RRepoGEO

REPOGEO REPORT · LITE

datawhalechina/so-large-lm

Default branch main · commit aec61236 · scanned 5/20/2026, 12:47:39 PM

GitHub: 7,301 stars · 604 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
28 /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
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 datawhalechina/so-large-lm, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    large-language-models, llm, deep-learning, machine-learning, ai, nlp, tutorial, curriculum, education, datawhale
  • highlicense#2
    Create a LICENSE file

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the text of the MIT License.
  • highabout#3
    Enhance the repository's About description

    Why:

    CURRENT
    大模型基础: 一文了解大模型基础知识
    COPY-PASTE FIX
    一个开源、系统、深入的大规模预训练语言模型（LLM）教程，从理论到实战，全面构建LLM知识体系。

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/so-large-lm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
The Illustrated Transformer
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. The Illustrated Transformer · recommended 1×
  2. Attention Is All You Need · recommended 1×
  3. karpathy/makemore · recommended 1×
  4. Generative AI with Large Language Models · recommended 1×
  5. huggingface/transformers · recommended 1×
  • CATEGORY QUERY
    What are the best resources for a complete introduction to large language models?
    you: not recommended
    AI recommended (in order):
    1. The Illustrated Transformer
    2. Attention Is All You Need
    3. Neural Networks: Zero to Hero (karpathy/makemore)
    4. Generative AI with Large Language Models
    5. Hugging Face Transformers (huggingface/transformers)

    AI recommended 5 alternatives but never named datawhalechina/so-large-lm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a structured curriculum to learn large language model theory and practice?
    you: not recommended
    AI recommended (in order):
    1. DeepLearning.AI's "Generative AI with Large Language Models" Specialization
    2. Coursera
    3. Google Cloud
    4. Hugging Face's "Natural Language Processing Course"
    5. Hugging Face Transformers library
    6. Stanford CS224N: Natural Language Processing with Deep Learning
    7. fast.ai's "Practical Deep Learning for Coders"
    8. edX's "Large Language Models (LLMs)" Professional Certificate
    9. MIT 6.S191: Introduction to Deep Learning

    AI recommended 9 alternatives but never named datawhalechina/so-large-lm. 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/so-large-lm?
    pass
    AI named datawhalechina/so-large-lm explicitly

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

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