RRepoGEO

REPOGEO REPORT · LITE

huggingface/smol-course

Default branch main · commit 32dde01a · scanned 6/20/2026, 1:58:39 PM

GitHub: 6,661 stars · 2,280 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 huggingface/smol-course, 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 for discoverability

    Why:

    COPY-PASTE FIX
    course, language-models, llm, vlm, fine-tuning, alignment, local-llm, budget-friendly, practical-guide
  • highreadme#2
    Clarify README H1 and opening sentence

    Why:

    CURRENT
    # a smol course

This is a practical course on aligning language models for your specific use case.
    COPY-PASTE FIX
    # The Smol Course: Practical Alignment for Local LLMs

This practical, hands-on course teaches you to align language models for your specific use case, designed to run efficiently on most local machines with minimal GPU requirements.
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://huggingface.co/smol-course

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 huggingface/smol-course
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. PEFT · recommended 1×
  3. Axolotl · recommended 1×
  4. QLoRA · recommended 1×
  5. Lit-GPT · recommended 1×
  • CATEGORY QUERY
    How to learn aligning language models for specific use cases locally?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT
    3. Axolotl
    4. QLoRA
    5. Lit-GPT
    6. Ollama
    7. Ludwig

    AI recommended 7 alternatives but never named huggingface/smol-course. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good introductory courses for fine-tuning small language models on a budget?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face's "NLP Course"
    2. Transformers library (huggingface/transformers)
    3. Datasets library (huggingface/datasets)
    4. Accelerate (huggingface/accelerate)
    5. Google Colab
    6. DeepLearning.AI's "Generative AI with Transformers"
    7. Coursera
    8. fast.ai's "Practical Deep Learning for Coders"
    9. fastai library (fastai/fastai)
    10. Udemy's "NLP - Natural Language Processing with Python"
    11. Udemy
    12. spaCy (explosion/spaCy)
    13. NLTK (nltk/nltk)
    14. Google's "Introduction to Generative AI"
    15. Google Cloud Skills Boost
    16. Google's AI tools

    AI recommended 16 alternatives but never named huggingface/smol-course. 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 huggingface/smol-course?
    pass
    AI did not name huggingface/smol-course — 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 huggingface/smol-course in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huggingface/smol-course 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 huggingface/smol-course solve, and who is the primary audience?
    pass
    AI named huggingface/smol-course explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
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