RRepoGEO

REPOGEO REPORT · LITE

zai-org/GLM-130B

Default branch main · commit 212215c5 · scanned 5/19/2026, 9:13:04 AM

GitHub: 7,651 stars · 601 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 zai-org/GLM-130B, 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
    ["large-language-model", "llm", "bilingual", "english-chinese", "deep-learning", "nlp", "transformer", "pre-trained-model", "efficient-inference", "gpu-optimization"]
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    http://keg.cs.tsinghua.edu.cn/glm-130b/posts/glm-130b/
  • lowabout#3
    Enhance the repository description for clarity and keywords

    Why:

    CURRENT
    GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
    COPY-PASTE FIX
    GLM-130B: An open, bilingual (English & Chinese) 130B parameter LLM with efficient inference on consumer GPUs (ICLR 2023).

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 zai-org/GLM-130B
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Qwen
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Qwen · recommended 1×
  2. Baichuan2 · recommended 1×
  3. ChatGLM3 · recommended 1×
  4. Yi · recommended 1×
  5. Llama 2 · recommended 1×
  • CATEGORY QUERY
    What are good open-source large language models for bilingual English and Chinese applications?
    you: not recommended
    AI recommended (in order):
    1. Qwen
    2. Baichuan2
    3. ChatGLM3
    4. Yi
    5. Llama 2
    6. BLOOMZ & BLOOM

    AI recommended 6 alternatives but never named zai-org/GLM-130B. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I run a massive pre-trained language model with 100+ billion parameters efficiently?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/DeepSpeed)
    2. Megatron-LM (NVIDIA/Megatron-LM)
    3. vLLM (vllm-project/vllm)
    4. Hugging Face Accelerate (huggingface/accelerate)
    5. bitsandbytes (TimDettmers/bitsandbytes)
    6. Ray (ray-project/ray)
    7. Ray Train
    8. Ray Core
    9. Open MPI
    10. NCCL (NVIDIA/nccl)
    11. TensorRT-LLM (NVIDIA/TensorRT-LLM)

    AI recommended 11 alternatives but never named zai-org/GLM-130B. 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 zai-org/GLM-130B?
    pass
    AI named zai-org/GLM-130B explicitly

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

  • If a team adopts zai-org/GLM-130B in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zai-org/GLM-130B 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 zai-org/GLM-130B solve, and who is the primary audience?
    pass
    AI named zai-org/GLM-130B explicitly

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

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zai-org/GLM-130B — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite