mlx-patterns

What problem does it solve?

Developing with MLX on Apple Silicon can be tricky due to unique patterns like nested layer access, lazy evaluation, and GPU memory management. This Skill provides critical patterns and best practices to help you write efficient and error-free MLX code.

Core Features & Use Cases

• Nested Layer Access: Guides on correctly accessing model layers to avoid AttributeError.
• Lazy Evaluation Mastery: Explains when and how to use mx.eval() for explicit computation.
• GPU Memory Management: Essential patterns for clearing the Metal cache to prevent Out-of-Memory errors.
• Use Case: When training a large language model with MLX, this Skill reminds you to call mx.metal.clear_cache() after each batch, preventing GPU memory exhaustion and ensuring stable training runs.

Quick Start

Correctly access nested MLX model layers

layer_output = model.model.layerslayer_idx

Force evaluation after MLX operations

import mlx.core as mx

result = model(input_ids)

mx.eval(result)

Clear GPU cache after large operations

mx.metal.clear_cache()