# afshinea/stanford-cme-295-transformers-large-language-models

VIP cheatsheet for Stanford's CME 295 Transformers and Large Language Models

Repository: https://github.com/afshinea/stanford-cme-295-transformers-large-language-models
Canonical: https://ross.abutalabs.com/products/stanford-cme-295-transformers-large-language-models
License: MIT
License Family: permissive
Last push: 2026-05-25T01:08:57+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 84, release rhythm 35, longevity 37
- inputs: {"age_days": 528, "days_push": 101, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4656, forks 674 (observed 2026-08-28T04:08:55.675309+00:00)

## What it is
A multilingual VIP cheatsheet summarizing key concepts from Stanford's CME 295 Transformers & Large Language Models course, distributed as a PDF. It covers transformer architectures, LLM training and optimization techniques, and applications like RAG and agents.

## Use cases
- review transformer architecture concepts before an exam
- quickly look up LoRA and fine-tuning techniques
- study for a course on large language models
- get a concise overview of attention variants like flash attention
- learn about RAG, agents, and reasoning models at a glance
- find a multilingual summary of LLM fundamentals

## When to choose
- you want a compact, well-organized reference for transformers and LLMs
- you are taking or teaching a course covering these topics
- you need study material in one of the many available languages

## When to avoid
- you need hands-on code or runnable implementations
- you want exhaustive, in-depth coverage rather than a summary
- you need up-to-date research beyond the course syllabus

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, llm-training, prompt-engineering, rag
- domain: large-language-models, tutorials, education, artificial-intelligence
- platform: cross-platform
- tags: cheatsheet, transformers, study-guide, stanford, course-notes, pdf

## Member repositories
- afshinea/stanford-cme-295-transformers-large-language-models (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:55.675309+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 2026-08-29T18:19:28.394789+00:00, confidence not recorded.
  - readme: https://github.com/afshinea/stanford-cme-295-transformers-large-language-models (fetched 2026-08-28T04:08:55.675309+00:00, sha df6a462adfbf)
- Data as of 2026-08-30T08:39:29.467469+00:00.