stanford-cs336/assignment1-basics resource

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The student starter code for Stanford CS336 Assignment 1, where learners implement language model basics (tokenizer, transformer, training loop) from scratch in Python. It includes unit tests, adapters, and data download scripts for TinyStories and OpenWebText samples.

Use cases

• learn to build a language model from scratch
• implement a BPE tokenizer in Python
• practice training a transformer on TinyStories
• coursework for Stanford CS336 language modeling
• hands-on LLM pretraining exercises

When to choose

• you want to understand LLM internals by implementing them yourself
• you are following the CS336 course and need the assignment scaffold
• you prefer minimal scaffolding with tests to guide implementation

When to avoid

• you need a production-ready training framework
• you want prebuilt models or high-level APIs like Hugging Face
• you are not prepared to write substantial code yourself

Facets

learning-resource · maturity active

machine-learning llm-training testing large-language-models deep-learning education python cross-platform course-assignment transformer language-modeling stanford-cs336 educational natural-language-processing

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