RRepoGEO

REPOGEO REPORT · LITE

wyf3/llm_related

Default branch main · commit 7c8bf6ba · scanned 5/28/2026, 10:48:10 AM

GitHub: 3,377 stars · 455 forks

AI VISIBILITY SCORE
25 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
0 pass · 1 warn · 1 fail
Objective metadata checks
AI knows your name
3 / 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 wyf3/llm_related, 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
    Expand README to clarify purpose as a learning resource/implementation collection

    Why:

    CURRENT
    # 复现各种大模型相关算法
    COPY-PASTE FIX
    # LLM Related Algorithms & Learning Records

This repository serves as a curated collection of implementations, learning notes, and practical examples for reproducing various large language model (LLM) algorithms. It's designed for researchers, developers, and enthusiasts looking to understand and replicate state-of-the-art LLM architectures and techniques, rather than being a production-ready library or framework.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    large-language-models, llm-implementations, deep-learning, machine-learning, nlp, algorithm-reproduction, learning-resources
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file to the repository root, choosing a standard open-source license such as MIT or Apache-2.0.

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 wyf3/llm_related
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 1×
  2. tensorflow/tensorflow · recommended 1×
  3. keras-team/keras · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. Dao-AILab/flash-attention · recommended 1×
  • CATEGORY QUERY
    How can I learn to implement large language model algorithms from scratch?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. TensorFlow (tensorflow/tensorflow)
    3. Keras (keras-team/keras)
    4. Hugging Face Transformers Library (huggingface/transformers)
    5. FlashAttention (Dao-AILab/flash-attention)
    6. DeepSpeed (microsoft/DeepSpeed)
    7. FSDP
    8. SentencePiece (google/sentencepiece)
    9. makemore (karpathy/makemore)
    10. nanoGPT (karpathy/nanoGPT)

    AI recommended 10 alternatives but never named wyf3/llm_related. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for resources to reproduce state-of-the-art large language model architectures.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. Hugging Face Transformers
    3. TensorFlow / Keras
    4. DeepSpeed
    5. Megatron-LM
    6. JAX / Flax
    7. OpenAI Triton

    AI recommended 7 alternatives but never named wyf3/llm_related. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    warn

    Suggestion:

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 wyf3/llm_related?
    pass
    AI named wyf3/llm_related explicitly

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

  • If a team adopts wyf3/llm_related in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wyf3/llm_related 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 wyf3/llm_related solve, and who is the primary audience?
    pass
    AI named wyf3/llm_related explicitly

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

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wyf3/llm_related — 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