RRepoGEO

REPOGEO REPORT · LITE

huggingface/smollm

Default branch main · commit a0417598 · scanned 5/21/2026, 1:07:50 PM

GitHub: 3,782 stars · 293 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
35 /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
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 huggingface/smollm, 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 H1 to explicitly state the core use case

    Why:

    CURRENT
    # Smol Models 🤏

Welcome to Smol Models, a family of efficient and lightweight AI models from Hugging Face.
    COPY-PASTE FIX
    # Smol Models 🤏: Efficient On-Device AI for Text and Vision

Welcome to Smol Models, a family of efficient and lightweight AI models from Hugging Face designed for high performance on resource-constrained devices.
  • mediumabout#2
    Enhance the repository description for clarity and keyword density

    Why:

    CURRENT
    Everything about the SmolLM and SmolVLM family of models
    COPY-PASTE FIX
    The official repository for SmolLM and SmolVLM, a family of efficient, lightweight AI models optimized for on-device and edge inference for text and vision tasks.

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/smollm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Gemma 2B
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Gemma 2B · recommended 1×
  2. Microsoft Phi-3-mini · recommended 1×
  3. Meta Llama 3 8B · recommended 1×
  4. Mistral 7B · recommended 1×
  5. TinyLlama 1.1B · recommended 1×
  • CATEGORY QUERY
    What are the best compact language models for efficient on-device inference with strong performance?
    you: not recommended
    AI recommended (in order):
    1. Google Gemma 2B
    2. Microsoft Phi-3-mini
    3. Meta Llama 3 8B
    4. Mistral 7B
    5. TinyLlama 1.1B
    6. OpenAI GPT-3.5 Turbo

    AI recommended 6 alternatives but never named huggingface/smollm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which efficient multimodal models support on-device visual QA and image description across multiple languages?
    you: not recommended
    AI recommended (in order):
    1. Gemini Nano
    2. LLaVA
    3. Core ML
    4. Vision Framework
    5. OpenVINO
    6. TensorFlow Lite

    AI recommended 6 alternatives but never named huggingface/smollm. 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/smollm?
    pass
    AI named huggingface/smollm explicitly

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

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

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

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huggingface/smollm — 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
huggingface/smollm — RepoGEO report