Token 1.11: What is Low-Rank Adaptation (LoRA)?

Making fine-tuning more efficient and less costly

Introduction

As the utilization of Large Language Models (LLMs) intensifies across various domains, the concept of fine-tuning garnered significant attention. Particularly in the context of billion-parameter models, fine-tuning is often seen as a resource-intensive endeavor. This has led to a focus on optimization methodologies, one of which is Low-Rank Adaptation (LoRA).

And today we are going to discuss it in detail. However, to fully appreciate its significance, it's crucial first to understand the necessity of fine-tuning in the LLM landscape and where it stands in comparison to other adaptation techniques.

Let’s dive in:

• Comparing LLM adaptation techniques: Identifying the necessity of fine-tuning

• Key scenarios where fine-tuning is indispensable

• Intuition behind LoRA

• How LoRA works

• The benefits of LoRA

Comparing LLM adaptation techniques

Adapting LLMs to your specific needs is key to making the most out of these powerful tools in your business. Here are some straightforward ways to do this:

1. Prompt Engineering: Designing specific input prompts that guide the model to apply its general knowledge in a way that's relevant to the task. Simply put, you need to phrase questions or requests in a way that effectively communicates what you want the AI to do or the type of information you want it to provide.

   • Few-shot or Zero-shot Learning: These techniques involve providing the model with a few or no examples of the specific task, relying on its pre-trained knowledge to infer the correct approach.

   • Chain-of-Thought (CoT) Prompting: this technique consists of modifying the original few-shot prompting by adding examples of problems and their solutions and a detailed description of intermediate reasoning steps while describing the solution. (Check “How to distinguish all the СoT-inspired concepts and use them for your projects”)

   • Other prompting techniques.

2. Retrieval-Augmented Generation (RAG): an architecture designed to harness the capabilities of large language models while providing the freedom to incorporate and update custom data at will. (Check ”What is Retrieval-Augmented Generation (RAG)?”)

3. Fine-Tuning: Involves additional training on a smaller, domain-specific dataset. This method adjusts the weights of the model to better align with the specific requirements of the task.

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We've explored chain-of-thought as part of prompt techniques and RAG, finding that they are more straightforward to implement compared to fine-tuning. Prompt techniques (especially prompt engineering) are relatively simple, requiring only a few examples to guide the model. RAG, while also less demanding, offers the benefit of integrating domain-specific data without the need to retrain the model. However, fine-tuning, despite its higher cost in terms of computational power, memory requirements, time, and expertise, is sometimes the only viable option for certain tasks. This is particularly true in scenarios where the level of customization and accuracy needed goes beyond what prompt engineering and RAG can provide.

Key scenarios where fine-tuning is indispensable include:

Previously in the FM/LLM series:

• Token 1.0: Where are you in the FMOps Infrastructure Stack?

• Token 1.1: From Task-Specific to Task-Centric ML: A Paradigm Shift

• Token 1.2: Use Cases for Foundation Models and Key Benefits

• Token 1.3: What is Retrieval-Augmented Generation (RAG)?

• Token 1.4: Foundation Models – The Building Blocks

• Token 1.5: From Chain-of-Thoughts to Skeleton-of-Thoughts, and everything in between

• Token 1.6: Transformer and Diffusion-Based Foundation Models

• Token 1.7: What Are Chain-of-Verification, Chain of Density, and Self-Refine?

• Token 1.8: Silicon Valley of AI Chips and Semiconductors

• Token 1.9: Open- vs Closed-Source AI Models: Which is the Better Choice for Your Business?

• Token 1.10: Large vs Small in AI: The Language Model Size Dilemma