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class GLU: Module, UnaryLayer {
  let dim: Int

  init(dim: Int) {
    self.dim = dim
  }

  func callAsFunction(_ x: MLXArray) -> MLXArray {
    let (a, b) = x.split(axis: dim)
    return a * MLXNN.sigmoid(b)

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Setup

Install packages:

pip install open-webui mlx-lm

Start Open WebUI server:

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Making MLX Go Fast

This guide assumes you have some familiarity with MLX and want to make your MLX model or algorithm as efficient as possible.

The guide covers the following:

• Graph Evaluation
• Type Promotion
• Operations

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Test Time Scaling with MLX LM and R1-based LLMs

Install MLX LM:

pip install mlx-lm

And run:

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Setup

On every machine in the cluster install openmpi and mlx-lm:

conda install conda-forge::openmpi
pip install -U mlx-lm

Next download the pipeline parallel run script. Download it to the same path on every machine:

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cmake_minimum_required(VERSION 3.27)

project(example LANGUAGES CXX)

set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)

find_package(
  Python 3.9
  COMPONENTS Interpreter Development.Module

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import argparse
from functools import partial
import multiprocessing as mp
from typing import Callable, Optional


import mlx.core as mx
import mlx.nn as nn
from mlx.utils import tree_map_with_path
from mlx_lm.utils import *

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This guide is adapted from this original post by Christopher Charles.

Project Setup

1. Get the source:

• Clone the MLX Swift Examples GitHub repository:

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from huggingface_hub import snapshot_download
import mlx.core as mx
import mlx.nn as nn
import time


class Block(nn.Module):
    def __init__(self, in_dims, dims, stride=1):
        super().__init__()