RRepoGEO

REPOGEO REPORT · LITE

EricLBuehler/mistral.rs

Default branch master · commit 6501d106 · scanned 5/17/2026, 3:37:09 AM

GitHub: 7,141 stars · 600 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
28 /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
2 / 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 EricLBuehler/mistral.rs, 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
    Update the repository 'About' description to highlight key features

    Why:

    CURRENT
    Fast, flexible LLM inference
    COPY-PASTE FIX
    A fast, pure Rust LLM inference engine supporting any Hugging Face model, true multimodality (text, vision, audio), full quantization control (MXFP4 ISQ), and a built-in web UI.
  • highhomepage#2
    Add the project documentation URL to the repository homepage field

    Why:

    COPY-PASTE FIX
    https://ericlbuehler.github.io/mistral.rs/
  • mediumtopics#3
    Expand repository topics with specific feature keywords

    Why:

    CURRENT
    llm, rust, uqff
    COPY-PASTE FIX
    llm, rust, uqff, llm-inference, multimodal, quantization, inference-engine, llm-runtime, huggingface-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 EricLBuehler/mistral.rs
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/candle
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/candle · recommended 1×
  2. rust-llm/llm · recommended 1×
  3. LaurentMazare/tch-rs · recommended 1×
  4. huggingface/rust-bert · recommended 1×
  5. burn-rs/burn · recommended 1×
  • CATEGORY QUERY
    How can I achieve high-performance large language model inference using Rust?
    you: not recommended
    AI recommended (in order):
    1. candle (huggingface/candle)
    2. llm (rust-llm/llm)
    3. tch-rs (LaurentMazare/tch-rs)
    4. rust-bert (huggingface/rust-bert)
    5. burn (burn-rs/burn)
    6. tract (sonos/tract)

    AI recommended 6 alternatives but never named EricLBuehler/mistral.rs. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools enable efficient multimodal LLM inference with quantization options for Hugging Face models?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. Hugging Face Optimum
    3. ONNX Runtime
    4. Intel OpenVINO
    5. TensorRT-LLM
    6. DeepSpeed-MII
    7. TGI (Text Generation Inference) by Hugging Face
    8. LMDeploy

    AI recommended 8 alternatives but never named EricLBuehler/mistral.rs. 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 EricLBuehler/mistral.rs?
    pass
    AI named EricLBuehler/mistral.rs explicitly

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

  • If a team adopts EricLBuehler/mistral.rs in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name EricLBuehler/mistral.rs — likely talking about a different project

    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 EricLBuehler/mistral.rs solve, and who is the primary audience?
    pass
    AI named EricLBuehler/mistral.rs explicitly

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

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EricLBuehler/mistral.rs — 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