RRepoGEO

REPOGEO REPORT · LITE

lxe/simple-llm-finetuner

Default branch master · commit e3dce32c · scanned 5/24/2026, 2:32:42 PM

GitHub: 2,056 stars · 129 forks

AI VISIBILITY SCORE
28 /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
2 / 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 lxe/simple-llm-finetuner, 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
    Reorder README to describe project before 'dead' notice

    Why:

    CURRENT
    The current README places the '👻👻👻 This project is effectively dead...' section before the main project description.
    COPY-PASTE FIX
    Move the '👻👻👻 This project is effectively dead...' section to after the initial project description (e.g., after the 'Simple LLM Finetuner is a beginner-friendly interface...' paragraph).
  • mediumhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://huggingface.co/spaces/lxe/simple-llama-finetuner
  • lowreadme#3
    Add a clear heading for recommended alternatives

    Why:

    CURRENT
    The alternatives are listed directly under the 'project is dead' notice.
    COPY-PASTE FIX
    Add a heading like `## Recommended Alternatives` above the list of links to LLaMA-Factory, Unsloth, and oobabooga/text-generation-webui.

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 lxe/simple-llm-finetuner
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Axolotl
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Axolotl · recommended 1×
  2. Hugging Face `trl` · recommended 1×
  3. Unsloth · recommended 1×
  4. Hugging Face `peft` · recommended 1×
  5. oobabooga/text-generation-webui · recommended 1×
  • CATEGORY QUERY
    What are user-friendly tools for fine-tuning large language models on consumer GPUs?
    you: not recommended
    AI recommended (in order):
    1. Axolotl
    2. Hugging Face `trl`
    3. Unsloth
    4. Hugging Face `peft`
    5. `oobabooga/text-generation-webui` (oobabooga/text-generation-webui)

    AI recommended 5 alternatives but never named lxe/simple-llm-finetuner. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a simple graphical interface to apply LoRA finetuning to transformer models.
    you: not recommended
    AI recommended (in order):
    1. kohya_ss (kohya-ss/sd-scripts)
    2. Axolotl (OpenAccess-AI-Collective/axolotl)
    3. Hugging Face AutoTrain Advanced (huggingface/autotrain-advanced)
    4. LoRA Dreamer (derrian-distro/LoRA_Dreamer)
    5. RunPod
    6. Vast.ai
    7. Google Colab

    AI recommended 7 alternatives but never named lxe/simple-llm-finetuner. 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 lxe/simple-llm-finetuner?
    pass
    AI did not name lxe/simple-llm-finetuner — 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 lxe/simple-llm-finetuner in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lxe/simple-llm-finetuner 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 lxe/simple-llm-finetuner solve, and who is the primary audience?
    pass
    AI named lxe/simple-llm-finetuner explicitly

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

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lxe/simple-llm-finetuner — 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