RRepoGEO

REPOGEO REPORT · LITE

horseee/Awesome-Efficient-LLM

Default branch main · commit 215a1540 · scanned 5/14/2026, 1:59:17 AM

GitHub: 2,003 stars · 165 forks

AI VISIBILITY SCORE
22 /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
1 / 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
    Reposition README's opening to clarify repo's identity as a curated list

    Why:

    CURRENT
    A curated list for **Efficient Large Language Models**
    COPY-PASTE FIX
    A comprehensive, curated list of **papers, techniques, and projects** for **Efficient Large Language Models (LLMs)**, designed for researchers and engineers to explore and understand the latest advancements in LLM optimization.
  • highlicense#2
    Add a LICENSE file and reference it in the README

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the MIT License text. Add the following section to your README: `## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.`
  • mediumtopics#3
    Expand repository topics and set a homepage URL

    Why:

    CURRENT
    Topics: compression, efficient-llm, knowledge-distillation, language-model, llm, llm-compression, model-quantization, pruning-algorithms
    COPY-PASTE FIX
    Add the following topics: `inference-acceleration`, `mixture-of-experts`, `kv-cache-compression`, `low-rank-decomposition`, `efficient-fine-tuning`, `efficient-training`, `llm-reasoning`. Also, set the 'Homepage' field in the repository settings to: `https://github.com/horseee/Awesome-Efficient-LLM`

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. ONNX Runtime · recommended 1×
  5. TensorRT · recommended 1×
  • CATEGORY QUERY
    How to reduce computational cost and memory footprint for large language models?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes
    2. ONNX Runtime
    3. TensorRT
    4. Hugging Face Transformers library
    5. PaddlePaddle's PaddleSlim
    6. PyTorch's `torch.nn.utils.prune`
    7. TensorFlow Model Optimization Toolkit
    8. FlashAttention
    9. Mamba
    10. LoRA
    11. QLoRA
    12. Hugging Face PEFT library
    13. DeepSpeed
    14. accelerate
    15. FairScale
    16. Apache TVM
    17. OpenVINO

    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 exist for optimizing LLM inference speed and reducing model size?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes (TimDettmers/bitsandbytes)
    2. NVIDIA TensorRT (NVIDIA/TensorRT)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. OpenVINO (openvinotoolkit/openvino)
    5. PyTorch (pytorch/pytorch)
    6. NVIDIA Apex (NVIDIA/apex)
    7. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    8. Hugging Face Transformers (huggingface/transformers)
    9. TensorFlow (tensorflow/tensorflow)
    10. FlashAttention (Dao-AILab/flash-attention)
    11. NVIDIA TensorRT-LLM (NVIDIA/TensorRT-LLM)
    12. vLLM (vllm-project/vllm)
    13. Triton Inference Server (triton-inference-server/server)
    14. DeepSpeed (microsoft/DeepSpeed)
    15. TorchDynamo (pytorch/pytorch)
    16. XLA (tensorflow/tensorflow)
    17. TVM (apache/tvm)

    AI recommended 17 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 named horseee/Awesome-Efficient-LLM 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 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?

Embed your GEO score

Drop this badge into the README of horseee/Awesome-Efficient-LLM. 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/horseee/Awesome-Efficient-LLM.svg)](https://repogeo.com/en/r/horseee/Awesome-Efficient-LLM)
HTML
<a href="https://repogeo.com/en/r/horseee/Awesome-Efficient-LLM"><img src="https://repogeo.com/badge/horseee/Awesome-Efficient-LLM.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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