RRepoGEO

REPOGEO REPORT · LITE

hans0809/MiniMind-in-Depth

Default branch main · commit f377fe40 · scanned 5/17/2026, 4:58:20 AM

GitHub: 1,003 stars · 86 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
23 /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
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 hans0809/MiniMind-in-Depth, 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
    llm, large-language-models, minimind, transformer, deep-learning, machine-learning, nlp, tokenizer, rope, moe, kv-cache, pretraining, sft, lora, dpo, tutorial, education, source-code-analysis
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    (Choose an appropriate open-source license like MIT or Apache-2.0 and add a LICENSE file to the repository root.)
  • mediumabout#3
    Refine the repository's About description

    Why:

    CURRENT
    轻量级大语言模型MiniMind的源码解读，包含tokenizer、RoPE、MoE、KV Cache、pretraining、SFT、LoRA、DPO等完整流程
    COPY-PASTE FIX
    深入解读轻量级大语言模型MiniMind的源码，提供从tokenizer到DPO的完整LLM架构与训练教程，帮助开发者和研究者透彻理解并实践大模型核心技术。

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 hans0809/MiniMind-in-Depth
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. Hugging Face Transformers Documentation · recommended 1×
  4. Deep Learning · recommended 1×
  5. Stanford CS224N: Natural Language Processing with Deep Learning · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive guide for understanding large language model architecture and training?
    you: not recommended
    AI recommended (in order):
    1. The Illustrated Transformer
    2. Attention Is All You Need
    3. Hugging Face Transformers Documentation
    4. Deep Learning
    5. Stanford CS224N: Natural Language Processing with Deep Learning
    6. OpenAI Blog Posts and Research Papers
    7. Google AI Blog

    AI recommended 7 alternatives but never named hans0809/MiniMind-in-Depth. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement and optimize advanced large language model components like MoE, LoRA, and DPO?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face Accelerate (huggingface/accelerate)
    3. PEFT (Parameter-Efficient Fine-tuning) Library (huggingface/peft)
    4. TRL (Transformer Reinforcement Learning) Library (huggingface/trl)
    5. PyTorch (pytorch/pytorch)
    6. PyTorch Lightning (Lightning-AI/lightning)
    7. DeepSpeed (microsoft/DeepSpeed)
    8. JAX (google/jax)
    9. Flax (google/flax)
    10. OpenAI Triton (openai/triton)
    11. bitsandbytes (TimDettmers/bitsandbytes)

    AI recommended 11 alternatives but never named hans0809/MiniMind-in-Depth. 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 hans0809/MiniMind-in-Depth?
    pass
    AI named hans0809/MiniMind-in-Depth explicitly

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

  • If a team adopts hans0809/MiniMind-in-Depth in production, what risks or prerequisites should they evaluate first?
    pass
    AI named hans0809/MiniMind-in-Depth 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 hans0809/MiniMind-in-Depth solve, and who is the primary audience?
    pass
    AI did not name hans0809/MiniMind-in-Depth — 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

Drop this badge into the README of hans0809/MiniMind-in-Depth. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/hans0809/MiniMind-in-Depth.svg)](https://repogeo.com/en/r/hans0809/MiniMind-in-Depth)
HTML
<a href="https://repogeo.com/en/r/hans0809/MiniMind-in-Depth"><img src="https://repogeo.com/badge/hans0809/MiniMind-in-Depth.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

hans0809/MiniMind-in-Depth — 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