# tanishqkumar/beyond-nanogpt

Minimal and annotated implementations of key ideas from modern deep learning research.

Repository: https://github.com/tanishqkumar/beyond-nanogpt
Canonical: https://ross.abutalabs.com/products/beyond-nanogpt
Language: Python
License: MIT
License Family: permissive
Last push: 2026-01-29T07:09:42+00:00

## Health v2 (maintenance only)
Score: 48/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 64, release rhythm 35, longevity 36
- inputs: {"age_days": 504, "days_push": 216, "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 1340, forks 112 (observed 2026-08-28T04:04:26.080375+00:00)

## What it is
An educational repository of minimal, annotated, from-scratch implementations of ~100 modern deep learning techniques, bridging nanoGPT and research-level work. It covers LLM techniques, attention variants, generative models, reinforcement learning, and GPU systems fundamentals, all runnable on a single GPU.

## Use cases
- learn how transformers and LLMs work from scratch
- understand KV caching and speculative decoding implementations
- study diffusion models and flow matching with minimal code
- learn reinforcement learning algorithms like PPO from scratch
- prepare to do deep learning research after learning basics
- understand tensor parallelism and GPU communication
- implement attention variants like linear attention myself

## When to choose
- you want annotated, from-scratch implementations to learn deep learning deeply
- you're moving from beginner LLM tutorials toward research-level understanding
- you want single-GPU runnable reference code for modern techniques
- you prefer reading and hacking on minimal code over heavy frameworks

## When to avoid
- you need production-ready, optimized model implementations
- you want a maintained library or framework to build applications on
- you need multi-node distributed training support
- you're looking for a plug-and-play model zoo

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, llm-training, reinforcement-learning, llm-inference, stable-diffusion, gpu-computing
- domain: deep-learning, large-language-models, machine-learning, education, tutorials
- platform: python, cross-platform
- tags: educational, from-scratch-implementations, nanogpt, annotated-code, transformers, diffusion-models, research-education, gpu

## Member repositories
- tanishqkumar/beyond-nanogpt (main) score 48

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:26.080375+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 2026-08-30T04:43:52.946510+00:00, confidence not recorded.
  - readme: https://github.com/tanishqkumar/beyond-nanogpt (fetched 2026-08-28T04:04:26.080375+00:00, sha ec3f4d706ef4)
- Data as of 2026-08-30T08:39:29.467469+00:00.