# stanford-cs336/lectures

Repository: https://github.com/stanford-cs336/lectures
Canonical: https://ross.abutalabs.com/products/stanford-cs336-lectures
Language: Python
License Family: other
Last push: 2026-05-28T06:35:41+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 84, release rhythm 35, longevity 39
- inputs: {"age_days": 549, "days_push": 97, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3678, forks 758 (observed 2026-08-28T04:08:13.702918+00:00)

## What it is
Lecture materials for Stanford CS336 (Language Modeling from Scratch), including executable Python lectures and PDF slides. Executable lectures can be compiled into traceable, viewable web pages using the edtrace tooling.

## Use cases
- learn how language models are built from scratch
- study Stanford CS336 lecture content
- run executable lecture code on LLM training concepts
- view lecture traces in a browser
- teach a course on language modeling

## When to choose
- you want structured university course material on building language models
- you prefer executable, code-driven lectures over static slides
- you are self-studying LLM fundamentals

## When to avoid
- you need a production library or tool for training models
- you want a finished framework rather than educational content
- you need materials with a permissive open-source license for redistribution

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, llm-training, developer-tools
- domain: large-language-models, machine-learning, education, tutorials
- platform: python
- tags: lecture-notes, stanford-cs336, language-modeling, executable-lectures, course-materials, web

## Member repositories
- stanford-cs336/lectures (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:13.702918+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:31:14.857405+00:00, confidence not recorded.
  - readme: https://github.com/stanford-cs336/lectures (fetched 2026-08-28T04:08:13.702918+00:00, sha 962c142a3eda)
- Data as of 2026-08-30T08:39:29.467469+00:00.