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roboflow/maestro

A streamlined tool to accelerate the fine-tuning process for multimodal models like Florence-2, PaliGemma 2, and Qwen2.5-VL.

Core Features

Accelerates fine-tuning of multimodal models (PaliGemma 2, Florence-2, Qwen2.5-VL).

Provides ready-to-use recipes and best practices for training.

Manages configuration, data loading, reproducibility, and training loops.

Supports efficient optimization strategies like LoRA, QLoRA, and graph freezing.

Offers both a command-line interface (CLI) and a Python API.

Quick Start

pip install "maestro[paligemma_2]"

Detailed Introduction

Maestro is an open-source framework designed to simplify and accelerate the fine-tuning of advanced multimodal models, including Florence-2, PaliGemma 2, and Qwen2.5-VL. It encapsulates best practices for machine learning workflows, handling complex aspects such as model configuration, data preparation, training loop setup, and ensuring reproducibility. By providing ready-to-use recipes and supporting efficient optimization techniques like LoRA and QLoRA, Maestro significantly reduces the code complexity and hardware requirements typically associated with training large vision-language models, making sophisticated AI model customization more accessible.