RRepoGEO

REPOGEO REPORT · LITE

DLLXW/baby-llama2-chinese

Default branch main · commit 98a20dbb · scanned 5/16/2026, 2:03:08 PM

GitHub: 2,918 stars · 357 forks

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 DLLXW/baby-llama2-chinese, 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
    Reposition the README introduction to clearly state its end-to-end pipeline nature

    Why:

    CURRENT
    本项目致力于构建一个小参数量的中文Llama2仓库。
    COPY-PASTE FIX
    本项目是一个**端到端（从头预训练到SFT）**的小参数量中文Llama2**训练与微调工具库**，旨在帮助LLM初学者在有限资源下（如24G单卡）快速构建具备中文问答能力的Chat-Llama2模型。它提供从数据处理、预训练、SFT指令微调到模型评估的完整流程，并计划扩展至奖励模型和强化学习。
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llama2, chinese-llm, llm-training, fine-tuning, pre-training, deep-learning, nlp, pytorch, small-llm, end-to-end-llm, gpu-efficient
  • mediumreadme#3
    Add a dedicated section highlighting the project's unique value proposition

    Why:

    COPY-PASTE FIX
    ## ✨ Why Baby-Llama2-Chinese?
本项目专注于提供一个**轻量级、资源高效**的中文Llama2训练与微调解决方案。与大型模型或通用框架不同，Baby-Llama2-Chinese旨在让个人开发者和研究者在**单张24G显卡**等有限硬件条件下，也能从头构建并微调出具备实用中文问答能力的LLM，实现**端到端**的LLM学习与实践。

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 DLLXW/baby-llama2-chinese
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. huggingface/peft · recommended 1×
  3. TimDettmers/bitsandbytes · recommended 1×
  4. facebookresearch/fastText · recommended 1×
  5. PaddlePaddle/Paddle · recommended 1×
  • CATEGORY QUERY
    How to pre-train and fine-tune a small Chinese language model on limited GPU resources?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PEFT (huggingface/peft)
    3. BitsAndBytes (TimDettmers/bitsandbytes)
    4. FastText (facebookresearch/fastText)
    5. PaddlePaddle (PaddlePaddle/Paddle)
    6. DeepSpeed (microsoft/DeepSpeed)
    7. TensorFlow Lite
    8. ONNX Runtime (microsoft/onnxruntime)

    AI recommended 8 alternatives but never named DLLXW/baby-llama2-chinese. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What is a good open-source repository for learning to build Chinese LLMs end-to-end?
    you: not recommended
    AI recommended (in order):
    1. FlagEval
    2. Chinese-LLaMA-Alpaca (ymcui/Chinese-LLaMA-Alpaca)
    3. ChatGLM-6B / ChatGLM2-6B / ChatGLM3
    4. LLaMA-Factory (hiyouga/LLaMA-Factory)
    5. Chinese-BERT-wwm (ymcui/Chinese-BERT-wwm)

    AI recommended 5 alternatives but never named DLLXW/baby-llama2-chinese. 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 DLLXW/baby-llama2-chinese?
    pass
    AI did not name DLLXW/baby-llama2-chinese — 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 DLLXW/baby-llama2-chinese in production, what risks or prerequisites should they evaluate first?
    pass
    AI named DLLXW/baby-llama2-chinese 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 DLLXW/baby-llama2-chinese solve, and who is the primary audience?
    pass
    AI did not name DLLXW/baby-llama2-chinese — 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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DLLXW/baby-llama2-chinese — 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