RRepoGEO

REPOGEO REPORT · LITE

uclaml/SPIN

Default branch main · commit a12ba808 · scanned 5/12/2026, 9:33:25 AM

GitHub: 1,240 stars · 105 forks

AI VISIBILITY SCORE
27 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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 uclaml/SPIN, 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 README H1 to clarify project type and purpose

    Why:

    CURRENT
    # Self-Play Fine-Tuning (SPIN)
    COPY-PASTE FIX
    # SPIN: A Self-Play Fine-Tuning Framework for Large Language Models
  • mediumreadme#2
    Add a concise problem/solution statement to the README introduction

    Why:

    COPY-PASTE FIX
    SPIN provides a robust and efficient method to enhance the capabilities of weak language models by leveraging self-play, enabling them to achieve performance comparable to stronger models through iterative fine-tuning.
  • mediumtopics#3
    Add more specific topics related to LLM training and alignment

    Why:

    CURRENT
    deep-learning, fine-tuning, large-language-models, self-play
    COPY-PASTE FIX
    deep-learning, fine-tuning, large-language-models, self-play, llm-training, model-alignment, reinforcement-learning-from-human-feedback, rlhf, generative-ai

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 uclaml/SPIN
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. pytorch/pytorch · recommended 2×
  3. tensorflow/tensorflow · recommended 2×
  4. huggingface/datasets · recommended 1×
  5. huggingface/peft · recommended 1×
  • CATEGORY QUERY
    How to improve the performance of a weak large language model?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face Datasets (huggingface/datasets)
    3. PyTorch (pytorch/pytorch)
    4. TensorFlow (tensorflow/tensorflow)
    5. Hugging Face PEFT (huggingface/peft)
    6. NL-Augmenter (GEM-benchmark/NL-Augmenter)
    7. TextAttack (TextAttack/TextAttack)
    8. OpenAI Playground/API
    9. LangChain (langchain-ai/langchain)
    10. LlamaIndex (run-llama/llama_index)
    11. Hugging Face Optimum (huggingface/optimum)

    AI recommended 11 alternatives but never named uclaml/SPIN. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective techniques for self-improving large language models through iterative training?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch (pytorch/pytorch)
    3. TensorFlow (tensorflow/tensorflow)
    4. Scale AI
    5. Surge AI
    6. GPT-4
    7. Claude
    8. Claude 3
    9. Gemini
    10. Llama 3
    11. NLPAug (makcedward/nlpaug)
    12. Label Studio (heartexlabs/label-studio)
    13. Argilla (argilla-io/argilla)
    14. Hugging Face TRL (huggingface/trl)

    AI recommended 14 alternatives but never named uclaml/SPIN. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 uclaml/SPIN?
    pass
    AI did not name uclaml/SPIN — 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 uclaml/SPIN in production, what risks or prerequisites should they evaluate first?
    pass
    AI named uclaml/SPIN 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 uclaml/SPIN solve, and who is the primary audience?
    pass
    AI did not name uclaml/SPIN — 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?

Embed your GEO score

Drop this badge into the README of uclaml/SPIN. 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/uclaml/SPIN.svg)](https://repogeo.com/en/r/uclaml/SPIN)
HTML
<a href="https://repogeo.com/en/r/uclaml/SPIN"><img src="https://repogeo.com/badge/uclaml/SPIN.svg" alt="RepoGEO" /></a>
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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite