Summary of "Stanford CS336 Language Modeling from Scratch | Spring 2025 | Lecture 1: Overview and Tokenization"
Summary of "Stanford CS336 Language Modeling from Scratch | Spring 2025 | Lecture 1: Overview and Tokenization"
Main Ideas and Concepts
- Course Introduction and Philosophy
- The course CS336 focuses on building language models from scratch, covering the entire pipeline: data systems, modeling, and training.
- The instructors emphasize the importance of understanding language models deeply by building them, rather than relying solely on high-level APIs or prompting proprietary models.
- There is a concern that modern researchers are becoming disconnected from the underlying technology due to industrialization and abstraction layers.
- The course aims to teach foundational mechanics, mindset (especially about scaling and efficiency), and partial intuitions about modeling decisions.
- Frontier models (e.g., GPT-4) are large, expensive, and proprietary, so the course focuses on smaller-scale models that still provide valuable learning.
- Challenges of Scale and Emergent Behavior
- Small-scale models differ significantly from large-scale ones in computational characteristics and emergent behaviors (e.g., in-context learning).
- Scaling laws and efficiency are crucial; the course teaches how to optimize model performance given compute and data constraints.
- The "bitter lesson" is that algorithms at scale matter more than just throwing compute at problems; efficiency improvements have drastically reduced costs over time.
- Historical Context and Current Landscape
- Language models have a long history from Shannon’s entropy estimates, n-gram models, to neural language models and transformers.
- Key milestones include the introduction of attention mechanisms, transformer architecture (2017), and foundation models like BERT, T5.
- OpenAI’s engineering and scaling mindset led to GPT-2 and GPT-3.
- There are various levels of model openness: closed models (GPT-4), open-weight models, and fully open-source models.
- The course aims to teach best practices based on open research and community knowledge.
- Course Structure and Assignments
- The course is rigorous and workload-heavy, designed for students who want deep understanding rather than quick application.
- Five main assignments, each building from scratch without scaffolding code, focusing on implementation, correctness, efficiency, and benchmarking.
- Students use a cluster with H100 GPUs; encouraged to prototype locally with small data before scaling up.
- Use of AI tools like Copilot is permitted but should be balanced with learning responsibility.
- Five Pillars (Units) of the Course
- Basics: Implement tokenizer, transformer architecture, training loop, optimizer (AdamW), and loss function. Use BPE tokenizer and train on small datasets.
- Systems: Optimize GPU kernels, parallelism (data and model parallelism), and inference efficiency. Learn GPU architecture, memory hierarchy, and kernel programming using Triton.
- Scaling Laws: Study how to optimally allocate compute between model size and data (Chinchilla scaling laws). Conduct experiments at small scale and extrapolate to larger scale.
- Data: Understand data curation, filtering, deduplication, and legal issues. Work with raw Common Crawl data to build high-quality datasets. Evaluate models using perplexity and standardized benchmarks.
- Alignment: Techniques to fine-tune base models to follow instructions, specify style, and improve safety. Includes supervised fine-tuning (SFT), learning from preference data, verifiers, and reinforcement learning algorithms like PPO, DPO, and GRPO.
- Tokenization Deep Dive
- Tokenization converts raw Unicode text strings into sequences of integers (tokens).
- Different tokenization approaches:
- Character-based: Maps each character to a code point; inefficient due to large vocabulary and poor compression.
- Byte-based: Uses raw bytes; small vocabulary but very long sequences, leading to inefficiency.
- Word-based: Splits text by words; vocabulary size can be huge and unknown, leading to out-of-vocabulary problems.
- Byte Pair Encoding (BPE): Adaptive method that merges frequent pairs of bytes or tokens iteratively to build a vocabulary that balances compression and vocabulary size.
- BPE is the standard tokenizer used in GPT-2 and remains effective despite its age.
- The lecture included a step-by-step example of BPE merges and implementation details.
- Tokenization is important for efficiency because attention mechanisms scale quadratically with sequence length.
- Course Logistics and Resources
- Lectures are recorded and available on YouTube with some delay.
- Online materials, assignments, and cluster access are provided.
- Grading based on correctness (unit tests) and performance (loss and efficiency).
- Slack or communication channels will be provided.
- Auditors have access to all materials.
Detailed Methodologies and Instructions
- Building a BPE Tokenizer:
- Start with raw text converted to bytes.
- Count occurrences of adjacent byte pairs.
- Merge the most frequent pair into a
Category
Educational
