RRepoGEO

REPOGEO REPORT · LITE

z-lab/dflash

Default branch main · commit 94e4abc5 · scanned 5/23/2026, 12:42:42 AM

GitHub: 4,695 stars · 331 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
35 /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
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 z-lab/dflash, 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
    Reinforce core domain and category in README's opening

    Why:

    CURRENT
    DFlash is a lightweight **block diffusion** model designed for speculative decoding. It enables efficient and high-quality parallel drafting.
    COPY-PASTE FIX
    DFlash is a **block diffusion model** for **speculative decoding**, a technique specifically for **accelerating large language model (LLM) generation**. It enables efficient and high-quality parallel drafting for LLMs.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm, large-language-models, speculative-decoding, block-diffusion, llm-acceleration, parallel-drafting, generative-ai, machine-learning
  • mediumreadme#3
    Add a clear 'Who is this for?' or 'Use Cases' section

    Why:

    COPY-PASTE FIX
    ## Who is DFlash for?
DFlash is designed for machine learning researchers and engineers working on accelerating large language model inference. It is particularly useful for those experimenting with speculative decoding techniques and seeking efficient parallel drafting solutions. Please note that DFlash is currently experimental and not yet production-ready.

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 z-lab/dflash
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers Library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers Library · recommended 1×
  2. vLLM · recommended 1×
  3. TensorRT-LLM · recommended 1×
  4. DeepSpeed-MII · recommended 1×
  5. Medusa · recommended 1×
  • CATEGORY QUERY
    How can I accelerate large language model generation using speculative decoding?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. vLLM
    3. TensorRT-LLM
    4. DeepSpeed-MII

    AI recommended 4 alternatives but never named z-lab/dflash. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient methods for parallel drafting in speculative decoding for LLMs?
    you: not recommended
    AI recommended (in order):
    1. Medusa
    2. Lookahead Decoding
    3. BRA Decoding
    4. Parallel Sampling with Tree Attention

    AI recommended 4 alternatives but never named z-lab/dflash. 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 z-lab/dflash?
    pass
    AI named z-lab/dflash explicitly

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

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

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

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z-lab/dflash — 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