working with specific terminology or looking to maintain consistency, this guide equips you with practical insights on fine-tuning for optimal results.

Takeaways

• 😀 Fine-tuning is not about teaching the model new information, but helping it better utilize its existing knowledge.
• 😀 Fine-tuning can help models focus on specific details and communicate more precisely, similar to teaching a doctor to specialize in a certain area.
• 😀 LoRA (Low-Rank Adaptation) is a more efficient alternative to full fine-tuning, modifying a smaller set of parameters instead of retraining the entire model.
• 😀 QLoRA (Quantized LoRA) further reduces memory requirements, making fine-tuning possible even on lower-end GPUs or CPUs.
• 😀 Fine-tuning helps improve consistency in response patterns and styles, such as formatting and terminology, rather than adding new facts or knowledge.
• 😀 High-quality training data is crucial, and it's better to have fewer but more consistent examples rather than a large volume of mediocre ones.
• 😀 Overfitting occurs when you fine-tune with too little data, leading the model to become too specialized and unable to generalize effectively.
• 😀 Ideal scenarios for fine-tuning include domain adaptation (like customizing a model to specific terminologies) and style matching (ensuring consistency across documents).
• 😀 Fine-tuning is not effective when you need a model to learn new, current information (like new frameworks or recent developments). For that, use Retrieval Augmented Generation (RAG).
• 😀 Always select a model size that matches your available computational resources; smaller models like llama2-7b can be more practical than larger models like llama2-70b.
• 😀 Before fine-tuning, it's essential to carefully choose the right base model and ensure you have the right licensing, especially for commercial use.

Q & A

What is the primary purpose of fine-tuning a model?

The primary purpose of fine-tuning is to adjust a pre-existing model to focus on specific patterns or styles of response, rather than teaching it entirely new knowledge.

How does fine-tuning differ from Retrieval-Augmented Generation (RAG)?

Fine-tuning is about refining how a model uses its existing knowledge to focus and communicate more precisely, while RAG is used to add new knowledge that the base model doesn't have.

What is LoRA, and how does it differ from full fine-tuning?

LoRA (Low-Rank Adaptation) is a method where only a smaller set of model parameters is modified, making it more computationally efficient than full fine-tuning, which requires adjusting all parameters.

What is the advantage of QLoRA over regular LoRA?

QLoRA (Quantized LoRA) enhances LoRA by reducing memory requirements through mathematical techniques, making it suitable for lower-end GPUs or even CPUs.

What is the main challenge when fine-tuning a model?

The main challenge is having quality training data. The model’s performance is directly tied to the consistency, format, and relevance of the training data provided.

When should you use fine-tuning, and when should you avoid it?

You should use fine-tuning when you need a model to be more precise in a particular domain or to follow a specific style. Avoid fine-tuning if you need the model to learn new facts or if you don’t have enough training data.

What are some common mistakes to avoid when fine-tuning a model?

Common mistakes include using too little training data (leading to overfitting), trying to teach the model new knowledge, and using inconsistent or poor-quality training data.

Why is it important to include edge cases in your training data?

Including edge cases helps the model understand its limitations and handle unusual or difficult scenarios gracefully, improving its overall robustness.

What is the role of the learning rate in fine-tuning?

The learning rate controls the size of the steps the model takes during training. A rate that’s too large may cause the model to overshoot optimal results, while one that's too small can make the training process slow.

What is the ideal model size for fine-tuning in most cases?

For most fine-tuning projects, smaller models like llama2-7b are ideal because they are efficient, faster to train, and cheaper to run compared to larger models, like llama2-70b, which require more resources.