RRepoGEO

REPOGEO REPORT · LITE

Xwin-LM/Xwin-LM

Default branch main · commit 4587c109 · scanned 6/18/2026, 11:38:09 PM

GitHub: 1,038 stars · 44 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
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 Xwin-LM/Xwin-LM, 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-alignment, large-language-models, sft, rlhf, reward-models, llama2, alpacaeval, math-benchmark, gsm8k, open-source-llm, deep-learning, machine-learning
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0). This is crucial for adoption and AI categorization.
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    Set the repository homepage URL to `https://huggingface.co/Xwin-LM` in the repository settings.

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 Xwin-LM/Xwin-LM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 2×
  2. huggingface/transformers · recommended 1×
  3. huggingface/peft · recommended 1×
  4. microsoft/DeepSpeed · recommended 1×
  5. huggingface/accelerate · recommended 1×
  • CATEGORY QUERY
    How to fine-tune large language models for better performance and stability?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PEFT (huggingface/peft)
    3. Microsoft DeepSpeed (microsoft/DeepSpeed)
    4. Hugging Face Accelerate (huggingface/accelerate)
    5. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    6. Weights & Biases (wandb/wandb)
    7. Ray Train (ray-project/ray)
    8. Ray Tune (ray-project/ray)
    9. Unsloth (unslothai/unsloth)

    AI recommended 9 alternatives but never named Xwin-LM/Xwin-LM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking open-source tools for advanced LLM alignment techniques like RLHF and reward modeling.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. TRL (Transformer Reinforcement Learning)
    3. DeepSpeed-Chat
    4. RL4LMs (Reinforcement Learning for Language Models)
    5. OpenAI's Alignment Research Framework (ARF)
    6. TRLX (Transformer Reinforcement Learning X)
    7. Colossal-AI

    AI recommended 7 alternatives but never named Xwin-LM/Xwin-LM. 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 Xwin-LM/Xwin-LM?
    pass
    AI named Xwin-LM/Xwin-LM explicitly

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

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

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

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Xwin-LM/Xwin-LM — 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