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PMetal

Getting Started

PMetal is a complete machine learning platform for Apple Silicon — from low-level Metal GPU kernels and Apple Neural Engine integration to high-level training APIs, a terminal TUI, and a full desktop GUI.

Prerequisites

Section titled “Prerequisites”
  • macOS on Apple Silicon (M1 or later)
  • Rust 1.85+ (for building from source or using the SDK)
  • Xcode Command Line Tools: xcode-select --install

Quick Install

Section titled “Quick Install”
Terminal window
# Option 1: Prebuilt binary
curl -fsSL https://github.com/Epistates/pmetal/releases/latest/download/pmetal-aarch64-apple-darwin.tar.gz | tar xz
sudo mv pmetal /usr/local/bin/


# Option 2: Install from crates.io
cargo install pmetal

See Installation for all options including building from source and GUI setup.

Your First Training Run

Section titled “Your First Training Run”

Fine-tune a model with LoRA in one command:

Terminal window
pmetal train \
  --model Qwen/Qwen3-0.6B \
  --dataset train.jsonl \
  --output ./output \
  --lora-r 16 --batch-size 4 --learning-rate 2e-4

PMetal automatically downloads the model from HuggingFace Hub, detects your hardware capabilities, and tunes kernel parameters for your specific chip.

Run Inference

Section titled “Run Inference”

Chat with your fine-tuned model:

Terminal window
pmetal infer \
  --model Qwen/Qwen3-0.6B \
  --lora ./output/lora_weights.safetensors \
  --prompt "Explain quantum entanglement" \
  --chat --show-thinking

Use the SDK

Section titled “Use the SDK”

Integrate PMetal into your own Rust applications:

use pmetal::easy;


let result = easy::finetune("Qwen/Qwen3-0.6B", "train.jsonl")
    .lora(16, 32.0)
    .learning_rate(2e-4)
    .epochs(3)
    .output("./output")
    .run()
    .await?;

Or from Python:

import pmetal


result = pmetal.finetune(
    "Qwen/Qwen3-0.6B",
    "train.jsonl",
    lora_r=16,
    learning_rate=2e-4,
    epochs=3,
)

Explore Further

Section titled “Explore Further”