REPOGEO REPORT · LITE

minimaxir/gpt-2-simple

Default branch master · commit 91d11ed8 · scanned 6/21/2026, 10:26:51 PM

GitHub: 3,400 stars · 669 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

28 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface minimaxir/gpt-2-simple, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README's value proposition to clarify its niche

Why:

CURRENT

Note: Development on gpt-2-simple has mostly been superceded by aitextgen, which has similar AI text generation capabilities with more efficient training time and resource usage. If you do not require using TensorFlow, I recommend using aitextgen instead. Checkpoints trained using gpt-2-simple can be loaded using aitextgen as well.

COPY-PASTE FIX

Note: While development on `gpt-2-simple` has largely shifted to `aitextgen` for more efficient and general AI text generation, `gpt-2-simple` remains a straightforward Python package for fine-tuning and generating text with OpenAI's original GPT-2 models (124M/355M) within a TensorFlow ecosystem, and for compatibility with existing `gpt-2-simple` checkpoints.

mediumhomepage#2

Add a homepage URL to the repository metadata

Why:

COPY-PASTE FIX

https://colab.research.google.com/github/minimaxir/gpt-2-simple/blob/master/gpt2_simple_example.ipynb

mediumreadme#3

Clarify the project's license in the README

Why:

COPY-PASTE FIX

## License

The `LICENSE` file contains the terms under which `gpt-2-simple` is distributed. It incorporates components released under the MIT License, including OpenAI's official GPT-2 repo, Neil Shepperd's GPT-2 fork, and `textgenrnn`.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface minimaxir/gpt-2-simple

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 2×
PyTorch Lightning · recommended 2×
Ludwig · recommended 1×
KerasNLP · recommended 1×
OpenAI API · recommended 1×

CATEGORY QUERY
How to easily fine-tune a pre-trained language model for custom text generation?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. Ludwig
3. KerasNLP
4. PyTorch Lightning
5. OpenAI API
6. Fast.ai
AI recommended 6 alternatives but never named minimaxir/gpt-2-simple. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are simple Python libraries for training and generating text with large language models?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. Keras
3. PyTorch Lightning
4. OpenAI Python Library
5. LangChain
6. Lit-GPT
AI recommended 6 alternatives but never named minimaxir/gpt-2-simple. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
warn
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of minimaxir/gpt-2-simple?
pass
AI did not name minimaxir/gpt-2-simple — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts minimaxir/gpt-2-simple in production, what risks or prerequisites should they evaluate first?
pass
AI named minimaxir/gpt-2-simple explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo minimaxir/gpt-2-simple solve, and who is the primary audience?
pass
AI named minimaxir/gpt-2-simple explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of minimaxir/gpt-2-simple. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/minimaxir/gpt-2-simple.svg)](https://repogeo.com/en/r/minimaxir/gpt-2-simple)

HTML

<a href="https://repogeo.com/en/r/minimaxir/gpt-2-simple"><img src="https://repogeo.com/badge/minimaxir/gpt-2-simple.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

minimaxir/gpt-2-simple — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite