RRepoGEO

REPOGEO REPORT · LITE

horseee/Awesome-Efficient-LLM

Default branch main · commit 215a1540 · scanned 6/24/2026, 1:08:23 PM

GitHub: 2,019 stars · 166 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
15 /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
0 / 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 horseee/Awesome-Efficient-LLM, 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
    Clarify README's opening to distinguish from executable tools

    Why:

    CURRENT
    A curated list for **Efficient Large Language Models**
    COPY-PASTE FIX
    This repository is a comprehensive, curated list of research papers and resources for **Efficient Large Language Models**, designed for researchers and practitioners to explore optimization techniques rather than providing direct implementations.
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that aligns with the project's intent for reuse.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add a relevant URL to the repository's homepage field in the About section (e.g., a project website, a related blog post, or simply the repository URL itself if no external site exists).

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 horseee/Awesome-Efficient-LLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 2×
  2. tensorflow/tensorflow · recommended 2×
  3. bitsandbytes · recommended 1×
  4. AutoGPTQ · recommended 1×
  5. ONNX Runtime · recommended 1×
  • CATEGORY QUERY
    How can I reduce the computational cost and memory footprint of large language models?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes
    2. AutoGPTQ
    3. ONNX Runtime
    4. TensorRT
    5. Hugging Face Optimum
    6. PyTorch
    7. DeepSpeed
    8. Hugging Face Transformers
    9. OpenVINO Toolkit
    10. FlashAttention
    11. FlashAttention-2
    12. LongFormer
    13. BigBird
    14. Mixtral 8x7B
    15. TVM (Apache TVM)
    16. vLLM
    17. llama.cpp

    AI recommended 17 alternatives but never named horseee/Awesome-Efficient-LLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What techniques are available for quantizing or pruning large language models for deployment?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. TensorFlow (tensorflow/tensorflow)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. PyTorch (pytorch/pytorch)
    5. TensorFlow (tensorflow/tensorflow)
    6. NVIDIA Apex (NVIDIA/apex)
    7. Hugging Face Transformers (huggingface/transformers)
    8. DistilBERT
    9. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    10. LoRA
    11. NVIDIA TensorRT
    12. Intel OpenVINO (openvinotoolkit/openvino)
    13. Qualcomm AI Engine Direct (QNN)

    AI recommended 13 alternatives but never named horseee/Awesome-Efficient-LLM. 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 horseee/Awesome-Efficient-LLM?
    pass
    AI did not name horseee/Awesome-Efficient-LLM — 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 horseee/Awesome-Efficient-LLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name horseee/Awesome-Efficient-LLM — 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 horseee/Awesome-Efficient-LLM solve, and who is the primary audience?
    pass
    AI did not name horseee/Awesome-Efficient-LLM — 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?

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horseee/Awesome-Efficient-LLM — 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