RRepoGEO

REPOGEO REPORT · LITE

ARahim3/mlx-tune

Default branch main · commit 9690fe1f · scanned 6/24/2026, 7:08:54 AM

GitHub: 1,323 stars · 85 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
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 ARahim3/mlx-tune, 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
    Reposition the README's main heading to explicitly state 'mlx-tune' is a specialized library

    Why:

    CURRENT
    <p align="center">
  <strong>Fine-tune LLMs, Vision, Audio, and OCR models on your Mac</strong><br>
  <em>SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.</em>
</p>
    COPY-PASTE FIX
    <p align="center">
  <strong>mlx-tune: The simplified library for fine-tuning LLMs, Vision, Audio, and OCR models on your Mac</strong><br>
  <em>A specialized tool for SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX with an Unsloth-compatible API.</em>
</p>
  • mediumabout#2
    Refine the 'About' description to emphasize 'mlx-tune' as a simplified library

    Why:

    CURRENT
    Fine-tune LLMs on your Mac with Apple Silicon. SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.
    COPY-PASTE FIX
    A simplified library for fine-tuning LLMs, Vision, Audio, and OCR models on your Mac with Apple Silicon. It offers SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning natively on MLX, with an Unsloth-compatible API.
  • lowreadme#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, perhaps titled 'Why mlx-tune? (Compared to MLX, PyTorch, or Hugging Face PEFT)', explaining how mlx-tune simplifies and streamlines the fine-tuning process specifically for Mac users compared to using these foundational tools directly.

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 ARahim3/mlx-tune
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. MPS Backend · recommended 1×
  3. huggingface/transformers · recommended 1×
  4. huggingface/peft · recommended 1×
  5. huggingface/accelerate · recommended 1×
  • CATEGORY QUERY
    How can I efficiently fine-tune large language models directly on my Apple Silicon Mac?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. MPS Backend
    3. Hugging Face Transformers (huggingface/transformers)
    4. PEFT (huggingface/peft)
    5. accelerate (huggingface/accelerate)
    6. MLX (ml-explore/mlx)
    7. llama.cpp (ggerganov/llama.cpp)
    8. Axolotl (OpenAccess-AI-Collective/axolotl)
    9. Unsloth (unsloth/unsloth)
    10. bitsandbytes (TimDettmers/bitsandbytes)

    AI recommended 10 alternatives but never named ARahim3/mlx-tune. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools enable local fine-tuning of vision, audio, and language models on macOS?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch
    3. Keras
    4. TensorFlow
    5. MLX
    6. JAX
    7. Core ML Tools

    AI recommended 7 alternatives but never named ARahim3/mlx-tune. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 ARahim3/mlx-tune?
    pass
    AI did not name ARahim3/mlx-tune — 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 ARahim3/mlx-tune in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ARahim3/mlx-tune 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 ARahim3/mlx-tune solve, and who is the primary audience?
    pass
    AI named ARahim3/mlx-tune explicitly

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

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ARahim3/mlx-tune — 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