RRepoGEO

REPOGEO REPORT · LITE

mattmireles/gemma-tuner-multimodal

Default branch main · commit af5f2d6d · scanned 6/29/2026, 7:02:43 AM

GitHub: 1,482 stars · 104 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
22 /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
1 / 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 mattmireles/gemma-tuner-multimodal, 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 to the repository

    Why:

    COPY-PASTE FIX
    gemma, fine-tuning, multimodal, apple-silicon, pytorch, mps, llm, lora, machine-learning, deep-learning
  • highreadme#2
    Reposition the main README heading to clarify its role as a complete solution

    Why:

    CURRENT
    # Gemma Multimodal Fine-Tuner
    COPY-PASTE FIX
    # Gemma Multimodal Fine-Tuner: A complete solution for multimodal Gemma 4 & 3n fine-tuning on Apple Silicon
  • mediumhomepage#3
    Add the repository's GitHub URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/mattmireles/gemma-tuner-multimodal

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 mattmireles/gemma-tuner-multimodal
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
salesforce/LAVIS
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. salesforce/LAVIS · recommended 2×
  2. facebookresearch/llama · recommended 2×
  3. huggingface/transformers · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. huggingface/accelerate · recommended 1×
  • CATEGORY QUERY
    How to fine-tune multimodal language models with images and audio on a Mac?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch (pytorch/pytorch)
    3. accelerate (huggingface/accelerate)
    4. bitsandbytes (TimDettmers/bitsandbytes)
    5. peft (huggingface/peft)
    6. LLaVA (haotian-liu/LLaVA)
    7. BLIP-2 (salesforce/LAVIS)
    8. SeamlessM4T (facebookresearch/seamless_communication)
    9. Pillow (python-pillow/Pillow)
    10. soundfile (bastibe/python-soundfile)
    11. librosa (librosa/librosa)
    12. Whisper (openai/whisper)
    13. Google Colab
    14. Kaggle Notebooks
    15. InstructBLIP (salesforce/LAVIS)
    16. MiniGPT-4 (Vision-CAIR/MiniGPT-4)
    17. Qwen-VL (QwenLM/Qwen-VL)
    18. RunPod
    19. Vast.ai
    20. Lambda Labs
    21. CogVLM (THUDM/CogVLM)
    22. PyTorch Lightning (Lightning-AI/lightning)
    23. Keras (keras-team/keras)
    24. TensorFlow (tensorflow/tensorflow)
    25. ViT
    26. ResNet
    27. Wav2Vec2
    28. GPT-2 (openai/gpt-2)
    29. Llama (facebookresearch/llama)
    30. Llama 2 (facebookresearch/llama)
    31. Mistral (mistralai/mistral-src)
    32. MLX (ml-explore/mlx)

    AI recommended 32 alternatives but never named mattmireles/gemma-tuner-multimodal. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Efficiently fine-tune large language models on Apple Silicon without needing NVIDIA hardware?
    you: not recommended
    AI recommended (in order):
    1. MLX
    2. PyTorch
    3. Hugging Face Transformers
    4. LoRA
    5. bitsandbytes

    AI recommended 5 alternatives but never named mattmireles/gemma-tuner-multimodal. 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 mattmireles/gemma-tuner-multimodal?
    pass
    AI did not name mattmireles/gemma-tuner-multimodal — 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 mattmireles/gemma-tuner-multimodal in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mattmireles/gemma-tuner-multimodal 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 mattmireles/gemma-tuner-multimodal solve, and who is the primary audience?
    pass
    AI did not name mattmireles/gemma-tuner-multimodal — 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?

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mattmireles/gemma-tuner-multimodal — 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