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

Of all named tools, what % are you?

Top rival

PyTorch

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

PyTorch · recommended 2×
MPS Backend · recommended 1×
huggingface/transformers · recommended 1×
huggingface/peft · recommended 1×
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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MARKDOWN (README)

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HTML

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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