RRepoGEO

REPOGEO REPORT · LITE

srush/llama2.rs

Default branch main · commit 2ca8f3dc · scanned 6/28/2026, 9:53:48 PM

GitHub: 1,064 stars · 57 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 srush/llama2.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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llama2, rust, llm, inference, cpu, quantization, machine-learning, deep-learning, ai, gptq
  • highreadme#2
    Strengthen README's opening statement for performance and niche

    Why:

    CURRENT
    This is a Rust implementation of Llama2 inference on CPU
The goal is to be as fast as possible.
    COPY-PASTE FIX
    This is a highly optimized, pure Rust implementation for Llama2 inference on CPU. Designed for maximum speed and minimal dependencies, `llama2.rs` provides a `llama.cpp`-like solution for running quantized Llama2 models directly in Rust, featuring 4-bit GPT-Q quantization, batched prefill, and SIMD support.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/srush/llama2.rs

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 srush/llama2.rs
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
tch-rs
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. tch-rs · recommended 2×
  2. candle · recommended 1×
  3. llm · recommended 1×
  4. tract · recommended 1×
  5. ort · recommended 1×
  • CATEGORY QUERY
    Seeking a fast Rust library for quantized large model inference on CPU.
    you: not recommended
    AI recommended (in order):
    1. candle
    2. llm
    3. tract
    4. tch-rs
    5. ort

    AI recommended 5 alternatives but never named srush/llama2.rs. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Rust frameworks offer high-performance CPU inference for large models with Python integration?
    you: not recommended
    AI recommended (in order):
    1. Burn
    2. Candle
    3. dfdx
    4. tch-rs
    5. ONNX Runtime

    AI recommended 5 alternatives but never named srush/llama2.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 srush/llama2.rs?
    pass
    AI did not name srush/llama2.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?

  • If a team adopts srush/llama2.rs in production, what risks or prerequisites should they evaluate first?
    pass
    AI named srush/llama2.rs 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 srush/llama2.rs solve, and who is the primary audience?
    pass
    AI named srush/llama2.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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srush/llama2.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