RRepoGEO

REPOGEO REPORT · LITE

wdndev/llama3-from-scratch-zh

Default branch main · commit 9aaab641 · scanned 5/10/2026, 2:38:20 PM

GitHub: 1,042 stars · 96 forks

AI VISIBILITY SCORE
22 /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
1 / 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/llama3-from-scratch-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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llama3, from-scratch, llm-implementation, deep-learning, transformer, educational, tutorial, machine-learning, chinese, llm-from-scratch
  • highreadme#2
    Clarify README's opening statement for educational positioning

    Why:

    CURRENT
    在这个文件中，从头实现了 Llama3，其中包含张量和矩阵乘法。
此外，直接从 Meta 提供的 Llama3 模型文件中加载张量，在运行此文件之前，需要下载权重。
    COPY-PASTE FIX
    这是一个从零开始实现 Llama3 模型架构的中文教程，旨在帮助开发者和学习者深入理解大型语言模型的内部工作原理，包括张量运算和模型加载。我们直接从 Meta 提供的 Llama3 模型文件中加载张量，并提供了内存优化的两层模型权重，方便在资源有限的机器上进行学习和实验。
  • mediumhomepage#3
    Add a relevant homepage link

    Why:

    COPY-PASTE FIX
    https://colab.research.google.com/drive/11MQb8Bn4Ck707VEcqqGVdytqOk3OrQQK?usp=sharing

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/llama3-from-scratch-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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. Hugging Face Tokenizers · recommended 1×
  3. SentencePiece · recommended 1×
  4. PyTorch · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    What are the steps to implement a modern transformer-based language model from scratch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Tokenizers
    2. SentencePiece
    3. PyTorch
    4. TensorFlow
    5. Keras
    6. JAX
    7. Hugging Face Transformers

    AI recommended 7 alternatives but never named wdndev/llama3-from-scratch-zh. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement a large language model on a machine with limited memory?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. bitsandbytes
    3. AWQ
    4. GPTQ
    5. AutoGPTQ
    6. optimum
    7. llama.cpp
    8. ONNX Runtime
    9. OpenVINO
    10. TensorRT
    11. TinyLlama
    12. Phi-2
    13. Gemma

    AI recommended 13 alternatives but never named wdndev/llama3-from-scratch-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
    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 wdndev/llama3-from-scratch-zh?
    pass
    AI did not name wdndev/llama3-from-scratch-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/llama3-from-scratch-zh in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wdndev/llama3-from-scratch-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/llama3-from-scratch-zh solve, and who is the primary audience?
    pass
    AI did not name wdndev/llama3-from-scratch-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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