RRepoGEO

REPOGEO REPORT · LITE

yanqiangmiffy/InstructGLM

Default branch master · commit 163d6c4e · scanned 6/11/2026, 12:13:01 PM

GitHub: 651 stars · 49 forks

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 yanqiangmiffy/InstructGLM, 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 specific topics to the repository

    Why:

    COPY-PASTE FIX
    chatglm, lora, instruction-tuning, chinese-nlp, large-language-models, fine-tuning, deepspeed
  • highreadme#2
    Reposition the README's main heading to clearly state the project's focus

    Why:

    CURRENT
    # InstructGLM

> 基于ChatGLM-6B+LoRA在指令数据集上进行微调
    COPY-PASTE FIX
    # InstructGLM: LoRA-based Instruction Tuning for ChatGLM-6B with Chinese Datasets
  • mediumhomepage#3
    Add the repository URL as the project homepage

    Why:

    COPY-PASTE FIX
    https://github.com/yanqiangmiffy/InstructGLM

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 yanqiangmiffy/InstructGLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 2×
  2. PyTorch FSDP · recommended 2×
  3. LoRA · recommended 1×
  4. Hugging Face transformers · recommended 1×
  5. Hugging Face peft · recommended 1×
  • CATEGORY QUERY
    What are the best methods for instruction tuning a large language model using Chinese datasets?
    you: not recommended
    AI recommended (in order):
    1. LoRA
    2. Hugging Face transformers
    3. Hugging Face peft
    4. bitsandbytes
    5. DeepSpeed
    6. PyTorch FSDP
    7. QLoRA
    8. P-tuning v2
    9. Prompt Tuning
    10. Belle
    11. Firefly
    12. COIG
    13. C-Eval
    14. DeepL API
    15. Google Cloud Translation API
    16. Baidu Translate API
    17. OpenAI API
    18. Anthropic Claude API
    19. Baidu ERNIE Bot API
    20. Aliyun Tongyi Qianwen API
    21. RLHF
    22. DPO
    23. Hugging Face trl
    24. Argilla
    25. Prodigy

    AI recommended 25 alternatives but never named yanqiangmiffy/InstructGLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I efficiently fine-tune a large language model with diverse instruction-following data?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT
    3. Accelerate
    4. DeepSpeed
    5. PyTorch FSDP
    6. Unsloth
    7. Triton Inference Server
    8. Weights & Biases
    9. MLflow

    AI recommended 9 alternatives but never named yanqiangmiffy/InstructGLM. 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 yanqiangmiffy/InstructGLM?
    pass
    AI named yanqiangmiffy/InstructGLM explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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yanqiangmiffy/InstructGLM — 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