RRepoGEO

REPOGEO REPORT · LITE

Wenyueh/MinivLLM

Default branch main · commit 6e47fd07 · scanned 6/10/2026, 10:33:05 PM

GitHub: 826 stars · 122 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 Wenyueh/MinivLLM, 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 GitHub topics for categorization

    Why:

    COPY-PASTE FIX
    llm-inference, paged-attention, flash-attention, vllm, nano-vllm, llm-benchmarking, cuda, deep-learning, machine-learning
  • highreadme#2
    Refine README's opening sentence to emphasize engine and benchmarking

    Why:

    CURRENT
    A custom implementation of vLLM inference engine with attention mechanism benchmarks, based on Nano-vLLM but with self-contained paged attention and flash attention implementation.
    COPY-PASTE FIX
    miniVLLM is a custom LLM inference engine, building on Nano-vLLM, that provides self-contained paged and flash attention implementations alongside comprehensive benchmarks for prefilling and decoding performance.
  • mediumhomepage#3
    Set the repository's homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/Wenyueh/MinivLLM

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 Wenyueh/MinivLLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
vllm-project/vllm
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. vllm-project/vllm · recommended 1×
  2. huggingface/text-generation-inference · recommended 1×
  3. microsoft/DeepSpeed · recommended 1×
  4. NVIDIA/TensorRT-LLM · recommended 1×
  5. ggerganov/llama.cpp · recommended 1×
  • CATEGORY QUERY
    Looking for a lightweight LLM inference engine demonstrating paged and flash attention.
    you: not recommended
    AI recommended (in order):
    1. vLLM (vllm-project/vllm)
    2. TGI (Text Generation Inference) (huggingface/text-generation-inference)
    3. DeepSpeed-MII (Model Inference Interface) (microsoft/DeepSpeed)
    4. TensorRT-LLM (NVIDIA/TensorRT-LLM)
    5. llama.cpp (ggerganov/llama.cpp)

    AI recommended 5 alternatives but never named Wenyueh/MinivLLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to benchmark prefilling and decoding performance of custom LLM attention implementations?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Profiler
    2. NVIDIA Nsight Systems
    3. NVIDIA Nsight Compute
    4. `time` module (Python)
    5. `time` command (Linux)
    6. `triton.testing.do_bench` (Triton)
    7. `huggingface/transformers` (huggingface/transformers)
    8. TensorBoard
    9. `nvidia-smi`
    10. `xformers` (facebookresearch/xformers)
    11. `flash_attention` (Dao-AILab/flash-attention)

    AI recommended 11 alternatives but never named Wenyueh/MinivLLM. 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 Wenyueh/MinivLLM?
    pass
    AI named Wenyueh/MinivLLM explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of Wenyueh/MinivLLM. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/Wenyueh/MinivLLM.svg)](https://repogeo.com/en/r/Wenyueh/MinivLLM)
HTML
<a href="https://repogeo.com/en/r/Wenyueh/MinivLLM"><img src="https://repogeo.com/badge/Wenyueh/MinivLLM.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

Wenyueh/MinivLLM — 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
Wenyueh/MinivLLM — RepoGEO report