# mlx

Guides Cursor to write high-performance Apple Silicon code with MLX, enforcing modular structure, unified memory usage, and explicit lazy evaluation.

- rule · `type=rule` · Cursor · ★ 3580
- Author: sanjeed5
- License: CC0-1.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/mlx.mdc
- Updated: 2026-05-19
- HTML: https://affpapa.org/en/nevibecode/rules/mlx

## What it does

This rule sets development standards for building machine learning workflows with Apple MLX in Python and Swift. It requires Cursor to split monolithic scripts into dedicated modules for datasets, `mlx.nn` architectures, and YAML configuration files instead of hardcoding parameters.

The guidance focuses on managing MLX lazy execution. Cursor uses `mx.compile()` on training steps and calls `mx.eval()` to force execution. Without explicit `mx.eval()` calls, computation graphs keep expanding in memory, causing unpredictable slowdowns.

Apple Silicon relies on unified memory. Because of this, device transfer patterns like `.to("gpu")` are omitted: arrays execute on the GPU by default. When CPU execution is needed, operations use `stream=mx.cpu`. The instructions also detail 4-bit weight quantization using a group size of 32 for efficient deployment.

## Install

### Cursor (native platform)

**Manually: put the files in place**
- File: `.cursor/rules/mlx.mdc` — Frontmatter: description, globs, alwaysApply

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/mlx.md` — The paths frontmatter field limits the files: move globs from the source into paths

### Codex

**AGENTS.md**
- Codex reads AGENTS.md: paste the rule text there

### GitHub Copilot

**Manually: put the files in place**
- File: `.github/instructions/mlx.instructions.md` — The applyTo field sets which files it applies to: move globs from the source into applyTo

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/rules/mlx.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/mlx.md`

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/mlx.mdc

## When to use it
- split MLX code into modular components and externalize hyperparameters into YAML
- prevent computation graph bloat by using `mx.compile` and explicit `mx.eval`
- eliminate redundant CPU-GPU memory copies by using unified memory
- apply 4-bit quantization to model weights before saving

## Example prompts
- Create a modular CNN in `mlx.nn` with hyperparameters loaded from `train_config.yaml`.
- Implement a training step with Adam, compile it with `mx.compile`, and evaluate the state via `mx.eval`.
- Quantize the model weights to 4-bit with group size 32 and export them to safetensors.

## Source text · mlx.mdc · CC0-1.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 44f6071d80282ef7.

BEGIN THIRD-PARTY FILE 44f6071d80282ef7
````text
---
description: This guide provides opinionated, actionable best practices for developing high-performance machine learning applications with MLX on Apple Silicon, focusing on Python and Swift.
globs: **/*
---
# mlx Best Practices

MLX is Apple's high-performance array framework for numerical computing and AI on Apple Silicon. Adhering to these guidelines ensures your MLX code is efficient, maintainable, and integrates seamlessly with Apple's ML ecosystem.

## 1. Code Organization and Structure

Organize your MLX projects into logical, reusable modules. Mirror the structure of official Apple examples (e.g., `corenet` MLX examples) for clarity and maintainability.

### 1.1 Modularize Components
Separate concerns into dedicated files: data loading, model definition, training loops, and utility functions.

❌ **BAD: Monolithic Script**
```python
# train.py
import mlx.core as mx
import mlx.nn as nn
# ... data loading, model definition, training loop all in one file ...
```

✅ **GOOD: Modular Structure**
```
my_project/
├── data/
│   └── mnist_loader.py
├── models/
│   └── simple_cnn.py
├── config/
│   └── train_config.yaml
├── utils/
│   └── metrics.py
└── train.py
```
```python
# models/simple_cnn.py
import mlx.core as mx
import mlx.nn as nn

class SimpleCNN(nn.Module):
    def __init__(self, num_classes: int):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.linear = nn.Linear(32 * 14 * 14, num_classes)

    def __call__(self, x: mx.array) -> mx.array:
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = x.reshape(x.shape[0], -1) # Flatten
        return self.linear(x)

# train.py
from data.mnist_loader import load_mnist_data
from models.simple_cnn import SimpleCNN
# ... training logic ...
```

### 1.2 Externalize Configuration
Store training parameters, model hyperparameters, and dataset paths in version-controlled YAML or JSON files.

❌ **BAD: Hardcoded Parameters**
```python
# train.py
learning_rate = 0.001
batch_size = 32
num_epochs = 10
model = SimpleCNN(num_classes=10)
```

✅ **GOOD: Config-Driven**
```yaml
# config/train_config.yaml
training:
  learning_rate: 0.001
  batch_size: 32
  num_epochs: 10
model:
  name: SimpleCNN
  num_classes: 10
data:
  path: "./data/mnist"
```
```python
# train.py
import yaml
from models.simple_cnn import SimpleCNN

with open("config/train_config.yaml", "r") as f:
    config = yaml.safe_load(f)

model = SimpleCNN(num_classes=config["model"]["num_classes"])
# ... use config["training"]["learning_rate"] etc.
```

## 2. Common Patterns and Anti-patterns

Leverage MLX's core features like lazy evaluation and unified memory for optimal performance.

### 2.1 Embrace Lazy Evaluation
MLX computations are lazy; they build a computation graph but execute only when results are explicitly needed. Use `mlx.eval()` or `mlx.compile()` to force execution.

❌ **BAD: Relying on Implicit Evaluation for Performance Critical Sections**
```python
# This loop might build a huge graph or trigger many small evaluations
# if intermediate results are printed or accessed.
for i in range(num_steps):
    loss, grads = value_and_grad(model, loss_fn)(model, X_batch, y_batch)
    # ... optimizer update ...
    # No explicit eval, performance can be unpredictable.
```

✅ **GOOD: Explicit Evaluation for Control**
Use `mlx.eval()` on the final outputs of a computation step to trigger execution efficiently. For static graphs, `mlx.compile()` offers further optimization.

```python
import mlx.core as mx
import mlx.nn as nn
from mlx.optimizers import Adam
````
END THIRD-PARTY FILE 44f6071d80282ef7

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