RRepoGEO

REPOGEO REPORT · LITE

ShaohonChen/Qwen3-SmVL

Default branch main · commit 872393c9 · scanned 6/15/2026, 10:03:39 PM

GitHub: 590 stars · 56 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 ShaohonChen/Qwen3-SmVL, 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 to improve categorization

    Why:

    COPY-PASTE FIX
    ["multimodal-llm", "vision-language-model", "qwen", "smolvlm", "chinese-llm", "fine-tuning", "small-model", "gpu-inference", "model-splicing"]
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    (Choose an appropriate open-source license, e.g., MIT or Apache-2.0, and add it as LICENSE or LICENSE.md in the repository root.)
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://swanlab.cn/@ShaohonChen/Qwen3-SmVL/overview

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 ShaohonChen/Qwen3-SmVL
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
salesforce/BLIP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. salesforce/BLIP · recommended 2×
  2. microsoft/GIT · recommended 2×
  3. pytorch/pytorch · recommended 2×
  4. MiniGPT-4 · recommended 1×
  5. BLIP-2 · recommended 1×
  • CATEGORY QUERY
    How to add visual understanding capabilities to a small Chinese language model?
    you: not recommended
    AI recommended (in order):
    1. MiniGPT-4
    2. BLIP-2
    3. LLaVA
    4. OpenCLIP
    5. Hugging Face Transformers Library
    6. ERNIE-ViLG

    AI recommended 6 alternatives but never named ShaohonChen/Qwen3-SmVL. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for approaches to integrate compact vision modules with existing small language models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. facebook/dinov2-small (facebookresearch/dinov2)
    3. google/vit-base-patch16-224 (google-research/vision_transformer)
    4. microsoft/beit-base-patch16-224-pt22k (microsoft/beit)
    5. google/mobilenet_v2_1.0_224
    6. facebook/opt-125m (facebookresearch/metaseq)
    7. google/gemma-2b (google/gemma)
    8. microsoft/phi-2
    9. HuggingFaceH4/zephyr-7b-beta (HuggingFaceH4/zephyr)
    10. PyTorch Image Models (timm) (rwightman/pytorch-image-models)
    11. resnet18
    12. mobilenetv3_large_100
    13. efficientnet_b0
    14. swin_tiny_patch4_window7_224 (microsoft/swin-transformer)
    15. openai/clip-vit-base-patch32 (openai/CLIP)
    16. Salesforce/blip-vqa-base (salesforce/BLIP)
    17. microsoft/git-base (microsoft/GIT)
    18. PyTorch (pytorch/pytorch)
    19. TensorFlow (tensorflow/tensorflow)
    20. Salesforce/blip-image-captioning-base (salesforce/BLIP)
    21. microsoft/git-base-coco (microsoft/GIT)
    22. Hugging Face Optimum (huggingface/optimum)
    23. bitsandbytes (TimDettmers/bitsandbytes)
    24. NNCF (openvinotoolkit/nncf)
    25. PyTorch (native quantization) (pytorch/pytorch)

    AI recommended 25 alternatives but never named ShaohonChen/Qwen3-SmVL. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 ShaohonChen/Qwen3-SmVL?
    pass
    AI did not name ShaohonChen/Qwen3-SmVL — 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 ShaohonChen/Qwen3-SmVL in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ShaohonChen/Qwen3-SmVL 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 ShaohonChen/Qwen3-SmVL solve, and who is the primary audience?
    pass
    AI named ShaohonChen/Qwen3-SmVL explicitly

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

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ShaohonChen/Qwen3-SmVL — 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