RRepoGEO

REPOGEO REPORT · LITE

deepseek-ai/DeepSeek-VL2

Default branch main · commit ef9f91e2 · scanned 6/26/2026, 10:33:11 AM

GitHub: 5,308 stars · 1,811 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)

3 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 deepseek-ai/DeepSeek-VL2, 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 the repository

    Why:

    COPY-PASTE FIX
    vision-language-model, multimodal-ai, large-language-model, deep-learning, ai-models, mixture-of-experts, vlm
  • mediumhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://huggingface.co/spaces/deepseek-ai/deepseek-vl2-small
  • mediumreadme#3
    Add a concise project summary at the top of the README

    Why:

    COPY-PASTE FIX
    DeepSeek-VL2 is an advanced series of open-source Mixture-of-Experts Vision-Language Models (VLMs) designed for state-of-the-art multimodal understanding and reasoning across images and text.

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 deepseek-ai/DeepSeek-VL2
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LLaVA
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LLaVA · recommended 1×
  2. CogVLM · recommended 1×
  3. Fuyu-8B · recommended 1×
  4. MiniGPT-4 / MiniGPT-v2 · recommended 1×
  5. BLIP-2 · recommended 1×
  • CATEGORY QUERY
    What are the best open-source multimodal models for complex image and text comprehension?
    you: not recommended
    AI recommended (in order):
    1. LLaVA
    2. CogVLM
    3. Fuyu-8B
    4. MiniGPT-4 / MiniGPT-v2
    5. BLIP-2
    6. InstructBLIP

    AI recommended 6 alternatives but never named deepseek-ai/DeepSeek-VL2. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I integrate efficient vision and language models for advanced multimodal reasoning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT
    3. LoRA
    4. AdaLoRA
    5. QLoRA
    6. PyTorch Lightning
    7. TorchMetrics
    8. OpenMMLab
    9. MMCV
    10. MMEngine
    11. MMPreTrain
    12. MMDetection
    13. MMSegmentation
    14. MMOCR
    15. MMEval
    16. DeepSpeed
    17. Accelerate
    18. JAX
    19. Flax
    20. Optax
    21. TensorFlow
    22. Keras
    23. TensorFlow Extended

    AI recommended 23 alternatives but never named deepseek-ai/DeepSeek-VL2. 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 deepseek-ai/DeepSeek-VL2?
    pass
    AI named deepseek-ai/DeepSeek-VL2 explicitly

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

  • If a team adopts deepseek-ai/DeepSeek-VL2 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named deepseek-ai/DeepSeek-VL2 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 deepseek-ai/DeepSeek-VL2 solve, and who is the primary audience?
    pass
    AI named deepseek-ai/DeepSeek-VL2 explicitly

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

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deepseek-ai/DeepSeek-VL2 — 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