RRepoGEO

REPOGEO REPORT · LITE

jiahe7ay/MINI_LLM

Default branch main · commit 78998e22 · scanned 6/3/2026, 8:12:59 PM

GitHub: 503 stars · 72 forks

AI VISIBILITY SCORE
30 /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
3 / 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 jiahe7ay/MINI_LLM, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT License) to the root of the repository.
  • highreadme#2
    Clarify README's opening to emphasize 'end-to-end example for learning'

    Why:

    CURRENT
    # Mini-llm
Created by Lil2J
## 📝介绍
本项目是我个人关于一个小参数量的中文大模型的一个实践复现。
    COPY-PASTE FIX
    # Mini-llm: 小型中文大模型端到端学习与复现示例
Created by Lil2J
## 📝介绍
本项目是我个人关于一个小参数量的中文大模型的一个实践复现，旨在提供一个完整的、可学习的LLM训练与微调流程示例。

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 jiahe7ay/MINI_LLM
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. Accelerate · recommended 1×
  3. PyTorch Lightning · recommended 1×
  4. DeepSpeed · recommended 1×
  5. MosaicML Composer · recommended 1×
  • CATEGORY QUERY
    How can I set up a full pipeline to pre-train and fine-tune a large language model?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Accelerate
    3. PyTorch Lightning
    4. DeepSpeed
    5. MosaicML Composer
    6. Google Cloud Vertex AI
    7. AWS SageMaker
    8. Azure Machine Learning
    9. NVIDIA NeMo Framework

    AI recommended 9 alternatives but never named jiahe7ay/MINI_LLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools are available for developing and experimenting with small-scale Chinese language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch
    3. torchtext
    4. TensorFlow
    5. Keras
    6. PaddlePaddle
    7. PaddleNLP
    8. Jieba
    9. pkuseg
    10. spaCy
    11. OpenNMT-py

    AI recommended 11 alternatives but never named jiahe7ay/MINI_LLM. 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 jiahe7ay/MINI_LLM?
    pass
    AI named jiahe7ay/MINI_LLM explicitly

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

  • If a team adopts jiahe7ay/MINI_LLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jiahe7ay/MINI_LLM 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 jiahe7ay/MINI_LLM solve, and who is the primary audience?
    pass
    AI named jiahe7ay/MINI_LLM 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 jiahe7ay/MINI_LLM. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/jiahe7ay/MINI_LLM"><img src="https://repogeo.com/badge/jiahe7ay/MINI_LLM.svg" alt="RepoGEO" /></a>
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jiahe7ay/MINI_LLM — 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