RRepoGEO

REPOGEO REPORT · LITE

mshumer/gpt-llm-trainer

Default branch main · commit 6d5e046e · scanned 5/15/2026, 11:42:43 PM

GitHub: 4,166 stars · 554 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 mshumer/gpt-llm-trainer, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Automate dataset generation and fine-tuning of LLaMA 2, GPT-3.5, and Claude 3 models for custom tasks with a simple, no-code pipeline.
  • mediumreadme#2
    Add a concise positioning statement at the top of the README

    Why:

    CURRENT
    # gpt-llm-trainer
[](https://twitter.com/mattshumer_)

NEW: Claude 3 -> LLaMA 2 7B Fine-Tuning version: [](https://colab.research.google.com/drive/1eLe0t8Alu997w5Ewnw9mE96dtaC-qEho?usp=sharing)
    COPY-PASTE FIX
    # gpt-llm-trainer
An experimental pipeline to automate dataset generation and fine-tuning of LLaMA 2, GPT-3.5, and Claude 3 models. Go from idea to a high-performing, task-specific LLM with minimal effort and no coding required.

[](https://twitter.com/mattshumer_)

NEW: Claude 3 -> LLaMA 2 7B Fine-Tuning version: [](https://colab.research.google.com/drive/1eLe0t8Alu997w5Ewnw9mE96dtaC-qEho?usp=sharing)

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 mshumer/gpt-llm-trainer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. Snorkel · recommended 1×
  3. Argilla · recommended 1×
  4. Label Studio · recommended 1×
  5. Prodigy · recommended 1×
  • CATEGORY QUERY
    What are options for automating dataset creation and model fine-tuning for custom tasks?
    you: not recommended
    AI recommended (in order):
    1. Snorkel
    2. Argilla
    3. Label Studio
    4. Prodigy
    5. Cleanlab
    6. Hugging Face Transformers
    7. Hugging Face Datasets
    8. Google Cloud AutoML
    9. Amazon SageMaker Autopilot

    AI recommended 9 alternatives but never named mshumer/gpt-llm-trainer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I easily fine-tune an open-source large language model for a specific application?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT
    3. TRL
    4. LoRA
    5. QLoRA
    6. Ludwig
    7. Axolotl
    8. OpenAI's Fine-tuning API
    9. Lit-GPT
    10. DeepSpeed
    11. FSDP

    AI recommended 11 alternatives but never named mshumer/gpt-llm-trainer. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 mshumer/gpt-llm-trainer?
    pass
    AI named mshumer/gpt-llm-trainer explicitly

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

  • If a team adopts mshumer/gpt-llm-trainer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mshumer/gpt-llm-trainer 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 mshumer/gpt-llm-trainer solve, and who is the primary audience?
    pass
    AI named mshumer/gpt-llm-trainer explicitly

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

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
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