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Maestro

Maestro is a Python developer tool that streamlines fine-tuning multimodal models by handling configuration, data loading, reproducibility and training-loop setup. It provides ready-to-use recipes for PaliGemma 2, Florence-2 and Qwen2.5-VL, with CLI and Python interfaces.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Maestro — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Maestro when building a fine-tuning workflow for PaliGemma 2, Florence-2 or Qwen2.5-VL and you want its documented configuration, data-loading and training setup. Its examples cover JSON extraction and object detection; the object-detection recipes are identified as experimental.

Who it is for

  • Python developers adapting vision-language models to image-related tasks
  • Teams configuring fine-tuning workflows for supported models

Documented uses

  • Configure model fine-tuning with dataset location, epochs, batch size, optimization strategy and metrics.
  • Use model-specific recipes for tasks such as JSON data extraction or object detection.
  • Set up training through a command-line interface or Python API.
Before you choose

Requirements & limits

The repository identifies Maestro as open source under the Apache-2.0 license.

  • The documented recipes cover PaliGemma 2, Florence-2 and Qwen2.5-VL.
  • Model-specific dependencies can clash; the README recommends a dedicated Python environment for each model.
  • The repository’s object-detection recipes are marked experimental.
  • Examples use LoRA or QLoRA, with a README note that these approaches and graph freezing help keep hardware requirements in check.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up a PaliGemma 2 fine-tuning workflow

Install Maestro’s PaliGemma 2 dependencies and configure the documented training call.

Before you start

  • Python environment for the model-specific dependencies
  • A dataset location
  • PaliGemma 2 model dependencies installed with pip install "maestro[paligemma_2]"

Steps

  1. Create a dedicated Python environment for PaliGemma 2, as recommended in the README.

  2. Install the model-specific dependencies with the command below.

    Documented command
    pip install "maestro[paligemma_2]"

  3. Configure training with a dataset location, epochs, batch size, optimization strategy and metrics using the CLI or Python API shown in the README.

Expected outcome

A configured PaliGemma 2 fine-tuning workflow using Maestro’s documented CLI or Python interface.

Guide limitations

  • The README example provides configuration values but does not specify dataset preparation details or hardware requirements.
  • Model dependency requirements may conflict, which is why the README recommends separate environments.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
13 October 2026
Original website title
roboflow/maestro · Official homepage title · retained in its source language
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