RRepoGEO

REPOGEO REPORT · LITE

vivo-ai-lab/BlueLM

Default branch main · commit afd48cab · scanned 6/9/2026, 8:02:03 PM

GitHub: 941 stars · 73 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 vivo-ai-lab/BlueLM, 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
    Add a concise, feature-rich English introduction to README_EN.md

    Why:

    COPY-PASTE FIX
    Add this sentence near the top of `README_EN.md`: "BlueLM is an open-source large language model developed by vivo AI Lab, offering 7B and 32K long-context models. It features robust function calling capabilities and provides an OpenAI-compatible API for seamless integration into your AI applications."
  • mediumlicense#2
    Clarify the '开放原子模型许可证' in the README

    Why:

    CURRENT
    协议说明**：B
    COPY-PASTE FIX
    In the "声明、协议、引用" section (or its English equivalent), add a clear statement: "This project is licensed under the OpenAtom Model License. Please refer to the `OpenAtom Model License.pdf` for the complete terms and conditions."

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 vivo-ai-lab/BlueLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Llama 3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Llama 3 · recommended 2×
  2. Mixtral 8x7B · recommended 1×
  3. Gemma · recommended 1×
  4. Mistral 7B · recommended 1×
  5. Falcon · recommended 1×
  • CATEGORY QUERY
    Looking for open-source large language models for general AI application development.
    you: not recommended
    AI recommended (in order):
    1. Llama 3
    2. Mixtral 8x7B
    3. Gemma
    4. Mistral 7B
    5. Falcon
    6. Vicuna

    AI recommended 6 alternatives but never named vivo-ai-lab/BlueLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some accessible large language models supporting function calling and OpenAI API?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4
    2. OpenAI GPT-3.5 Turbo
    3. Anthropic Claude 3
    4. Google Gemini
    5. Mistral Large / Mistral Medium
    6. Cohere Command R / Command R+
    7. Llama 3

    AI recommended 7 alternatives but never named vivo-ai-lab/BlueLM. 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 vivo-ai-lab/BlueLM?
    pass
    AI named vivo-ai-lab/BlueLM explicitly

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

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

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

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vivo-ai-lab/BlueLM — 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