RRepoGEO

REPOGEO REPORT · LITE

ml-explore/mlx-swift-lm

Default branch main · commit a47894a1 · scanned 6/4/2026, 6:57:12 PM

GitHub: 539 stars · 248 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 ml-explore/mlx-swift-lm, 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 comprehensive topics to improve categorization

    Why:

    COPY-PASTE FIX
    swift, mlx, llm, vlm, large-language-models, vision-language-models, on-device-ml, fine-tuning, quantized-models, apple-silicon, machine-learning, deep-learning
  • highreadme#2
    Clarify the README's opening sentence to emphasize its unique value

    Why:

    CURRENT
    MLX Swift LM is a Swift package to build tools and applications with large language models (LLMs) and vision language models (VLMs) in MLX Swift.
    COPY-PASTE FIX
    MLX Swift LM is the official Swift package for building on-device applications with large language models (LLMs) and vision language models (VLMs), leveraging Apple's MLX framework for optimized performance on Apple Silicon.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://your-project-homepage-url.com

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 ml-explore/mlx-swift-lm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Core ML
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Core ML · recommended 2×
  2. huggingface/transformers · recommended 1×
  3. huggingface/swift-transformers · recommended 1×
  4. huggingface/diffusers · recommended 1×
  5. ml-explore/mlx · recommended 1×
  • CATEGORY QUERY
    How to integrate large language models and vision models into Swift applications?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. Hugging Face Transformers (huggingface/transformers)
    3. swift-transformers (huggingface/swift-transformers)
    4. Hugging Face Diffusers (huggingface/diffusers)
    5. MLX (ml-explore/mlx)
    6. OpenAI API
    7. OpenAISwift (adamrushy/OpenAISwift)
    8. Google Cloud Vertex AI
    9. Azure OpenAI Service
    10. Azure AI Vision
    11. Vision Framework
    12. Natural Language Framework
    13. Firebase ML

    AI recommended 13 alternatives but never named ml-explore/mlx-swift-lm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Swift libraries support fine-tuning quantized LLMs for on-device deployment?
    you: not recommended
    AI recommended (in order):
    1. Core ML Tools
    2. Hugging Face Transformers
    3. bitsandbytes
    4. QLoRA
    5. Core ML
    6. Swift for TensorFlow
    7. Metal Performance Shaders (MPS) Graph
    8. MLX

    AI recommended 8 alternatives but never named ml-explore/mlx-swift-lm. 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 ml-explore/mlx-swift-lm?
    pass
    AI did not name ml-explore/mlx-swift-lm — 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 ml-explore/mlx-swift-lm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ml-explore/mlx-swift-lm 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 ml-explore/mlx-swift-lm solve, and who is the primary audience?
    pass
    AI named ml-explore/mlx-swift-lm explicitly

    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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ml-explore/mlx-swift-lm — 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