Building LLMs from the Ground Up: A 3-hour Coding Workshop

2024-08-31 · Source: Sebastian Raschka · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, extended

Summary

This coding workshop, led by Sebastian Rashka, a staff research engineer at Lightning AI, focuses on building large language models (LLMs) from the ground up. The workshop covers six main topics, starting with an introduction to LLMs and then diving into practical coding examples. Key areas include understanding LLM input data, coding the LLM architecture, pre-training an LLM on a small dataset, loading larger pre-trained LLMs, and fine-tuning LLMs to follow human instructions. The content emphasizes a "from scratch" approach to foster a deeper understanding of LLM mechanics, utilizing a public domain short story, "The Verdict" by Edith Wharton, for training purposes. It also introduces the `tiktoken` library for efficient tokenization and the L GPT library for more advanced LLM tasks, including Low-Rank Adaptation (LoRA) for efficient fine-tuning.

Key takeaway

For AI Engineers and students aiming to deeply understand LLM mechanics, this workshop provides a practical, code-centric approach. You should prioritize hands-on implementation of core components like tokenization and architecture to grasp underlying principles. Consider using tools like L GPT and techniques like LoRA to manage computational resources efficiently when experimenting with larger models or datasets, ensuring your learning is both foundational and scalable.

Key insights

Building LLMs from scratch, including data preparation, architecture, pre-training, and fine-tuning, enhances fundamental understanding.

Principles

• Tokenization is foundational to LLM data processing.
• LLMs are trained via next-token prediction.
• LoRA enables efficient fine-tuning of large models.

Method

The workshop outlines a workflow: data preparation (tokenization, batching), architecture coding (Transformer blocks), pre-training (loss minimization), and instruction fine-tuning (using prompt templates and LoRA).

In practice

• Use `tiktoken` for fast, GPT-compatible tokenization.
• Employ L GPT for streamlined LLM downloading and fine-tuning.
• Apply LoRA to reduce trainable parameters during fine-tuning.

Topics

• Large Language Models
• Tokenization
• LLM Architecture
• Model Pre-training
• Instruction Fine-tuning

Best for: Machine Learning Engineer, AI Engineer, AI Student

Related on AIssential

• Training an LLM from Scratch, Locally — Angelos Perivolaropoulos, ElevenLabs — Training an LLM from scratch involves understanding core components like tokenization, transformer architecture, and the training loop.
• System Report for CCL25-Eval Task 5: New Dataset and LoRA-Fine-Tuned Qwen2.5 — Domain-specific datasets and LoRA fine-tuning significantly enhance LLM performance in specialized tasks like classical poetry analysis.
• Developing an LLM: Building, Training, Finetuning — LLM development involves building architecture, pre-training on vast datasets, and fine-tuning for specific tasks and behaviors.
• The Must-Know Topics for an LLM Engineer — LLM engineering requires understanding an interdependent stack, from data representation and model architecture to training, optimization, and evaluation.
• Building LLM From Scratch: Understanding How Large Language Models Work — Large Language Models fundamentally predict the next token by processing numerical representations of text through attention mechanisms.
• How Large Language Models Are Actually Built, Everything I Learned From Stanford’s CS229 LLM… — Data, evaluation, and systems are the true differentiators in LLM quality, not just architecture.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Sebastian Raschka.