LLM Learning Path Level 1 - Presentation Slides
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Welcome to the H2O LLM Learning Path - Presentation Slides Level 1! These slides, created by H2O.ai University, are designed to support your learning journey in understanding Large Language Models (LLMs) and their applications in business use cases. For more information on the course, please visit: https://h2o.ai/university/courses/large-language-models-level1/. This resource is for learning purposes only and is tailored to help you grasp the fundamental concepts of LLMs and equip you with the knowledge to apply them in real-world scenarios. The presentation slides are part of the comprehensive LLM Learning Path, starting with Level 1, which is carefully crafted to build your understanding and practical skills from the ground up. Follow along with our instructor's guidance using these materials, and ensure you develop the foundational skills necessary to unlock the power of LLMs. Happy learning!
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LLM Learning Path Level 1 - Presentation Slides
- 1. H2O.ai Confidential LLM Learning Path - Level 1 Author: Andreea Turcu Head of Global Training @H2O.ai
- 2. H2O.ai Confidential Fine-tuning Refining pre-trained models using task-specific data, enhancing their performance on targeted tasks. Foundation Powerful language models trained on extensive text data, forming the basis for various language tasks. Building Steps for LLMs 01 03 Eval LLMs Thoroughly assessing and comparing LLMs is increasingly vital due to their heightened significance and complexity. 04 05 04 03 02 01 DataPrep Converting documents into instruction pairs, like QA pairs, facilitating fine-tuning and tasks. 02 Database Effectively utilize company data with a database that seamlessly integrates new PDFs, eliminating the need for model retraining. 05 Applications Elevate interactions with advanced language comprehension and LLM-driven response generation for enriched user experiences. 06
- 3. H2O.ai Confidential Table of Contents 1. Introduction to Language Models 2. Understanding LLM Architecture / Foundation Models 3. Getting Started with LLM Data Studio 4. Fine-tuning LLMs 5. Making Your Own GPT and Fine-tuning using LLM Studio 6. Evaluating and Benchmarking LLMs 7. Practical Applications and Case Studies
- 4. H2O.ai Confidential Contents at a Glance 1. Introduction to Language Models ● What is a Language Model? ● Techniques Commonly Used ● Importance and Applications
- 5. H2O.ai Confidential Foundation Powerful language models trained on extensive text data, forming the basis for various language tasks. Building Steps for LLMs 01 05 04 03 02 01 Contents at a Glance 1. Introduction to Language Models 2. Understanding LLM Architecture / Foundation Models ● What are Foundation Models? ● Neural Networks and Deep Learning ● Transformer Architecture vs. LLM Architecture ● Pre-training & fine-tuning of LLMs ● Transfer Learning and Adaptation
- 6. H2O.ai Confidential Generative AI Definitions Foundation Models Large Language Models (LLMs) Unlabeled Training Data Additional Text-Based Data Transformer Algorithm Transformer Algorithm Foundation Model LLM Generative AI Collection of ML algorithms that learn a representation of artifacts from data and models, and use it to generate brand-new, completely original artifacts that preserve a likeness to original data or models. Foundation model Is a Large machine learning model trained on a large amount of unlabeled data using a transformer algorithm. This model can be augmented by a range of fine-tuning (adapter) techniques. The resulting model can be further adapted to a wide range of applications. Large Language Model (LLM) An LLM is a type of foundation model specifically designed for natural language processing. Generative Pre-trained Transformer (GPT) Is an LLM specifically designed to predict the next token. For example ChatGPT is a conversational application built on top of an LLM.
- 7. Essential topics: 1. Grasping the essence of Foundation Models 2. Delving into Neural Networks and Deep Learning 3. Exploring the intricacies of the Transformer Architecture 4. Understanding the concepts of pre-training and fine-tuning in LLMs 5. Navigating Transfer Learning and Adaptation techniques
- 8. Foundation models can be used for a wide range of tasks: 1. Answering questions 2. Generating human-like text 3. Translating languages 4. Creating chatbots 5. Summarizing articles, and more
- 9. Neural Networks Each node receives input from multiple nodes in the previous layer, performs a computation, and passes the output to the next layer. The output of the last layer represents the final prediction or decision made by the neural network.
- 10. Deep Learning = Neural networks with multiple layers Deep learning models are capable of learning complex patterns and representations from large amounts of data. The term "deep" refers to the depth of the network, which signifies the number of hidden layers between the input and output layers.
- 11. In forward propagation, input data flows through the network, transforming into a meaningful output.
- 12. Backpropagation fine-tunes network parameters by minimizing prediction errors through iterative adjustments based on desired output.
