RRepoGEO

REPOGEO REPORT · LITE

01-ai/Yi-1.5

Default branch main · commit 0704ac06 · scanned 6/1/2026, 11:27:25 AM

GitHub: 559 stars · 33 forks

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 01-ai/Yi-1.5, 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
  • highhomepage#1
    Add a homepage URL to the repository About section

    Why:

    COPY-PASTE FIX
    https://01-ai.github.io/
  • mediumreadme#2
    Strengthen the README's introductory paragraph

    Why:

    CURRENT
    ## Intro
    
    Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. 
    
    Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. 
    
    Yi-1.5 comes in 3 model sizes: 34B, 9B, and 6B. For model details and benchmarks, see Model C
    COPY-PASTE FIX
    ## Intro
    
    Yi-1.5 is an upgraded version of Yi, delivering stronger performance in coding, math, reasoning, and instruction-following capability. Continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse samples, Yi-1.5 provides powerful large language models (LLMs) in 3 sizes (34B, 9B, 6B) for developers and researchers. It maintains excellent capabilities in language understanding, commonsense reasoning, and reading comprehension, making it ideal for applications requiring advanced instruction following. For model details and benchmarks, see Model C.

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 01-ai/Yi-1.5
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Code Llama
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Code Llama · recommended 1×
  2. Mistral 7B · recommended 1×
  3. Llama 2 · recommended 1×
  4. DeepSeek-Coder · recommended 1×
  5. Phi-2 · recommended 1×
  • CATEGORY QUERY
    What open-source large language models excel at coding, math, and complex reasoning tasks?
    you: not recommended
    AI recommended (in order):
    1. Code Llama
    2. Mistral 7B
    3. Llama 2
    4. DeepSeek-Coder
    5. Phi-2
    6. Falcon

    AI recommended 6 alternatives but never named 01-ai/Yi-1.5. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a powerful, continuously pre-trained language model for improved instruction following.
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Gemini 1.5 Pro
    4. Llama 3 70B Instruct (meta-llama/llama3)
    5. Mistral Large

    AI recommended 5 alternatives but never named 01-ai/Yi-1.5. 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 01-ai/Yi-1.5?
    pass
    AI did not name 01-ai/Yi-1.5 — 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 01-ai/Yi-1.5 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named 01-ai/Yi-1.5 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 01-ai/Yi-1.5 solve, and who is the primary audience?
    pass
    AI named 01-ai/Yi-1.5 explicitly

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

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01-ai/Yi-1.5 — 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