RRepoGEO

REPOGEO REPORT · LITE

BICLab/SpikingBrain-7B

Default branch main · commit ef999871 · scanned 6/25/2026, 1:38:55 PM

GitHub: 1,329 stars · 190 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
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 BICLab/SpikingBrain-7B, 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 for improved categorization

    Why:

    COPY-PASTE FIX
    spiking-neural-networks, large-language-models, llm, brain-inspired-ai, moe, efficient-attention, neuromorphic-computing, deep-learning
  • highreadme#2
    Reposition the README's "About" section to emphasize LLM identity

    Why:

    CURRENT
    Inspired by brain mechanisms, **SpikingBrain** integrates **hybrid efficient attention**, **MoE modules**, and **spike encoding** into its architecture, supported by a universal conversion pipeline compatible with the open-source model ecosystem.
    COPY-PASTE FIX
    **SpikingBrain** is a novel **Large Language Model (LLM)** inspired by brain mechanisms, integrating **hybrid efficient attention**, **MoE modules**, and **spike encoding** into its architecture. It is supported by a universal conversion pipeline compatible with the open-source model ecosystem.
  • mediumhomepage#3
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    https://your-project-homepage.com

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 BICLab/SpikingBrain-7B
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. FlashAttention-2 · recommended 1×
  3. DeepSpeed · recommended 1×
  4. Accelerate · recommended 1×
  5. Megatron-LM · recommended 1×
  • CATEGORY QUERY
    How to build large language models with efficient attention and Mixture of Experts for less data?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. FlashAttention-2
    3. DeepSpeed
    4. Accelerate
    5. Megatron-LM
    6. Apex
    7. Fairseq
    8. OpenMoE
    9. Colossal-AI
    10. Llama 2
    11. Mistral
    12. Falcon
    13. LoRA
    14. QLoRA
    15. Hugging Face PEFT library

    AI recommended 15 alternatives but never named BICLab/SpikingBrain-7B. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks enable brain-inspired large models using spike encoding for improved efficiency?
    you: not recommended
    AI recommended (in order):
    1. SpiNNaker
    2. Brian2
    3. Nengo
    4. Intel Loihi
    5. Lava
    6. NEST
    7. SNNA
    8. IBM TrueNorth

    AI recommended 8 alternatives but never named BICLab/SpikingBrain-7B. 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 BICLab/SpikingBrain-7B?
    pass
    AI named BICLab/SpikingBrain-7B explicitly

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

  • If a team adopts BICLab/SpikingBrain-7B in production, what risks or prerequisites should they evaluate first?
    pass
    AI named BICLab/SpikingBrain-7B 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 BICLab/SpikingBrain-7B solve, and who is the primary audience?
    pass
    AI did not name BICLab/SpikingBrain-7B — 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?

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BICLab/SpikingBrain-7B — 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