Intro to Large Language Models: Pre-training, fine-tuning, and RAG
Generative AI Fundamentals: In the Generative AI development process, understanding the distinctions between pre-training, fine-tuning, and RAG (Retrieval-Augmented Generation) is crucial for efficient resource allocation and achieving targeted results. Here’s a comparative analysis for a practical perspective: Pre-training: • Purpose: To create a versatile base model with a broad grasp of language. • Resources & Cost: Resource-heavy, requiring thousands of GPUs and significant investment, often in millions. • Time & Data: Longest phase, utilizing extensive, diverse datasets. • Impact: Provides a robust foundation for various AI applications, essential for general language understanding. Fine-tuning: • Purpose: Customize the base model for specific tasks or domains. • Resources & Cost: More economical, utilizes fewer resources. • Time & Data: Quicker, focused on smaller, task-specific datasets. • Impact: Enhances model performance for particular applications, crucial for specialized tasks and efficiency in AI solutions. RAG: • Purpose: Augment the model’s responses with external, real-time data. • Resources & Cost: Depends on retrieval system complexity. • Time & Data: Varies based on integration and database size. • Impact: Offers enriched, contextually relevant responses, pivotal for tasks requiring up-to-date or specialized information. So what? Understanding these distinctions helps in strategically deploying AI resources. While pre-training establishes a broad base, fine-tuning offers specificity. RAG introduces an additional layer of contextual relevance. The choice depends on your project’s goals: broad understanding, task-specific performance, or dynamic, data-enriched interaction. Effective AI development isn’t just about building models; it’s about choosing the right approach to meet your specific needs and constraints. Whether it’s cost efficiency, time-to-market, or the depth of knowledge integration, this understanding guides you to make informed decisions for impactful AI solutions. Save the snapshot below to have this comparative analysis at your fingertips for your next AI project.What is Pre-training?
Generative AI Fundamentals Part 4 - Pre-training: https://youtu.be/R75Sy88zSEI?si=be9PFTSr8N5cDtGV What is Pre-training? Pre-training is the process of teaching a model to understand and process language before it's fine-tuned for specific tasks. It involves exposing the model to vast amounts of text data. How does Pre-training work? During pre-training, the model learns to predict the next word in a sentence, understand context, and capture the essence of language patterns. This is done through unsupervised learning, where the model organizes and makes sense of data without explicit instructions. How to train your ChatGPT? 1. Download ~10TB of text. 2. Get a cluster of ~6,000 GPUs. 3. Compress the text into a neural network, pay ~$2M, wait ~12 days. 4. Obtain base model. (Numbers sourced from "Intro to LLMs" by Andrej Karpathy) In the next couple of videos we will talk about Fine-Tuning and Retrieval-Augmented Generation (RAG). Thanks for tuning in!What is a Transformer Model?
Generative AI Fundamentals Part 3: https://youtu.be/WJVkwFBe2rY?si=Jgh31TpzxNejwbOm If you have looked up the full-form of ChatGPT, you know that GPT stands for Generative Pre-Trained Transformer. What is a Transformer Model? A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence. It was first introduced in a 2017 research paper by Google titled “Attention is All You Need”. At the time, it was showcased as a translation model but its applications in text generation have become exceedingly popular. How does a Transformer Model work? Transformer models apply an evolving set of mathematical techniques, called attention or self-attention, to detect subtle ways even distant data elements in a series influence and depend on each other. The power of the transformer architecture lies in its ability to learn the relevance and context of all of the words in a sentence. Not just for each word to its neighbor, but to every other word in a sentence. The model can then apply attention weights to those relationships and learn the relevance of each word to each other word, no matter where they are in the input. Transformer Models provide a step-change in performance over RNNs or Recurrent Neural Networks by parallelizing the process of text generation which was previously conducted sequentially, and was therefore more limiting. This paved the way for the Generative AI revolution we are experiencing today. Stay tuned for the next video where we will dive deeper into pre-training and fine-tuning LLMs.What is a Large Language Model?
Generative AI Fundamentals Part 2: https://youtu.be/HBHhYgH3RiE?si=kZaxQdeiAnMHkPux What is a Large Language Model? A multi-billion parameter language model trained on extensive text datasets to predict the next word in a self-supervised manner. How does a Large Language Model work? LLMs work by analyzing vast amounts of text, learning statistical patterns and relationships between words, and using this knowledge to generate text that is coherent and contextually appropriate. For a deeper dive, check out Andrej Karpathy's talk: https://lnkd.in/gshNJUkPWhat is Generative AI?
Introducing Part 1 of the Generative AI Fundamentals Series: https://youtu.be/Hr1_DPh1sEU?si=4j4Ere4ImZHObjEo What is Generative AI? AI systems that can generate new content based on a variety of inputs. Inputs and outputs to these models can include text, images, audio, 3D models, or other types of data. Large Language Models (LLMs) such as GPT-4 and Llama 2 are popular examples of Generative AI. How Does Generative AI Work? Generative AI models employ neural networks to identify patterns and structures in existing data, enabling them to generate new and original content. A notable breakthrough in generative AI is the ability to apply various learning approaches, including unsupervised or semi-supervised learning, in their training. This has empowered organizations to more effectively and rapidly utilize large volumes of unlabeled data to create foundational models. These models, as their name suggests, serve as a base for AI systems capable of performing multiple tasks. Examples of these foundational models include GPT-4 and Stable Diffusion, which enhance the user's ability to harness the nuances of language. For instance, applications such as ChatGPT, powered by GPT-4, allow users to generate essays from short text prompts. In contrast, Stable Diffusion can create photorealistic images based on text inputs. In the next video we will dive deeper into Large Language Models. Stay tuned!
