REPOGEO REPORT · LITE

ZHZisZZ/dllm

Default branch main · commit ca176752 · scanned 6/28/2026, 5:43:35 AM

GitHub: 2,593 stars · 271 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

highreadme#1

Elevate the core definition of dLLM in the README

Why:

CURRENT

The current structure where 'Simple Diffusion Language Modeling' is a paragraph under H1, and the detailed definition is in the 'Overview' section.

COPY-PASTE FIX

Replace the `<p align="center">Simple Diffusion Language Modeling</p>` with a more explicit and prominent statement, perhaps directly under the H1, like: `dLLM is a library that unifies the training and evaluation of diffusion language models, bringing transparency and reproducibility to the entire development pipeline.`

hightopics#2

Add more specific diffusion-related topics

Why:

CURRENT

discrete-diffusion-models, llm, nlp

COPY-PASTE FIX

discrete-diffusion-models, diffusion-models, language-modeling, nlp, generative-ai, deep-learning, transformers

mediumreadme#3

Add a 'Why dLLM?' or 'Comparison' section to the README

Why:

COPY-PASTE FIX

Add a new section titled 'Why dLLM?' or 'Comparison to other Diffusion/LLM Frameworks' that explicitly states what dLLM is *not* (e.g., not a general LLM inference engine like vLLM) and highlights its unique focus on unifying training and evaluation for *diffusion language models* compared to libraries like Hugging Face Diffusers or Transformers.

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

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 1×
Diffusers · recommended 1×
GLIDE · recommended 1×
DALL-E 2 · recommended 1×
CompVis Latent Diffusion Models · recommended 1×

CATEGORY QUERY
How can I train and evaluate diffusion models for natural language generation tasks?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. Diffusers
3. GLIDE
4. DALL-E 2
5. CompVis Latent Diffusion Models
6. Stable Diffusion
7. Imagen
8. Parti
9. PyTorch
10. TensorFlow
AI recommended 10 alternatives but never named ZHZisZZ/dllm. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a library for scalable training and unified evaluation of discrete diffusion language models.
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. PyTorch-Lightning (Lightning-AI/pytorch-lightning)
3. Diffusers (huggingface/diffusers)
4. JAX (google/jax)
5. Flax (google/flax)
6. DeepSpeed (microsoft/DeepSpeed)
7. OpenAI's `guided-diffusion` (openai/guided-diffusion)
AI recommended 7 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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MARKDOWN (README)

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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