Stanford CS336 Language Modeling from Scratch | Spring 2025 | Overview and Tokenization

CS 336: Language Models from Scratch - Lecture 1 Summary

Key Concepts:

• Language Models from Scratch: Building language models from the ground up to understand the underlying technology.
• Tokenization: Converting text into numerical representations (tokens) for language models.
• BPE (Byte Pair Encoding): A tokenization algorithm that merges frequent byte pairs to create tokens.
• Transformers: The dominant architecture for modern language models.
• Scaling Laws: Relationships between model size, training data, and performance.
• Efficiency: Optimizing model training and inference for resource utilization.
• Alignment: Fine-tuning language models to follow instructions, exhibit desired styles, and ensure safety.
• Supervised Fine-Tuning (SFT): Training a language model on labeled prompt-response pairs.
• Learning from Feedback: Improving language models using preference data or verifiers.
• DPO (Direct Preference Optimization): An algorithm for training language models from preference data.
• GRPO (Group Relative Preference Optimization): An algorithm that simplifies PPO and makes it more efficient by removing the value function.
• Kernels: Optimized code for performing computations on GPUs.
• Parallelism: Distributing model training and inference across multiple GPUs.
• Inference: Generating text using a trained language model.

1. Introduction and Motivation

• Main Topics and Key Points:
  • The course aims to provide a deep understanding of language models by building them from scratch.
  • Researchers are becoming disconnected from the underlying technology due to reliance on proprietary models.
  • Fundamental research requires tearing up the stack and co-designing data, systems, and models.
  • The industrialization of language models makes frontier models (e.g., GPT-4) inaccessible to most researchers.
  • Small language models may not be representative of larger models due to differences in flop allocation and emergent behavior.
• Key Arguments or Perspectives:
  • Percy and Tatsu believe that building language models from scratch is essential for understanding them deeply.
  • Abstraction layers in AI can be "leaky," making it difficult to understand the underlying processes.
  • Scaling is important, but efficiency is crucial for maximizing performance within a given compute budget.
• Notable Quotes or Significant Statements:
  • Tatsu: "...you got to build it from scratch to understand it..."
  • Percy: "...we want to enable the fundamental research to continue and our philosophy is to understand it you have to build it..."
• Technical Terms, Concepts, or Specialized Vocabulary:
  • Flops: Floating point operations per second, a measure of computational power.
  • Emergent Behavior: Unexpected capabilities that arise as models are scaled up.
• Logical Connections between Different Sections and Ideas:
  • The introduction establishes the need for the course by highlighting the disconnect between researchers and the technology.
  • The discussion of industrialization and limitations of small models leads to the focus on mechanics, mindset, and intuitions.

2. Course Philosophy and Objectives

• Main Topics and Key Points:
  • The course focuses on three types of knowledge: mechanics, mindset, and intuitions.
  • Mechanics: Understanding how things work (e.g., transformers, model parallelism).
  • Mindset: Squeezing the most out of hardware and taking scaling seriously.
  • Intuitions: Understanding which data and modeling decisions lead to good models.
  • The "bitter lesson" is that algorithms at scale matter, and efficiency is crucial.
  • Algorithmic efficiency has improved faster than Moore's Law.
• Key Arguments or Perspectives:
  • The right framing is to ask: "What is the best model one can build given a certain compute and data budget?"
  • Efficiency is more important at larger scales due to the high cost of wasted resources.
• Data, Research Findings, or Statistics Mentioned:
  • GPT-4 is rumored to have 1.8 trillion parameters and cost $100 million to train.
  • XAI is building clusters with 200,000 H100s.
  • OpenAI paper from 2020 showed a 44x algorithmic efficiency improvement from 2012 to 2019.

3. Historical Context and Current Landscape

• Main Topics and Key Points:
  • Language models have been around for a while, with early work by Shannon and Google.
  • The deep learning revolution in the 2010s brought key ingredients like neural language models, sequence-to-sequence models, Adam optimizer, and attention mechanisms.
  • Foundation models like ELMo, BERT, and T5 were significant advancements.
  • OpenAI's GPT-2 and GPT-3 demonstrated the power of scaling.
  • Open models like those from EleutherAI and Meta have emerged.
  • There are different levels of openness: closed models, open weight models, and open source models.
  • Frontier models are currently dominated by companies like OpenAI, Anthropic, and Google.
• Technical Terms, Concepts, or Specialized Vocabulary:
  • N-gram Models: Language models that predict the next word based on the previous n words.
  • Foundation Models: Large language models trained on vast amounts of data and adapted to various downstream tasks.
• Logical Connections between Different Sections and Ideas:
  • The historical overview provides context for the current state of language models.
  • The discussion of openness highlights the importance of learning how to build models from scratch.

