maestro

Maestro

Maestro is a streamlined tool designed to accelerate the fine-tuning of multimodal models, encapsulating best practices from core modules. It handles configuration, data loading, reproducibility, and training loop setup, making it easier for developers to work with popular vision-language models such as Florence-2, PaliGemma 2, and Qwen2.5-VL.

Key Features:

• Easy Installation: Install model-specific dependencies with ease.
• Command-Line Interface: Kick off fine-tuning using a simple CLI, specifying key parameters like dataset location, epochs, batch size, and optimization strategy.
• Python API: For greater control, use the Python API to fine-tune models with customizable configurations.
• Support for Multiple Models: Ready-to-use recipes for various vision-language models.

Benefits:

• Accelerated Development: Streamlines the fine-tuning process, saving time and effort.
• Reproducibility: Ensures consistent results across different training sessions.
• Community Contributions: Open to feedback and contributions, fostering a collaborative environment.

Highlights:

• Supports models like Florence-2, PaliGemma 2, and Qwen2.5-VL.
• Offers a single CLI/SDK to reduce code complexity.
• Consistent JSONL format for streamlined data handling.