RRepoGEO

REPOGEO REPORT · LITE

srush/llama2.rs

Default branch main · commit 2ca8f3dc · scanned 5/17/2026, 5:12:38 PM

GitHub: 1,061 stars · 56 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
  • highreadme#1
    Reposition the README's opening to highlight its unique approach

    Why:

    CURRENT
    # llama2.rs 🤗

This is a Rust implementation of Llama2 inference on CPU

The goal is to be as fast as possible.
    COPY-PASTE FIX
    # llama2.rs 🤗

A fast, minimalist Llama 2 inference engine in pure Rust, inspired by `llama.cpp`. Optimized for CPU, it offers high-performance, quantized inference with features like batched prefill and SIMD support, making it ideal for integrating Llama 2 directly into Rust applications.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    llama2, rust, llm, inference, cpu, quantization, gptq, machine-learning, deep-learning, ai
  • mediumhomepage#3
    Add the repository URL as the project homepage

    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
huggingface/candle
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/candle · recommended 2×
  2. rust-llm/llm · recommended 1×
  3. huggingface/rust-bert · recommended 1×
  4. sonos/tract · recommended 1×
  5. microsoft/onnxruntime-rs · recommended 1×
  • CATEGORY QUERY
    What are the best options for efficient CPU inference of large language models in Rust?
    you: not recommended
    AI recommended (in order):
    1. candle (huggingface/candle)
    2. llm (rust-llm/llm)
    3. rust-bert (huggingface/rust-bert)
    4. tract (sonos/tract)
    5. ONNX Runtime Rust bindings (microsoft/onnxruntime-rs)
    6. burn (burn-rs/burn)

    AI recommended 6 alternatives but never named srush/llama2.rs. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a performant Rust library for quantized large language model inference with Python bindings.
    you: not recommended
    AI recommended (in order):
    1. candle (huggingface/candle)
    2. llm-rs
    3. ctranslate2
    4. Burn
    5. tch-rs

    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?

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
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