RRepoGEO

REPOGEO REPORT · LITE

ZHZisZZ/dllm

Default branch main · commit ca176752 · scanned 5/17/2026, 4:23:20 AM

GitHub: 2,502 stars · 264 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
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 ZHZisZZ/dllm, 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
  • highabout#1
    Clarify the 'About' description to specify project type

    Why:

    CURRENT
    dLLM: Simple Diffusion Language Modeling
    COPY-PASTE FIX
    dLLM: A Python library for training and evaluating diffusion language models.
  • mediumreadme#2
    Reinforce project type and purpose in the README's opening tagline

    Why:

    CURRENT
    <p align="center">
Simple Diffusion Language Modeling
</p>
    COPY-PASTE FIX
    <p align="center">
A Python library for unifying the training and evaluation of diffusion language models.
</p>
  • lowtopics#3
    Add more specific and clarifying topics

    Why:

    CURRENT
    discrete-diffusion-models, llm, nlp
    COPY-PASTE FIX
    discrete-diffusion-models, llm, nlp, python, diffusion-language-models

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 ZHZisZZ/dllm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. JAX · recommended 2×
  3. TensorFlow · recommended 2×
  4. Keras · recommended 2×
  5. Hugging Face Diffusers · recommended 1×
  • CATEGORY QUERY
    How can I train and evaluate diffusion-based language models efficiently?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Diffusers
    3. Hugging Face Accelerate
    4. PyTorch Lightning
    5. JAX
    6. Flax
    7. DeepSpeed
    8. NVIDIA Apex
    9. TensorFlow
    10. Keras
    11. TF-Agents

    AI recommended 11 alternatives but never named ZHZisZZ/dllm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks exist for building and experimenting with discrete diffusion models for NLP?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch
    3. JAX
    4. TensorFlow
    5. Keras
    6. diffusers

    AI recommended 6 alternatives but never named ZHZisZZ/dllm. 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 ZHZisZZ/dllm?
    pass
    AI named ZHZisZZ/dllm explicitly

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

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

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

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ZHZisZZ/dllm — 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