RRepoGEO

REPOGEO REPORT · LITE

ConnorJL/GPT2

Default branch master · commit 936fe2a2 · scanned 5/23/2026, 5:08:01 PM

GitHub: 1,413 stars · 328 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
35 /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
3 / 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 ConnorJL/GPT2, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    gpt2, transformer, large-language-model, llm, deep-learning, tensorflow, tpu, gpu, text-generation, machine-learning
  • highreadme#2
    Clarify the README's opening statement to emphasize its role as a GPT-2 implementation for research/learning

    Why:

    CURRENT
    # GPT2
**Disclaimer: This is not the official GPT2 implementation! I've done my best to follow the specifications of the original GPT2 model as closely as possible, but be warned that I have not been able to replicate the full performance of the original model using this code. I don't know why this is, I haven't been able to track down any bug that could be causing this.**

An implementation of training for GPT2 that supports both GPUs and TPUs.
    COPY-PASTE FIX
    # GPT2: A Research-Focused Implementation for Training on GPUs and TPUs

This repository provides a clean, educational implementation of the GPT-2 model, designed for researchers and practitioners interested in understanding and training transformer-based text generation models on both GPUs and TPUs. While not the official OpenAI release, this project aims to closely follow the original GPT-2 specifications.
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://github.com/ConnorJL/GPT2

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 ConnorJL/GPT2
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud TPUs
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud TPUs · recommended 1×
  2. JAX · recommended 1×
  3. Flax · recommended 1×
  4. NVIDIA GPUs · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    How can I train a large language model efficiently using TPUs or GPUs?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud TPUs
    2. JAX
    3. Flax
    4. NVIDIA GPUs
    5. PyTorch
    6. PyTorch FSDP
    7. Microsoft DeepSpeed
    8. TensorFlow
    9. tf.distribute API
    10. Horovod
    11. Hugging Face Accelerate
    12. Megatron-LM

    AI recommended 12 alternatives but never named ConnorJL/GPT2. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an open-source implementation to train a transformer-based text generation model.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch-Lightning
    3. DeepSpeed
    4. fairseq
    5. TensorFlow Text

    AI recommended 5 alternatives but never named ConnorJL/GPT2. 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 ConnorJL/GPT2?
    pass
    AI named ConnorJL/GPT2 explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts ConnorJL/GPT2 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ConnorJL/GPT2 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 ConnorJL/GPT2 solve, and who is the primary audience?
    pass
    AI named ConnorJL/GPT2 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 ConnorJL/GPT2. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/ConnorJL/GPT2.svg)](https://repogeo.com/en/r/ConnorJL/GPT2)
HTML
<a href="https://repogeo.com/en/r/ConnorJL/GPT2"><img src="https://repogeo.com/badge/ConnorJL/GPT2.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

ConnorJL/GPT2 — 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