RRepoGEO

REPOGEO REPORT · LITE

km1994/LLMsNineStoryDemonTower

Default branch main · commit 3baf9100 · scanned 6/22/2026, 5:34:01 PM

GitHub: 2,165 stars · 207 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 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 km1994/LLMsNineStoryDemonTower, 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 improve categorization

    Why:

    COPY-PASTE FIX
    llms, large-language-models, nlp, deep-learning, ai, chatglm, llama, alpaca, vicuna, langchain, stable-diffusion, multimodal, fine-tuning, inference-acceleration, practical-guides
  • highlicense#2
    Add a LICENSE file to clarify usage terms

    Why:

    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 reflects the intended usage terms for the content and code.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., a project website, blog post, or main documentation page) to the 'Homepage' field in the repository settings.

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 km1994/LLMsNineStoryDemonTower
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 3×
  2. ggerganov/llama.cpp · recommended 2×
  3. pytorch/pytorch · recommended 2×
  4. OpenAI API · recommended 1×
  5. langchain-ai/langchain · recommended 1×
  • CATEGORY QUERY
    How can I practically apply different large language models for natural language processing tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. OpenAI API
    3. LangChain (langchain-ai/langchain)
    4. Google AI Studio / Gemini API
    5. SpaCy (explosion/spaCy)
    6. Llama.cpp / Ollama (ggerganov/llama.cpp)
    7. NLTK (nltk/nltk)

    AI recommended 7 alternatives but never named km1994/LLMsNineStoryDemonTower. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for fine-tuning or accelerating inference for large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face PEFT Library (huggingface/peft)
    2. Axolotl (OpenAccessAICollective/axolotl)
    3. PyTorch Quantization API (pytorch/pytorch)
    4. NVIDIA TensorRT (NVIDIA/TensorRT)
    5. Hugging Face Transformers Trainer (huggingface/transformers)
    6. DeepSpeed (microsoft/DeepSpeed)
    7. ONNX Runtime (microsoft/onnxruntime)
    8. llama.cpp (ggerganov/llama.cpp)
    9. PyTorch Pruning API (pytorch/pytorch)
    10. Hugging Face Transformers (huggingface/transformers)
    11. PaddlePaddle PaddleSlim (PaddlePaddle/PaddleSlim)
    12. OpenVINO (openvinotoolkit/openvino)
    13. vLLM (vllm-project/vllm)
    14. TGI (Text Generation Inference) by Hugging Face (huggingface/text-generation-inference)

    AI recommended 14 alternatives but never named km1994/LLMsNineStoryDemonTower. 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
    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 km1994/LLMsNineStoryDemonTower?
    pass
    AI named km1994/LLMsNineStoryDemonTower explicitly

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

  • If a team adopts km1994/LLMsNineStoryDemonTower in production, what risks or prerequisites should they evaluate first?
    pass
    AI named km1994/LLMsNineStoryDemonTower 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 km1994/LLMsNineStoryDemonTower solve, and who is the primary audience?
    pass
    AI named km1994/LLMsNineStoryDemonTower explicitly

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

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km1994/LLMsNineStoryDemonTower — 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