RRepoGEO

REPOGEO REPORT · LITE

kuleshov-group/llmtools

Default branch main · commit 0ec1d280 · scanned 6/4/2026, 12:27:45 PM

GitHub: 733 stars · 77 forks

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 kuleshov-group/llmtools, 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 specific topics for LLM finetuning and quantization

    Why:

    COPY-PASTE FIX
    llm-finetuning, quantization, low-precision, consumer-gpu, llm, machine-learning, deep-learning, pytorch, lora, qlora, modulo-lora, quip
  • highlicense#2
    Add a LICENSE file or clarify license in README

    Why:

    COPY-PASTE FIX
    Add a `LICENSE` file (e.g., MIT, Apache-2.0) to the repository root, or explicitly state the applicable license(s) in the README.
  • mediumreadme#3
    Strengthen README's opening to emphasize 2-bit finetuning on consumer GPUs

    Why:

    CURRENT
    LLMTools is a user-friendly library for running and finetuning LLMs in low-resource settings. Features include:
    COPY-PASTE FIX
    LLMTools is a cutting-edge library specifically designed for **2-bit, 3-bit, and 4-bit finetuning of Large Language Models on a single consumer GPU**, making advanced LLM research and deployment accessible in low-resource settings. Key features include:

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 kuleshov-group/llmtools
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
QLoRA
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. QLoRA · recommended 2×
  2. LoRA · recommended 1×
  3. TimDettmers/bitsandbytes · recommended 1×
  4. huggingface/peft · recommended 1×
  5. microsoft/DeepSpeed · recommended 1×
  • CATEGORY QUERY
    How can I finetune large language models efficiently on a single consumer GPU?
    you: not recommended
    AI recommended (in order):
    1. QLoRA
    2. LoRA
    3. bitsandbytes (TimDettmers/bitsandbytes)
    4. Hugging Face `peft` library (huggingface/peft)
    5. DeepSpeed Zero Redundancy Optimizer (ZeRO) (microsoft/DeepSpeed)
    6. FlashAttention
    7. xFormers (facebookresearch/xformers)

    AI recommended 7 alternatives but never named kuleshov-group/llmtools. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools enable low-precision quantization for finetuning LLMs on resource-constrained devices?
    you: not recommended
    AI recommended (in order):
    1. QLoRA
    2. bitsandbytes
    3. PEFT
    4. AWQ
    5. GPTQ
    6. ONNX Runtime

    AI recommended 6 alternatives but never named kuleshov-group/llmtools. 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 kuleshov-group/llmtools?
    pass
    AI named kuleshov-group/llmtools explicitly

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

  • If a team adopts kuleshov-group/llmtools in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kuleshov-group/llmtools 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 kuleshov-group/llmtools solve, and who is the primary audience?
    pass
    AI named kuleshov-group/llmtools 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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kuleshov-group/llmtools — 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