RRepoGEO

REPOGEO REPORT · LITE

huggingface/smol-course

Default branch main · commit 32dde01a · scanned 5/10/2026, 2:53:04 PM

GitHub: 6,639 stars · 2,288 forks

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

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

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's H1 and opening sentence to emphasize its course nature

    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 Small Language Models

This practical course teaches you how to align small language models (LLMs) and vision-language models (VLMs) for your specific use case, focusing on hands-on learning with minimal GPU requirements and no paid services.
  • mediumhomepage#2
    Add the course's main URL as the repository homepage

    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
pytorch/pytorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 2×
  2. huggingface/peft · recommended 1×
  3. OpenAccess-AI-Collective/axolotl · recommended 1×
  4. unslothai/unsloth · recommended 1×
  5. microsoft/DeepSpeed · recommended 1×
  • CATEGORY QUERY
    How to align small language models for specific use cases with minimal GPU requirements?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face `peft` library (huggingface/peft)
    2. Axolotl (OpenAccess-AI-Collective/axolotl)
    3. Unsloth (unslothai/unsloth)
    4. Microsoft DeepSpeed (microsoft/DeepSpeed)
    5. PyTorch FSDP (pytorch/pytorch)
    6. TinyLlama
    7. Phi-2
    8. Gemma 2B/7B
    9. Mistral 7B

    AI recommended 9 alternatives but never named huggingface/smol-course. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a practical course on fine-tuning LLMs for local machines?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face
    2. transformers (huggingface/transformers)
    3. fast.ai (fastai/fastai)
    4. PyTorch (pytorch/pytorch)
    5. Amazon SageMaker
    6. AWS

    AI recommended 6 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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