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pmetal-lora

LoRA and QLoRA training implementations with Metal acceleration.

Overview

This crate provides efficient Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA) training for LLMs on Apple Silicon. It includes architecture-specific optimizations and a dynamic model system for seamless multi-architecture support.

Features

Standard LoRA: Low-rank adaptation with configurable rank and alpha
QLoRA: 4-bit quantized base weights with full-precision adapters
Dynamic Architecture: Auto-detect and load any supported model
Fused Training: Metal-accelerated forward/backward passes (~2x speedup)
Gradient Checkpointing: Memory-efficient training for large models
Sequence Packing: Efficient training on variable-length data

Usage

Basic LoRA Training

use pmetal_lora::{DynamicLoraModel, TrainableModel};
use pmetal_core::LoraConfig;

// Configure LoRA
let config = LoraConfig {
    r: 16,
    alpha: 16.0,
    dropout: 0.0,
    ..Default::default()
};

// Load model with LoRA adapters
let mut model = DynamicLoraModel::from_pretrained("path/to/model", config)?;

// Training loop
for batch in dataloader {
    let logits = model.forward(&batch.input_ids, None)?;
    // Compute loss and backprop...
}

// Save adapters
model.save_lora_weights("output/lora_weights.safetensors")?;

Loading Trained Adapters

// Load base model with LoRA structure
let mut model = DynamicLoraModel::from_pretrained("path/to/model", config)?;

// Load trained adapter weights
model.load_lora_weights("output/lora_weights.safetensors")?;

// Run inference
let logits = model.forward(&input_ids, None)?;

Architecture Support

Architecture	LoRA	QLoRA	Grad Checkpoint
Llama	Yes	Yes	Yes
Qwen	Yes	No	Yes
DeepSeek	Yes	No	Yes
Mistral	Yes	Yes	Yes
Gemma	Yes	No	Yes
Phi	Yes	No	Yes
GPT-OSS	Yes	No	No
Granite	Yes	No	Yes
Cohere	Yes	No	Yes

Configuration

Parameter	Description	Default
`r`	LoRA rank	8
`alpha`	Scaling factor	16.0
`dropout`	Dropout rate	0.0
`target_modules`	Modules to adapt	All attention + MLP

Modules

Module	Description
`dynamic`	`DynamicLoraModel` with auto-detection
`llama_lora`	LLaMA-specific LoRA implementation
`llama_qlora`	LLaMA QLoRA with 4-bit quantization
`qwen3_lora`	Qwen3-specific LoRA with grad checkpointing
`trainable`	`TrainableModel` trait definition
`fused_training`	Metal-accelerated training utilities

Performance

Compared to mlx-lm on identical hardware:

Metric	pmetal-lora	mlx-lm
Steps/sec	1.33	0.62
Memory	~10 GB	19 GB

License

MIT OR Apache-2.0

pmetal-lora 0.1.2