RRepoGEO

REPOGEO REPORT · LITE

qiufengqijun/mini_qwen

Default branch main · commit 4f1ea6ee · scanned 6/5/2026, 1:42:32 PM

GitHub: 852 stars · 109 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 qiufengqijun/mini_qwen, 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
    Add a concise introductory sentence to the README to clarify project type

    Why:

    CURRENT
    # mini_qwen

## 目录
- [简介](#简介)
- [快速开始](#快速开始)
- [模型下载链接](#模型下载链接)
- [数据集介绍](#数据集介绍)
- [训练流程](#训练流程)
- [结果分析与模型评估](#结果分析与模型评估)
- [猜你想问](#猜你想问)
- [总结](#总结)
- [避坑建议](#避坑建议)

## 简介
mini_qwen是一个从头开始训练的1B参数的大型语言模型(LLM)项目...
    COPY-PASTE FIX
    # mini_qwen

这是一个从头训练1B参数中英文大语言模型的参考项目，特别优化了低显存训练流程，涵盖预训练、微调和DPO。

## 目录
- [简介](#简介)
- [快速开始](#快速开始)
- [模型下载链接](#模型下载链接)
- [数据集介绍](#数据集介绍)
- [训练流程](#训练流程)
- [结果分析与模型评估](#结果分析与模型评估)
- [猜你想问](#猜你想问)
- [总结](#总结)
- [避坑建议](#避坑建议)

## 简介
mini_qwen是一个从头开始训练的1B参数的大型语言模型(LLM)项目...
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file to the repository root with a suitable open-source license (e.g., MIT, Apache-2.0, or GPL-3.0) to clarify usage rights.

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 qiufengqijun/mini_qwen
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Lightning-AI/lit-gpt
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Lightning-AI/lit-gpt · recommended 1×
  2. huggingface/accelerate · recommended 1×
  3. microsoft/DeepSpeed · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. TimDettmers/bitsandbytes · recommended 1×
  • CATEGORY QUERY
    How to train a small-scale LLM from scratch with limited GPU memory?
    you: not recommended
    AI recommended (in order):
    1. Lit-GPT (Lightning-AI/lit-gpt)
    2. Hugging Face Accelerate (huggingface/accelerate)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. PyTorch FSDP (pytorch/pytorch)
    5. bitsandbytes (TimDettmers/bitsandbytes)
    6. FlashAttention (Dao-AILab/flash-attention)

    AI recommended 6 alternatives but never named qiufengqijun/mini_qwen. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for experimenting with LLM pre-training, fine-tuning, and DPO for bilingual models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Accelerate
    3. PEFT
    4. TRL
    5. PyTorch Lightning
    6. DeepSpeed
    7. JAX
    8. Flax
    9. Orbax
    10. TensorFlow
    11. Keras
    12. Lit-GPT

    AI recommended 12 alternatives but never named qiufengqijun/mini_qwen. 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 qiufengqijun/mini_qwen?
    pass
    AI named qiufengqijun/mini_qwen explicitly

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

  • If a team adopts qiufengqijun/mini_qwen in production, what risks or prerequisites should they evaluate first?
    pass
    AI named qiufengqijun/mini_qwen 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 qiufengqijun/mini_qwen solve, and who is the primary audience?
    pass
    AI named qiufengqijun/mini_qwen explicitly

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

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qiufengqijun/mini_qwen — 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