RRepoGEO

REPOGEO REPORT · LITE

AILab-CVC/SEED

Default branch main · commit 93b3cf40 · scanned 6/14/2026, 5:38:09 AM

GitHub: 641 stars · 33 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 AILab-CVC/SEED, 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
  • highreadme#1
    Reposition the README's opening to clarify SEED as a multimodal LLM

    Why:

    CURRENT
    # :chestnut: SEED Multimodal

... The repository provides the official implementation of SEED, SEED-LLaMA.
    COPY-PASTE FIX
    # :chestnut: SEED Multimodal: Official Implementation of SEED-LLaMA, a Multimodal Large Language Model

This repository provides the official implementation of SEED and SEED-LLaMA, focusing on advanced multimodal large language models for comprehensive vision-language understanding and generation. It includes robust training code for Multimodal LLM pretraining and instruction tuning, supporting large-scale multi-node training with efficient data pipelines.
  • mediumtopics#2
    Add more specific topics for LLMs and training

    Why:

    CURRENT
    foundation-model, multimodal, vision-language
    COPY-PASTE FIX
    foundation-model, multimodal, vision-language, large-language-model, llm-training, multimodal-llm
  • lowlicense#3
    Add a section to README clarifying the license

    Why:

    COPY-PASTE FIX
    ## License

This project is released under a custom license. Please refer to the [LICENSE](LICENSE) file in the repository for full details.

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 AILab-CVC/SEED
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. huggingface/diffusers · recommended 1×
  3. huggingface/peft · recommended 1×
  4. Lightning-AI/lightning · recommended 1×
  5. tensorflow/tensorflow · recommended 1×
  • CATEGORY QUERY
    How can I build a multimodal AI that understands both images and text effectively?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Transformers (huggingface/transformers)
    3. Diffusers (huggingface/diffusers)
    4. PEFT (huggingface/peft)
    5. PyTorch Lightning (Lightning-AI/lightning)
    6. TensorFlow (tensorflow/tensorflow)
    7. Keras (keras-team/keras)
    8. OpenAI CLIP (openai/CLIP)
    9. MMDetection (open-mmlab/mmdetection)
    10. MMDetection3D (open-mmlab/mmdetection3d)
    11. DeepSpeed (microsoft/DeepSpeed)
    12. FSDP (pytorch/pytorch)

    AI recommended 12 alternatives but never named AILab-CVC/SEED. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good options for training large-scale multimodal language models with efficient data pipelines?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning
    2. DeepSpeed
    3. Hugging Face Accelerate
    4. JAX
    5. Flax
    6. Orbax
    7. TensorFlow
    8. Keras
    9. tf.data
    10. NVIDIA DALI

    AI recommended 10 alternatives but never named AILab-CVC/SEED. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 AILab-CVC/SEED?
    pass
    AI named AILab-CVC/SEED explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite