RRepoGEO

REPOGEO REPORT · LITE

wdndev/tiny-llm-zh

Default branch main · commit 667fd773 · scanned 6/20/2026, 6:43:02 AM

GitHub: 1,048 stars · 122 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
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

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README's opening to emphasize full pipeline implementation

    Why:

    CURRENT
    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...
    COPY-PASTE FIX
    本项目旨在从零开始，完整实现一个用于快速入门学习的小参数量中文大语言模型，覆盖从分词、预训练、指令微调到人类对齐、测评、量化和部署的全流程。
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    LLM, Chinese-LLM, Large-Language-Models, NLP, Deep-Learning, LLM-Training-Pipeline, From-Scratch-LLM, Instruction-Tuning, RLHF, DPO, Tiny-LLM
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root and populate it with the text of your chosen open-source license (e.g., MIT 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
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. huggingface/datasets · recommended 1×
  3. BERT-base-Chinese · recommended 1×
  4. RoBERTa-base-Chinese · recommended 1×
  5. ELECTRA-base-Chinese · recommended 1×
  • CATEGORY QUERY
    How to implement a complete small-scale Chinese large language model pipeline for learning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face Datasets (huggingface/datasets)
    3. BERT-base-Chinese
    4. RoBERTa-base-Chinese
    5. ELECTRA-base-Chinese
    6. Bloom-560m
    7. Qwen-1.8B
    8. BertTokenizer
    9. AutoTokenizer
    10. PyTorch (pytorch/pytorch)
    11. TensorFlow (tensorflow/tensorflow)
    12. JAX (google/jax)
    13. Jieba (fxsjy/jieba)
    14. TensorBoard (tensorflow/tensorboard)
    15. Pandas (pandas-dev/pandas)
    16. Scikit-learn (scikit-learn/scikit-learn)
    17. NLTK (nltk/nltk)

    AI recommended 17 alternatives but never named wdndev/tiny-llm-zh. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source projects provide full lifecycle support for building small Chinese LLMs?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Datasets
    3. Hugging Face Accelerate
    4. Hugging Face Hub
    5. PaddlePaddle
    6. PaddleNLP
    7. PaddleSpeech
    8. PaddleServing
    9. MindSpore
    10. MindFormers
    11. OpenMMLab
    12. OpenMMLab LLM
    13. DeepSpeed

    AI recommended 13 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 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?

  • 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 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?

Embed your GEO score

Drop this badge into the README of wdndev/tiny-llm-zh. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/wdndev/tiny-llm-zh.svg)](https://repogeo.com/en/r/wdndev/tiny-llm-zh)
HTML
<a href="https://repogeo.com/en/r/wdndev/tiny-llm-zh"><img src="https://repogeo.com/badge/wdndev/tiny-llm-zh.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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