- 13. To remember: ● Not all neural networks qualify as deep learning models. ● Deep learning is distinguished by network depth. ● Depth enables the learning of intricate data features and relationships. ● This leads to improved performance in tasks like image recognition and natural language processing.
- 14. Applications of Neural Networks and Deep Learning in LLMs: • Natural Language Processing (NLP) • Speech Recognition • Recommendation Systems • Text Generation • Language Understanding and Context • Automation and Efficiency • User Experience Enhancement
- 15. H2O.ai Confidential v ● The emergence of Large Language Models (LLMs) coincided with advancements in language understanding and generation. ● LLMs are distinguished by their exceptional size and complexity. ○ These models consist of billions of specialized components. ○ These components enable LLMs to comprehend intricate language nuances. ● LLMs are capable of generating high-quality text.
- 16. H2O.ai Confidential v Fine-tune Example: Learn a Specific Style of Answering and Writing Fine-tuning training Hyperparameter tuning Data Scientist Fine-Tuned Large Language Model Foundation Large Language Model Autoregressive, trained on diverse data (“the whole internet”). Good at continuing text. Specialized style: learned prompt & answer, instructions
- 17. H2O.ai Confidential Crucial Role in Language Models 1. Enhanced Communication 2. Information Assessment 3. Ethical Implications 4. Prospects for the Future
- 18. H2O.ai Confidential Key Areas where LMs are used: 1. Chatbots and Virtual Assistants 2. Language Translation 3. Content Generation 4. Sentiment Analysis 5. Text Completion and Auto-correction 6. Voice Assistants
- 19. H2O.ai Confidential Distinguishing Characteristics of LLMs 1. Scale 2. Creative Writing 3. Complex Problem Solving 4. Domain Expertise 5. Enhanced Language Understanding 6. Data Efficiency 7. Pre-training and Fine-tuning 8. Contextual Understanding 9. Language Generation 10. Transfer Learning 11. Versatility and Applications 12. Research and Innovation
- 20. H2O.ai Confidential Some important terms related to the Transformer architecture: 1. Attention 2. Multi-head Attention 3. Encoder 4. Decoder 5. Self-Attention 6. Feed-Forward Neural Network 7. Positional Encoding 8. Masking
- 21. H2O.ai Confidential Reminder - The Transformer is a specialized neural network architecture introduced in the research paper "Attention is All You Need." - Its primary function is to process sequences of data. - It utilizes self-attention, a distinctive mechanism, to efficiently capture relationships between words within a sentence.
- 22. H2O.ai Confidential Reminder - Large Language Models fall within a broader category of models trained on extensive textual data without human annotations. - Prominent models such as GPT-3 and BERT are constructed based on the underlying Transformer architecture. - These models attain comprehensive language representations by harnessing the abundant data they encounter during their training process.
- 23. H2O.ai Confidential Primary objective of LLMs - The primary aim of Large Language Models is to acquire potent language representations from extensive text data. - Once they have gained this expertise, they can undergo fine-tuning for specific language tasks. - These tasks may include sentiment analysis, question-answering, or text classification, among others.
- 27. Transfer learning ● Uses a pre-trained model as a foundation for a new task. ● Instead of starting from scratch, the model begins with pre-trained weights. ● Fine-tunes on a smaller labeled dataset specific to the new task. ● Adapts pre-learned representations to the new data's patterns and characteristics. ● Ideal for tasks with limited labeled data or resource-intensive training.
- 28. Adaptation (domain adaptation) ● Targets domain differences between source and target domains. ● Its goal is to make a model trained on the source domain perform well on the target domain, even with limited labeled data. ● A key challenge is ensuring effective generalization despite distribution shifts. ● Adaptation techniques align representations from the source domain with the target domain to reduce domain discrepancies.
- 29. H2O.ai Confidential Robot Adaptation Approaches 1. Feature-based adaptation: Simplifies the robot's view by finding common features between old and new objects. 2. Instance-based adaptation: Adjusts the robot's focus by prioritizing similar objects in the new environment. 3. Model-based adaptation: Fine-tunes the robot's recognition abilities by emphasizing relevant details in the new environment.
- 30. H2O.ai Confidential ● Fine-tuning in LLMs enhances adaptation. ● Empowers models with styles, personalities, and domain knowledge. ● Starts with a pre-trained LLM. ● Pre-training is generic, lacks specificity.
- 31. H2O.ai Confidential - Knowing LLM architecture empowers researchers and practitioners. - Enables capturing context, managing long-range connections, and producing quality results. - Enhances application design, boosts model performance, and improves language-related tasks.