RRepoGEO

REPOGEO REPORT · LITE

datawhalechina/llms-from-scratch-cn

Default branch main · commit 6ca2631b · scanned 6/24/2026, 6:03:09 AM

GitHub: 4,213 stars · 581 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 datawhalechina/llms-from-scratch-cn, 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
    Add a direct, keyword-rich introductory sentence to the README

    Why:

    CURRENT
    如果你想从0手写代码，构建大语言模型，本项目很适合你。
    COPY-PASTE FIX
    本项目是一个面向中文读者的、基于Python的实践教程，旨在从零开始逐步构建和深入理解大型语言模型（LLM）的架构与原理。
  • hightopics#2
    Expand topics to include core technologies and content type

    Why:

    CURRENT
    glm, llama, llm, llms-from-scratch, rwkv
    COPY-PASTE FIX
    glm, llama, llm, llms-from-scratch, rwkv, python, deep-learning, machine-learning, tutorial, guide
  • mediumhomepage#3
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/datawhalechina/llms-from-scratch-cn

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 datawhalechina/llms-from-scratch-cn
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 1×
  2. Hugging Face Transformers · recommended 1×
  3. TensorFlow · recommended 1×
  4. Keras · recommended 1×
  5. JAX · recommended 1×
  • CATEGORY QUERY
    How can I learn to build large language models from scratch using Python?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. Hugging Face Transformers
    3. TensorFlow
    4. Keras
    5. JAX
    6. Flax
    7. Haiku
    8. NumPy
    9. OpenAI's Triton

    AI recommended 9 alternatives but never named datawhalechina/llms-from-scratch-cn. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a hands-on guide to deeply understand large language model architecture principles.
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. Hugging Face Transformers Library (huggingface/transformers)
    3. TensorFlow (tensorflow/tensorflow)
    4. JAX (google/jax)

    AI recommended 4 alternatives but never named datawhalechina/llms-from-scratch-cn. 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 datawhalechina/llms-from-scratch-cn?
    pass
    AI did not name datawhalechina/llms-from-scratch-cn — 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 datawhalechina/llms-from-scratch-cn in production, what risks or prerequisites should they evaluate first?
    pass
    AI named datawhalechina/llms-from-scratch-cn 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 datawhalechina/llms-from-scratch-cn solve, and who is the primary audience?
    pass
    AI did not name datawhalechina/llms-from-scratch-cn — 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?

Embed your GEO score

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datawhalechina/llms-from-scratch-cn — 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