# openai/grok

Repository: https://github.com/openai/grok
Canonical: https://ross.abutalabs.com/products/grok
Language: Python
License: MIT
License Family: permissive
Archived: true
Last push: 2024-03-19T07:03:15+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1969, "days_push": 897, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4263, forks 587 (observed 2026-08-28T04:08:40.668782+00:00)

## What it is
Research code accompanying the paper 'Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets' from OpenAI. It provides training scripts to reproduce grokking curve experiments on small algorithmic datasets.

## Use cases
- reproduce grokking experiments from the paper
- study delayed generalization in neural networks
- train models on small algorithmic datasets
- explore overfitting vs generalization dynamics
- research deep learning generalization phenomena

## When to choose
- you want to reproduce or extend the grokking paper's experiments
- you are researching generalization dynamics in small-scale neural network training

## When to avoid
- you need a production machine learning framework
- you want a maintained tool with active releases
- you need training on large real-world datasets

## Facets
- artifact type: library
- maturity: experimental
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning
- platform: python
- tags: grokking, research-code, generalization, overfitting, academic-paper, research

## Member repositories
- openai/grok (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:40.668782+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 2026-08-29T18:22:04.633731+00:00, confidence not recorded.
  - readme: https://github.com/openai/grok (fetched 2026-08-28T04:08:40.668782+00:00, sha 019344cffbdd)
- Data as of 2026-08-30T08:39:29.467469+00:00.