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PMetal: high-performance Apple Silicon framework for local LLM inference, LoRA/QLoRA fine-tuning, serving, quantization, and MLX/Metal acceleration.
| Date | Stars |
|---|---|
| 2026-07-31 | 306 |
| 2026-08-02 | 306 |
| 2026-08-06 | 306 |
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[](https://crates.io/crates/pmetal) [](https://www.rust-lang.org) [](LICENSE) [](https://www.apple.com/macos) # PMetal **Powdered Metal** — An ML SDK, framework, and application suite for Apple Silicon, written in Rust. 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. Ship fine-tuned models without leaving the Apple ecosystem. ## Use PMetal Your Way ### Desktop GUI <img src="public/pmetal_gui.png" alt="pmetal screenshot showing GUI" style="width: 100%; max-width: 100%; margin: 20px 0;"/> A full Tauri + Svelte desktop application for visual model management, training, and inference. ```bash cd crates/pmetal-gui bun install && bun tauri dev ``` 10 pages: Dashboard, Models, Datasets, Training, Distillation, GRPO, Inference, Merging, Quantize, and Settings. Download models from HuggingFace, configure LoRA training with live loss metrics, chat with models, merge weights, and quantize — all from the GUI. Training runs in-process with real-time progress updates. ### Terminal TUI <img src="public/pmetal_tui.png" alt="pmetal screenshot showing TUI" style="width: 100%; max-width: 100%; margin: 20px 0;"/> A full-featured terminal control center with 9 tabs. ```bash pmetal tui ``` | Tab | Description | |-----|-------------| | **Dashboard** | Live loss curves (braille), LR schedule, throughput sparklines, timing breakdown gauges | | **Device** | GPU/ANE info, Metal feature detection, memory gauge, kernel tuning, UltraFusion topology | | **Models** | Browse cached models, HuggingFace Hub search (`S`), memory fit estimation, download | | **Datasets** | Scan and preview local datasets (JSONL, Parquet, CSV) with line counts | | **Training** | Configure and launch SFT/LoRA/QLoRA training runs with sectioned parameter forms | | **Distillation** | Configure knowledge distillation (online, offline, progressive) | | **GRPO** | Configure GRPO/DAPO reasoning training with reward functions and sampling params | | **Inference** | Interactive chat interface with markdown rendering and generation settings sidebar | | **Jobs** | Training run history with log viewer, status tracking, and metadata | Keybindings: `Tab`/`Shift+Tab` to switch tabs, `Alt+1-9` for direct access, `L` to adjust learning rate mid-run, `q` to quit. ### CLI ```bash # LoRA fine-tuning with sequence packing (default) pmetal train \ --model Qwen/Qwen3-0.6B \ --dataset train.jsonl \ --output ./output \ --lora-r 16 --batch-size 4 --learning-rate 2e-4 # Inference with LoRA adapter pmetal infer \ --model Qwen/Qwen3-0.6B \ --lora ./output/lora_weights.safetensors \ --prompt "Explain quantum entanglement" \ --chat --show-thinking # Knowledge distillation pmetal distill \ --teacher Qwen/Qwen3-4B \ --student Qwen/Qwen3.5-0.8B-Base \ --dataset train.jsonl # GRPO reasoning training pmetal grpo \ --model Qwen/Qwen3-0.6B \ --dataset reasoning.jsonl \ --reasoning-rewards # HuggingFace model search with memory fit pmetal search "qwen 0.6b" --detailed # Merge models with SLERP pmetal merge \ --models model-a model-b \ --method slerp --t 0.5 # Quantize to GGUF pmetal quantize \ --model ./output \ --output model.gguf --type q4km # Fuse LoRA into base model pmetal fuse \ --model Qwen/Qwen3-0.6B \ --lora ./output/lora_weights.safetensors # Evaluate perplexity pmetal eval \ --model Qwen/Qwen3-0.6B \ --dataset eval.jsonl # Start OpenAI-compatible server (requires --features serve) pmetal serve --model Qwen/Qwen3-0.6B --port 8080 ``` #### All CLI Commands | Command | Description | |---------|-------------| | `train` | Fine-tune w
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:182aa497bbe8e3c4, topic:fine-tuning, topic:lora, topic:qlora