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pmetal-py

Python bindings for PMetal via PyO3.

Overview

This crate provides a cdylib Python extension module exposing PMetal's training, inference, and model management APIs to Python. Built with PyO3 and installable via maturin.

Installation

cd crates/pmetal-py
pip install maturin
maturin develop --release

Quick Start

Easy API

import pmetal

# Fine-tune with sensible defaults
result = pmetal.finetune(
    "Qwen/Qwen3-0.6B",
    "train.jsonl",
    lora_r=16,
    learning_rate=2e-4,
    epochs=3,
)
print(f"Loss: {result['final_loss']}, Steps: {result['total_steps']}")

# Inference
text = pmetal.infer("Qwen/Qwen3-0.6B", "What is 2+2?")
print(text)

Full Control

import pmetal

# Configure training
lora_config = pmetal.LoraConfig(r=16, alpha=32.0)
training_config = pmetal.TrainingConfig(
    learning_rate=2e-4,
    num_epochs=3,
    batch_size=4,
    max_seq_len=2048,
)

# Create and run trainer
trainer = pmetal.Trainer(
    model_id="Qwen/Qwen3-0.6B",
    lora_config=lora_config,
    training_config=training_config,
    dataset_path="train.jsonl",
)
trainer.add_callback(pmetal.ProgressCallback())
result = trainer.train()

# Load model for inference
model = pmetal.Model.load("Qwen/Qwen3-0.6B")
print(model.generate("Hello world", temperature=0.7))

API Reference

Module-Level Functions

Function	Description
`finetune(model_id, dataset_path, ...)`	Fine-tune with sensible defaults
`infer(model_id, prompt, ...)`	Run inference
`download_model(model_id, ...)`	Download from HuggingFace Hub
`download_file(url, ...)`	Download a file

Classes

Class	Description
`Model`	Model loading and inference (`Model.load()`, `model.generate()`)
`Trainer`	Training orchestration (`Trainer(...)`, `trainer.train()`)
`Tokenizer`	Tokenization (`Tokenizer.from_file()`)
`LoraConfig`	LoRA configuration (r, alpha, dropout, use_rslora, use_dora)
`TrainingConfig`	Training hyperparameters (learning_rate, batch_size, num_epochs, ...)
`GenerationConfig`	Generation parameters (max_tokens, temperature, top_k, top_p, ...)
`DataLoaderConfig`	Data loading parameters (batch_size, max_seq_len, shuffle, ...)
`ProgressCallback`	Progress bar callback
`LoggingCallback`	Logging callback
`MetricsJsonCallback`	JSONL metrics callback

Enums

Enum	Values
`Dtype`	Float32, Float16, BFloat16
`Quantization`	None, NF4, FP4, Int8
`LoraBias`	None, All, LoraOnly
`LrSchedulerType`	Constant, Linear, Cosine, CosineWithRestarts, Polynomial
`OptimizerType`	AdamW, Adam8bit, ScheduleFree
`DatasetFormat`	Auto, Simple, Alpaca, ShareGpt, OpenAi, Reasoning
`ModelArchitecture`	Llama, Qwen2, Qwen3, Gemma, Mistral, Phi, ...

Modules

Module	Description
`easy`	Top-level `finetune()` and `infer()` functions
`model`	`Model` class with `load()` and `generate()`
`trainer`	`Trainer` class with `train()` and callbacks
`config`	Configuration classes and enums
`callbacks`	Training callback classes
`tokenizer`	`Tokenizer` class
`hub`	HuggingFace Hub download functions
`array_bridge`	MLX array to Python conversion
`error`	Error handling and conversion

License

MIT OR Apache-2.0

pmetal-py 0.3.11