Low Rank Adaptation: Reduce the Cost of Model Fine-Tuning

Key takeaways 

LoRA makes fine-tuning LLMs faster and more memory-efficient by reducing the number of parameters required to retrain a model. 

• With LoRA, you can take an open-source LLM and fine-tune it to your specific needs, saving the effort of training a full LLM from scratch.

• LoRA also minimizes the time and resources needed to train LLMs for computer vision, natural language processing, and recommendation systems.

• You can use or implement LoRA in job roles such as data scientist, machine learning engineer, and AI researcher. 

Learn more about low rank adaptation, how it works, and explore careers where you can use LoRA to retrain LLMs. Afterward, if you’re ready to learn how to build and train deep neural networks, enroll in the Deep Learning Specialization. The program also offers insights into implementing vectorized neural networks and deep learning in applications. 

What is low rank adaptation of large language models?

Low rank adaptation is a faster and more efficient way of training large language models (LLMs) and other neural networks. The training process for a neural network is time-consuming and resource-heavy. You must fine-tune the model to use a foundational model like Google Gemini or ChatGPT for a specific task. Instead of starting from scratch, you can use low rank adaptation to lower the resources required to fine-tune a model, making it faster and more memory-efficient.

How does low rank adaptation work?

Low rank adaptation reduces the number of training parameters you need to fine-tune a model. To understand how this happens, explore the initial process of training an LLM. 

Training large language models

An LLM has layers of neural networks that process data like text or images to learn the patterns and defining characteristics of the training data set. After training with vast amounts of data, the model can generalize what it learned and apply that knowledge to other data sets. This is how a single LLM like ChatGPT or Claude AI can accomplish many tasks, such as writing a poem, generating an image, or analyzing data.

These foundational models can have billions or trillions of parameters that impact how they learn and process data. The sheer volume of learning parameters allows the LLM to handle many different tasks. If you want the model to be highly specialized at a specific task, you need to fine-tune the model to get better performance.

Fine-tuning massive LLMs can be limiting since it requires time and money. Low rank adaptation is a strategy where you can avoid fine-tuning all of the parameters and focus solely on the parameters you need for your specific purpose. 

Low rank adaptation 

Low rank adaptation effectively stops the model from changing the internal parameters, like the weights between nodes that help the model qualify data and understand how data points relate to the whole. The weights within a neural network can change as needed during fine-tuning. By freezing them at their current values, the model will not need to undergo the time-consuming and computing power-heavy process of adjusting all of the internal parameters, and the model will continue to work as expected.

Next, you can add a low rank matrix, which adds a small set of weights that includes only those you need for the task onto the existing weights of the model. Low rank is a mathematical concept, meaning this data is less complex than the original data—it has a lower rank. It’s similar to adding a filter to a camera lens in that you’re not changing what’s inside the model. Instead, you’re overlaying additional data onto it. By training only those added weights, you can train your model for specialized tasks faster using fewer computational resources.

Read more: Neural Network Weights: A Comprehensive Guide

Using low rank adaptation, you can take an open-source LLM and fine-tune it to your specific needs without training an entire LLM from scratch. 

Low rank adaptation examples

Low rank adaptation applies to any situation where you train a neural network to accomplish a task. You can use LoRA in projects like:

• Natural language processing (NLP): LLMs are particularly well-suited to NLP tasks because they can process and understand sequential data, like text. LoRA is a lightweight method of fine-tuning these models for specific tasks. For example, you could use LoRA to fine-tune an LLM to grade papers and homework in a classroom setting. The model would retain the knowledge of a foundational language model but specialize in the specific resources the students are learning from, such as a textbook. 

• Computer vision: You can also use low rank adaptation to train models that generate or understand images. A real-world example of such a model is Stable Diffusion, a generative AI model you can prompt to create images in many different styles. Low rank adaptation could help you produce a lightweight model based on Stable Diffusion but specialized to a specific task, such as illustrating a book or creating a series of particular images. 

• Recommendation systems: You can use this popular method of analyzing a user’s behavior or sales history to recommend something else they might be interested in. You may be familiar with Netflix or Amazon’s recommendation systems, which take factors like your past use and how other users with similar interests behave to predict what you might want next. You can use low rank adaptation to train a recommendation system that personalizes to each user in a way that keeps that user's data separate from the overall system.

Who uses low rank adaptation?

Professionals who create and train LLMs and other neural networks can use low rank adaptation for faster fine-tuning and lightweight, shareable models. To discover jobs where you can train LLMs, consider exploring careers like data scientist, machine learning engineer, or artificial intelligence (AI) researcher.

1. Data scientist

Average annual base salary in the US (Glassdoor): $119,000 [1]

Job outlook (projected growth from 2024 to 2034): 34 percent [2]

As a data scientist, you will help companies and organizations understand and analyze data. Data scientists use neural networks like LLMs to find patterns within data, which means you may train models for specific tasks using LoRA in this role. You'll determine the needed data and collect, preprocess, and analyze it. After this process, you'll have the necessary insights to create visualizations or reports to present your findings to leadership or colleagues.

2. Machine learning engineer

Average annual base salary in the US (Glassdoor): $126,000 [3]

Job outlook (projected growth from 2024 to 2034): 34 percent [2]

As a machine learning engineer, you'll use AI and machine learning principles to create algorithms and models to solve complex problems. You might use low rank adaptation to train existing models for specialized uses in various industries, including health care, manufacturing, entertainment, and transportation, working with robotics, self-driving vehicles, and more. 

3. AI researchers

Average annual base salary in the US (Glassdoor): $102,000 [4]

Job outlook (projected growth from 2024 to 2034): 20 percent [5]

As an AI researcher, you will study AI and find ways to advance the technology in the field. You might use low rank adaptation to create models specialized for practical purposes or to understand the theoretical principles of LLMs. In this role, you will design and execute research experiments and share your work with project stakeholders and the greater scientific community. 

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