pmetal

Quick Start (Easy API)

import pmetal

---

## Installation

Prebuilt signed binaries are available on the [Releases](https://github.com/Epistates/pmetal/releases) page.

Crates are available on [crates.io](https://crates.io/crates/pmetal).

Build from source:

```bash
git clone https://github.com/epistates/pmetal.git && cd pmetal
cargo build --release          # CLI + TUI
cd crates/pmetal-gui && bun install && bun tauri build  # GUI (optional)

Hardware Support

PMetal automatically detects Apple Silicon capabilities at startup and tunes kernel parameters accordingly.

Auto-detected features: GPU family, device tier, core counts, memory bandwidth, dynamic caching, mesh shaders, NAX (M5+), UltraFusion topology (via sysctl hw.packages), ANE availability.

Tier-based kernel tuning: Matrix tile sizes, FlashAttention block sizes, fused kernel threadgroup sizes, and batch multipliers are automatically selected based on device tier (Base/Pro/Max/Ultra) and GPU family. See docs/hardware-support.md for the full tuning matrix.

Training Infrastructure

• Sequence Packing: Efficiently pack multiple sequences into single batches for 2-5x throughput. Enabled by default
• Gradient Checkpointing: Trade compute for memory on large models with configurable layer grouping
• Adaptive LR: EMA-based anomaly detection with spike recovery, plateau reduction, and divergence detection
• Callback System: TrainingCallback trait with lifecycle hooks (on_step_start, on_step_end, should_stop) for metrics logging, progress reporting, and clean cancellation
• Checkpoint Management: Save and resume training from checkpoints with best-loss rollback
• Tool/Function Calling: Chat templates with native tool definitions for Qwen, Llama 3.1+, Mistral v3+, and DeepSeek
• Schedule-Free Optimizer: Memory-efficient optimizer without learning rate schedules
• Metal Fused Optimizer: GPU-accelerated AdamW parameter updates
• 8-bit Adam: Memory-efficient optimizer for large models
• LoRA+: Differentiated learning rates for LoRA A and B matrices
• NEFTune: Noise-augmented fine-tuning for improved generation quality
• Distributed Training: mDNS auto-discovery, Ring All-Reduce with gradient compression

License

Licensed under either of MIT or Apache-2.0.