RRepoGEO

REPOGEO REPORT · LITE

Zeyi-Lin/LLM-Finetune

Default branch main · commit c523c926 · scanned 6/3/2026, 10:17:50 PM

GitHub: 645 stars · 80 forks

AI VISIBILITY SCORE
23 /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
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 Zeyi-Lin/LLM-Finetune, 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
    Add a clear, concise purpose statement to the README's opening

    Why:

    CURRENT
    The README currently starts with links to experiment details after the H1.
    COPY-PASTE FIX
    Add a sentence like: 'This repository provides practical examples and scripts for instruction finetuning of large language models, specifically focusing on Qwen2-VL (multimodal), Qwen2, and GLM4 models for tasks like text classification and named entity recognition.' directly after the H1.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm-finetuning, large-language-models, qwen2, glm4, multimodal-llm, instruction-tuning, text-classification, named-entity-recognition
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    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 project's intended usage.

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 Zeyi-Lin/LLM-Finetune
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. keras-team/keras · recommended 1×
  3. tensorflow/hub · recommended 1×
  4. Lightning-AI/lightning · recommended 1×
  5. OpenAI API · recommended 1×
  • CATEGORY QUERY
    How can I finetune a large language model for custom text classification tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. Keras (keras-team/keras)
    3. TensorFlow Hub (tensorflow/hub)
    4. PyTorch Lightning (Lightning-AI/lightning)
    5. OpenAI API
    6. Google Cloud Vertex AI
    7. Amazon SageMaker

    AI recommended 7 alternatives but never named Zeyi-Lin/LLM-Finetune. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to perform instruction finetuning on multimodal language models.
    you: not recommended
    AI recommended (in order):
    1. LLaVA
    2. MiniGPT-4
    3. Flamingo
    4. OpenFlamingo
    5. BLIP-2
    6. InstructBLIP

    AI recommended 6 alternatives but never named Zeyi-Lin/LLM-Finetune. 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 Zeyi-Lin/LLM-Finetune?
    pass
    AI named Zeyi-Lin/LLM-Finetune explicitly

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

  • If a team adopts Zeyi-Lin/LLM-Finetune in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Zeyi-Lin/LLM-Finetune 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 Zeyi-Lin/LLM-Finetune solve, and who is the primary audience?
    pass
    AI did not name Zeyi-Lin/LLM-Finetune — 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

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Zeyi-Lin/LLM-Finetune — 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