RRepoGEO

REPOGEO REPORT · LITE

InternLM/xtuner

Default branch main · commit 5d7b1048 · scanned 5/27/2026, 4:17:34 PM

GitHub: 5,138 stars · 422 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 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 InternLM/xtuner, 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 core value proposition to the top of the README

    Why:

    CURRENT
    The README excerpt shows '## 🚀 Speed Benchmark' and '## 🎉 News' before '## 📖 XTuner V1'.
    COPY-PASTE FIX
    Move the sentence 'XTuner V1 is a next-generation LLM training engine specifically designed for ultra-large-scale MoE models.' to be one of the first text lines in the README, ideally as a prominent H1 or H2.
  • hightopics#2
    Add functional keywords to the repository topics

    Why:

    CURRENT
    agent, deepseek-v3, gpt-oss, intern-s1, internvl, kimi-k2, llm, multimodal, qwen3-moe, qwen3-vl, reinforcement-learning
    COPY-PASTE FIX
    Add the following topics: `moe-training`, `llm-training`, `distributed-training`, `fine-tuning`, `large-language-models`, `deep-learning-framework`.
  • mediumcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., 'XTuner vs. DeepSpeed/Megatron-LM', explaining when XTuner is the preferred choice, especially for InternLM models and MoE-specific optimizations, compared to more general distributed training frameworks.

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 InternLM/xtuner
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/DeepSpeed
Recommended in 6 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/DeepSpeed · recommended 6×
  2. pytorch/pytorch · recommended 3×
  3. NVIDIA/Megatron-LM · recommended 2×
  4. hpcaitech/ColossalAI · recommended 2×
  5. facebookresearch/fairscale · recommended 1×
  • CATEGORY QUERY
    Looking for an efficient training engine for ultra-large Mixture-of-Experts (MoE) language models.
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/DeepSpeed)
    2. Megatron-LM (NVIDIA/Megatron-LM)
    3. FairScale (facebookresearch/fairscale)
    4. Colossal-AI (hpcaitech/ColossalAI)
    5. PyTorch FSDP (pytorch/pytorch)

    AI recommended 5 alternatives but never named InternLM/xtuner. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest tools for optimizing large-scale MoE model training scenarios?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/DeepSpeed)
    2. DeepSpeed-MoE (microsoft/DeepSpeed)
    3. ZeRO (microsoft/DeepSpeed)
    4. DeepSpeed-Ulysses (microsoft/DeepSpeed)
    5. DeepSpeed-MII (microsoft/DeepSpeed)
    6. Megatron-LM (NVIDIA/Megatron-LM)
    7. CUDA
    8. NCCL
    9. Triton (openai/triton)
    10. FairSeq (facebookresearch/fairseq)
    11. PyTorch (pytorch/pytorch)
    12. FSDP (pytorch/pytorch)
    13. Colossal-AI (hpcaitech/ColossalAI)

    AI recommended 13 alternatives but never named InternLM/xtuner. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 InternLM/xtuner?
    pass
    AI named InternLM/xtuner explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite