#33 Is LoRA the Right Alternative to Full Fine-Tuning?

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! We are trying something new in this issue and focusing on deeper discussions on LLM essentials like prompting, LoRA, vector search, and more. I also shared a bunch of short, digestible videos on my channel on key LLM/GenAI concepts and architectures (linked below). Enjoy the read!

What’s AI Weekly

This week in What’s AI, I created several short videos covering some of the most important aspects of LLMs today. I discuss RAG, MoE, diffusion models, temperature and hallucinations, and more. Check out this digestible playlist here!

— Louis-François Bouchard, Towards AI Co-founder & Head of Community

Learn AI Together Community section!

AI poll of the week!

Some prompting techniques in our AI toolkit are zero-shot, few-shot, chains, chain-of-thought, and role prompting.

• Zero-shot prompting is when a model is asked to produce output without examples demonstrating the task. Many tasks are well within Large Language Models’ capabilities, so it works well for day-to-day tasks.
• Few-shot prompting allows language models to learn from a limited number of samples. This adaptability allows them to handle various tasks with only a small set of training samples.
• Role prompting involves instructing the LLM to assume a specific role or identity for task execution, such as functioning as a copywriter. This instruction can influence the model’s response by providing context or perspective for the task.
• Chain Prompting involves linking a series of prompts sequentially, where the output from one prompt serves as the input for the next.
• Chain of Thought Prompting (CoT) is a method designed to prompt large language models to articulate their thought process, enhancing the accuracy of the results. This technique involves presenting examples that showcase the reasoning process, guiding the LLM to explain its logic while responding to prompts.

Although these techniques are great guidelines, prompting is an iterative process; there’s no perfect way to do it. The four prompting keywords to remember are precise language, sufficient context, testing variations, and reviewing output.

For everyone who selected ‘using specific techniques’, tell the community and us which techniques work best for you and what your general use cases are.

Meme of the week!

Meme shared by rucha8062

TAI Curated section

Article of the week

Visualizing Low-Rank Adaptation (LoRA) by JAIGANESAN

LLMs require substantial internal data for enterprise use cases, and the current generation of LLMs requires a complex pipeline involving RAG, fine-tuning, and function calling to use this and achieve the reliability needed for corporate applications. For training or full fine-tuning a one billion parameter model, we need at least 24 to 32 GB HBM in GPU, and we also need to store checkpoints of the training Model. All the model parameters remain active, so it is computationally costly for average users to do full fine-tuning.

That’s where LoRA comes in. In LoRA, we freeze the model’s parameters and fine-tune the model with fewer separate parameters, which reduces the computation resources and efficiently fine-tunes the model. With LoRA, we can easily switch between different fine-tuned models, we don’t require large memory allocations, and the LoRA fine-tuning process is much faster.

This article looks into the inner workings of fine-tuning and explores the concepts that make it possible. Specifically, it explores Singular Value Decomposition (SVD), its connection to LoRA, and how fine-tuning occurs in feedforward networks.

Our must-read articles

1. In-Depth Understanding of Vector Search for RAG and Generative AI Applications by Talib

Vector search and Retrieval-Augmented Generation (RAG) significantly enhance the capabilities of large language models (LLMs) by providing more accurate and contextually relevant responses. Vector search works by converting data into vector embeddings stored in vector databases, allowing efficient similarity searches crucial for applications like recommendation systems and customer support bots. RAG integrates these external data sources into the LLM pipeline, supplementing the model with additional information from vector databases to improve its responses without extensive retraining. Over time, RAG systems have evolved from simple Retrieve-Read approaches to advanced models with reranking, rewriting, and modular components that enhance relevance and precision. These systems include various modules such as search, memory, fusion, and task adaptability, making RAG more flexible and efficient for diverse tasks.

This article shows a practical implementation of vector embeddings, storing them in a database and performing similarity searches to retrieve relevant information for generating responses.

2. Revolutionizing Named Entity Recognition with Efficient Bidirectional Transformer Models by Chien Vu

NER is crucial in natural language processing for identifying and classifying entities like names, dates, and locations in text. Traditional models often struggled with context and complexity, but the introduction of bidirectional transformers like BERT (Bidirectional Encoder Representations from Transformers) has significantly improved NER accuracy and efficiency. These models leverage the context from both directions, enabling a better understanding of the nuances in language.

GLiNER addresses the limitations of traditional and large autoregressive NER models by introducing a more efficient and flexible approach. It offers a more efficient, scalable, and versatile approach to detecting NER, making it a valuable tool for various NLP applications. The article highlights the practical applications of these advancements, such as improved information extraction in various industries, from finance to healthcare. It discusses the future potential of integrating these models with other AI technologies to enhance their capabilities further.

3. From Concept to Creation: U-Net for Flawless Inpainting by Dawid Kopeć

Image inpainting is a powerful computer vision technique for restoring missing or damaged parts of images. U-Net, a versatile convolutional neural network architecture, is revolutionizing the field of image inpainting, particularly for seamless image restoration and enhancement tasks. U-Net has a symmetrical design comprising an encoder and a decoder, pivotal in capturing high-resolution features and spatial context, enabling the network to generate high-quality inpainted images. The encoder compresses the input image into a latent space representation while the decoder reconstructs the image, filling in the missing or corrupted parts with remarkable accuracy. Moreover, skip connections between corresponding layers in the encoder and decoder ensure the preservation of spatial information, enhancing the model’s performance. U-Net’s efficacy in various applications, from medical imaging to creative arts, underscores its robustness and adaptability.

This article goes deeper into building and implementing a U-Net architecture specifically for image inpainting and aims to bridge that gap, offering a comprehensive guide for anyone interested in using U-Net for this exciting application.

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Published via Towards AI