RRepoGEO

REPOGEO REPORT · LITE

wdndev/llama3-from-scratch-zh

Default branch main · commit 9aaab641 · scanned 6/20/2026, 1:38:32 PM

GitHub: 1,049 stars · 97 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
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

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

OVERALL DIRECTION
  • highreadme#1
    Refine the README's opening paragraph to emphasize core value and keywords

    Why:

    CURRENT
    # 从零实现 Llama3 模型

## 注意
1. 本文翻译自大佬的 llama3-from-scratch 仓库，本人只是将英文翻译为中文，并无任何改动，略微改动模型权重文件，方便加载。原版英文：[README_en.md](README_en.md)。
    COPY-PASTE FIX
    # 从零实现 Llama3 模型：中文版深度学习指南

本仓库提供了一个从零开始实现 Llama3 模型核心组件的中文教程，涵盖张量运算和矩阵乘法，旨在帮助中文读者深入理解大型语言模型（LLM）的内部工作原理。此项目是 llama3-from-scratch 仓库的中文翻译版本，并针对中文环境进行了优化，方便加载模型权重。
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    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
karpathy/nanogpt
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. karpathy/nanogpt · recommended 2×
  2. PyTorch · recommended 1×
  3. NumPy · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. Jupyter Notebooks · recommended 1×
  • CATEGORY QUERY
    How can I learn to implement a large language model's core components from scratch?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. NumPy
    3. Hugging Face Transformers
    4. Jupyter Notebooks
    5. JupyterLab
    6. Matplotlib
    7. Seaborn
    8. scikit-learn
    9. Weights & Biases
    10. TensorBoard

    AI recommended 10 alternatives but never named wdndev/llama3-from-scratch-zh. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a guide to understand LLM internals with minimal computational resources.
    you: not recommended
    AI recommended (in order):
    1. The Illustrated Transformer
    2. NanoGPT (karpathy/nanogpt)
    3. Let's build GPT: from scratch, in code, spelled out. (karpathy/nanogpt)
    4. Hugging Face Transformers Library (huggingface/transformers)
    5. Attention Is All You Need

    AI recommended 5 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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wdndev/llama3-from-scratch-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