RRepoGEO

REPOGEO REPORT · LITE

wdndev/tiny-llm-zh

Default branch main · commit 667fd773 · scanned 5/10/2026, 8:12:45 AM

GitHub: 1,026 stars · 116 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 wdndev/tiny-llm-zh, 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
    Clarify README's opening to emphasize 'end-to-end learning project'

    Why:

    CURRENT
    # Tiny LLM zh

## 1.简介

本项目旨在构建一个小参数量的中文语言大模型，用于快速入门学习大模型相关知识，如果此项目对你有用，可以点一下start，谢谢！
    COPY-PASTE FIX
    # Tiny LLM zh: 从零实现小参数量中文大语言模型 (端到端学习项目)

## 1.简介

本项目旨在构建一个**端到端的小参数量中文语言大模型实现与学习项目**，用于快速入门学习大模型相关知识，如果此项目对你有用，可以点一下start，谢谢！
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of the Apache-2.0 License.

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 wdndev/tiny-llm-zh
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. Hugging Face Datasets · recommended 1×
  3. PyTorch · recommended 1×
  4. PyTorch Lightning · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    How can I implement a small parameter Chinese language model covering the full development lifecycle?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Datasets
    3. PyTorch
    4. PyTorch Lightning
    5. TensorFlow
    6. Keras
    7. OpenNMT
    8. FastText
    9. PaddlePaddle
    10. PaddleNLP

    AI recommended 10 alternatives but never named wdndev/tiny-llm-zh. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools are available for building compact Chinese LLMs, supporting MoE and deepspeed training?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. DeepSpeed (microsoft/DeepSpeed)
    3. Megatron-LM (NVIDIA/Megatron-LM)
    4. Colossal-AI (hpcaitech/ColossalAI)
    5. PaddlePaddle (PaddlePaddle/Paddle)

    AI recommended 5 alternatives but never named wdndev/tiny-llm-zh. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 wdndev/tiny-llm-zh?
    pass
    AI named wdndev/tiny-llm-zh explicitly

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

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