4. Course Logistics and Structure

• Main Topics and Key Points:
  • The course is a five-unit class with a significant workload.
  • It is designed for people with an obsessive need to understand how things work.
  • The course has five assignments, each requiring building from scratch with unit tests and adapter interfaces.
  • Students should prototype with small datasets before running large jobs on the cluster.
  • There will be leaderboards for some assignments.
  • AI tools can be used judiciously, but students are responsible for their own learning.
  • A cluster with H100s is provided by Together AI.
  • The course is organized into five units: Basics, Systems, Scaling Laws, Data, and Alignment.
• Step-by-step Processes, Methodologies, or Frameworks Explained:
  • The course structure is divided into five units, each building upon the previous one.
  • The assignments require implementing components from scratch and benchmarking performance.

5. Course Content Overview

• Main Topics and Key Points:
  • The course covers tokenization, model architecture, training, systems optimization, scaling laws, data curation, and alignment.
  • Basics: Implementing a tokenizer (BPE), model architecture (transformer), and training loop.
  • Systems: Optimizing hardware utilization through kernels (Triton), parallelism, and inference.
  • Scaling Laws: Determining the optimal model size for a given compute budget.
  • Data: Evaluating data quality, crawling, filtering, deduplication, and transformation.
  • Alignment: Fine-tuning models to follow instructions, exhibit desired styles, and ensure safety through SFT, DPO, and GRPO.
• Technical Terms, Concepts, or Specialized Vocabulary:
  • RMS Norm: A normalization technique similar to layer norm but simpler.
  • GQA/MQA: Grouped-query attention and multi-query attention, techniques for reducing attention's computational cost.
  • Speculative Decoding: A technique for speeding up inference by using a smaller model to predict multiple tokens.
  • Chinchilla Optimal: The optimal model size for a given compute budget.
• Logical Connections between Different Sections and Ideas:
  • The course content is structured to build a complete language model pipeline, starting with the basics and progressing to more advanced topics.
  • Each unit addresses a specific aspect of language model development, contributing to the overall goal of understanding and building efficient models.

6. Tokenization Deep Dive

• Main Topics and Key Points:
  • Tokenization converts raw text into numerical representations (tokens).
  • The process involves encoding strings to tokens and decoding them back.
  • The vocabulary size is the number of unique tokens.
  • Examples of tokenizers and their behavior are shown using a website.
  • Different tokenization approaches are discussed: character-based, byte-based, word-based, and BPE.
  • Character-based tokenization has a large vocabulary size and inefficient use of tokens.
  • Byte-based tokenization has long sequences and a poor compression ratio.
  • Word-based tokenization has an unbounded vocabulary size and issues with rare words.
  • BPE is an adaptive algorithm that merges frequent byte pairs to create tokens.
  • The BPE algorithm is explained step-by-step with an example.
• Step-by-step Processes, Methodologies, or Frameworks Explained:
  • The BPE algorithm is explained in detail, including counting pairs, merging the most frequent pair, and applying the merge to the training set.
• Technical Terms, Concepts, or Specialized Vocabulary:
  • Code Point: A numerical representation of a character in Unicode.
  • Compression Ratio: The number of bytes represented by a token.
• Logical Connections between Different Sections and Ideas:
  • The discussion of different tokenization approaches leads to the explanation of BPE as a more effective solution.
  • The step-by-step explanation of the BPE algorithm provides a clear understanding of how it works.

Synthesis/Conclusion

The CS 336 course provides a comprehensive, hands-on approach to understanding and building language models from scratch. It emphasizes the importance of efficiency, scaling, and a deep understanding of the underlying technology. The course covers a wide range of topics, from tokenization and model architecture to systems optimization, data curation, and alignment. By building models from the ground up, students will gain the skills and knowledge necessary to contribute to fundamental research and develop innovative AI solutions. The course aims to instill a mindset of maximizing efficiency within given resource constraints, preparing students to tackle the challenges of building and deploying language models at scale.