RRepoGEO

REPOGEO REPORT · LITE

unslothai/notebooks

Default branch main · commit ff0685ab · scanned 5/25/2026, 7:58:55 PM

GitHub: 5,383 stars · 883 forks

AI VISIBILITY SCORE
40 /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
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 unslothai/notebooks, 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
    Expand repository topics to improve categorization

    Why:

    CURRENT
    unsloth
    COPY-PASTE FIX
    unsloth, fine-tuning, notebooks, llm, ai-models, machine-learning, deep-learning, text-generation, computer-vision, audio-processing, rlhf, pytorch, tensorflow, huggingface
  • mediumreadme#2
    Add a concise introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    Add this paragraph directly after the initial logo/badge block and before the '## 📒 Fine-tuning Notebooks' heading:

"This repository provides over 250 practical Colab notebooks for fine-tuning and Reinforcement Learning (RL) across various AI models, including text, vision, audio, embedding, and TTS. Each notebook features data preparation, training, and inference steps, optimized for efficiency with Unsloth."
  • lowabout#3
    Enhance the repository description to emphasize practicality

    Why:

    CURRENT
    250+ Fine-tuning & RL Notebooks for text, vision, audio, embedding, TTS models.
    COPY-PASTE FIX
    250+ practical & ready-to-use Fine-tuning & RL Notebooks for text, vision, audio, embedding, TTS models.

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 unslothai/notebooks
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. Kaggle · recommended 1×
  3. PyTorch · recommended 1×
  4. TensorFlow · recommended 1×
  5. GitHub · recommended 1×
  • CATEGORY QUERY
    Where can I find practical notebooks for fine-tuning various AI models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Kaggle
    3. PyTorch
    4. TensorFlow
    5. GitHub
    6. Google Colaboratory
    7. Papers With Code

    AI recommended 7 alternatives but never named unslothai/notebooks. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for examples and guides to fine-tune text, vision, and audio models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers & Datasets
    2. PyTorch Lightning
    3. Keras (with TensorFlow)
    4. fast.ai
    5. TensorFlow Hub
    6. OpenAI's Fine-tuning API
    7. SpeechBrain

    AI recommended 7 alternatives but never named unslothai/notebooks. 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 unslothai/notebooks?
    pass
    AI named unslothai/notebooks explicitly

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

  • If a team adopts unslothai/notebooks in production, what risks or prerequisites should they evaluate first?
    pass
    AI named unslothai/notebooks 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 unslothai/notebooks solve, and who is the primary audience?
    pass
    AI named unslothai/notebooks 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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unslothai/notebooks — 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