skills.rest
cavit99
cavit99COMMUNITY
Explore all skills by this author →
GitHub

mlx

Validate MLX and MLX-LM behavior against upstream sources on Apple silicon.

33|3|Updated Mar 3, 2025
Search Tags:#validation#mlx#upstream#patching#porting#mlx-lm
One-click install
npx skills add https://github.com/cavit99/mlx-LLM-cheatsheet --skill mlx-cavit99
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mlx
Source: https://github.com/cavit99/mlx-LLM-cheatsheet/tree/main/skills/mlx
Command: npx skills add https://github.com/cavit99/mlx-LLM-cheatsheet --skill mlx-cavit99

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mlx, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Use this skill to verify MLX and MLX-LM behavior against upstream source and runtime, enabling patching MLX-based repos, porting PyTorch/JAX code to MLX, and validating lazy evaluation, indexing, compilation, streams, and Metal kernel behavior on Apple silicon.

Core Features & Use Cases

  • Verify latest MLX and MLX-LM releases against upstream source.
  • Patch MLX-based repos, port PyTorch/JAX code, debug indexing, lazy evaluation, compilation, and stream behavior.
  • Validate local MLX model loading and generation in Apple silicon environments.
  • Use bundled helpers and validators (mlx_release_info.sh, mlx_probe.sh) to surface current facts locally.

Quick Start

Set the skill path and run the bundled runtime probe to confirm current MLX behavior.

Frequently Asked Questions about mlx

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I verify MLX behavior against upstream sources?▼

Validate MLX behavior against upstream sources by running bundled helpers like mlx_release_info.sh and mlx_probe.sh to surface current facts locally and ensure alignment with official releases.

What is the best way to port PyTorch or JAX code to MLX on Apple silicon?▼

Port PyTorch or JAX code to MLX on Apple silicon by verifying lazy evaluation, indexing, compilation, streams, and Metal kernel behavior against upstream MLX sources using bundled validation scripts.

How do I debug edge cases in MLX lazy evaluation and Metal kernel behavior?▼

Debug MLX lazy evaluation and Metal kernel edge cases by using runtime probes and minimal local validation from bundled scripts to check current behavior against official source references.

Does MLX-LM work with local model loading and generation on Apple silicon?▼

Yes, MLX-LM supports local model loading and generation on Apple silicon, and you can validate this behavior against upstream sources using bundled validators to ensure proper alignment with official releases.

Why does my MLX-based repo need patching after a new upstream release?▼

MLX-based repos need patching after new upstream releases when local behavior diverges from official sources; runtime probes and validators detect these mismatches in lazy evaluation, streams, and compilation.

Do I need numpy installed to validate MLX and MLX-LM behavior?▼

Yes, numpy is a required dependency alongside mlx to run the bundled scripts and validators that probe current runtime behavior and verify alignment with upstream sources.

Related Skills

Explore More Software Engineering Skills→
luqmannurhakimbazmanluqmannurhakimbazman

mlx-dev

Guide developers to write idiomatic MLX code on Apple Silicon.

Community
Advanced
schpetschpet
5

apple-container

Run Linux containers natively on macOS with Apple Silicon.

Community
Advanced
ml-exploreml-explore
2.0k

swift-mlx

Build and train neural networks with lazy evaluation and automatic differentiation on Apple Silicon.

Official
Intermediate
stelemansteleman
1

metal-kernel

Implement Metal kernels and host stubs for PyTorch operators on Apple Silicon.

Community
Advanced