Episode 26: Developing and Training LLMs From Scratch
May 15, 2024
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Sebastian Raschka discusses developing and training large language models (LLMs) from scratch, covering topics like prompt engineering, fine-tuning, and RAG systems. They explore the skills, resources, and hardware needed, the lifecycle of LLMs, live coding to create a spam classifier, and the importance of hands-on experience. They also touch on using PyTorch Lightning and fabric for managing large models, and reveal insights on techniques in natural language processing models and evaluating LLMs for classification problems.
In building Large Language Models (LLMs), understanding the input-output dynamics is crucial for coding the data input process.
Engineering LLMs involves pre-training on broad data sets followed by fine-tuning for specific tasks to enhance model capabilities.
Choosing the right hardware and resources, from single GPUs to cloud platforms like AWS, impact LLM development efficiency.
Constructing an LLM from scratch involves step-by-step processes, including coding tasks like next token prediction for pre-training and fine-tuning.
Experimenting with architectural variations like GPT-2 and Mistral refines coding skills and boosts comprehension of advanced LLM concepts.
Deep dives
Building LLMs: Understanding the LLM Life Cycle from Scratch
To delve into the world of building Large Language Models (LLMs), it's essential to start by understanding the entire life cycle of an LLM from the ground up. Beginning with the fundamental concept of feeding data into the model, you lay the foundation for comprehending the input-output dynamics of LLMs. This first step involves coding the data input process before diving into the complexities of the LLM architecture.
Engineering the LLM Architecture: Pre-Training and Fine-Tuning for Specialized Tasks
Moving onwards, the journey into building LLMs progresses to engineering the architecture through pre-training and fine-tuning for specific tasks. Pre-training the LLM involves training it on a broad dataset, while fine-tuning adjusts it to perform specialized functions. Whether it's instructing the model for precise responses or instilling new knowledge through continued pre-training, these stages hone the LLM's capabilities for diverse applications.
Training Environments: Navigating Hardware and Resource Requirements for LLM Work
Understanding the hardware and resource needs for working with LLMs is crucial. From the limitations of single GPUs to the scalability of multi-GPU and cloud platforms like AWS, GCP, and Azure, choosing the right training environment impacts the efficiency and effectiveness of LLM development. Tools like Lightning AI studio provide flexibility and cost efficiency in managing compute resources for LLM training.
Coding Fundamentals: Implementing LLMs Step-by-Step for Hands-On Learning
Implementing an LLM from scratch involves a step-by-step process that starts with understanding the data input and progresses to constructing the architecture and training the model. By coding tasks like next token prediction for pre-training and fine-tuning, the practical application of building LLMs enhances learning by ensuring accuracy in model implementation.
Iterating the Architectural Design: Exploring Variations and Derivatives of LLMs for Enhanced Learning
As you venture into the realm of building LLMs, experimenting with architectural variations and derivatives such as GPT-2, Mistral, and Fi offers a deeper understanding of the nuances of LLM design. Constructing models that share a base architecture while exploring different functionalities through coding exercises like loading pre-trained weights refines your coding skills and boosts comprehension of advanced LLM concepts.
Importance of Excitement and Teamwork in Learning
Pursuing topics that spark excitement and partnering with others to learn and grow can be pivotal when engaging with complex subjects like LLMs. Specializing in areas of interest, learning from colleagues, and acknowledging the collaborative nature of knowledge exchange are essential components of successful exploration and skill development.
Challenges and Tips for Evaluating LLMs
Evaluating LLMs poses challenges, particularly in ensuring the test data is independent to prevent pre-training data overlap. Benchmarking for factual correctness is common, but evaluating conversational tasks presents a unique hurdle. Techniques like adjusting content for insight into memory recall can aid in assessment, highlighting the nuanced nature of verifying LLM capabilities.
Potential Impacts of LLMs on Community Interaction
The integration of LLMs in platforms like Stack Overflow raises concerns about a potential reduction in community interactions and knowledge-sharing dynamics. The convenience of LLM-based responses may deter active engagement on community-driven platforms, potentially affecting the collaborative contribution and problem-solving found in web forums like Stack Overflow.
Balancing LLM Utility and Community Engagement
While LLMs offer efficiencies in answering routine queries, their widespread use may alter user participation on online platforms like Stack Overflow or Wikipedia. Maintaining a balance between leveraging LLM capabilities for standardized tasks while promoting active community engagement to foster unique problem-solving approaches is crucial for sustaining dynamic knowledge-sharing environments.
Hugo speaks with Sebastian Raschka, a machine learning & AI researcher, programmer, and author. As Staff Research Engineer at Lightning AI, he focuses on the intersection of AI research, software development, and large language models (LLMs).
How do you build LLMs? How can you use them, both in prototype and production settings? What are the building blocks you need to know about?
In this episode, we’ll tell you everything you need to know about LLMs, but were too afraid to ask: from covering the entire LLM lifecycle, what type of skills you need to work with them, what type of resources and hardware, prompt engineering vs fine-tuning vs RAG, how to build an LLM from scratch, and much more.
The idea here is not that you’ll need to use an LLM you’ve built from scratch, but that we’ll learn a lot about LLMs and how to use them in the process.
Near the end we also did some live coding to fine-tune GPT-2 in order to create a spam classifier!
