roboflow/maestro

Health v2 · maintenance only

Adoption not part of the score

2694 stars · 222 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

maestro is a Python library from Roboflow that streamlines fine-tuning of multimodal vision-language models such as Florence-2, PaliGemma 2, and Qwen2.5-VL. It encapsulates configuration, data loading, reproducibility, and training loop setup into ready-to-use recipes with LoRA/QLoRA support.

Use cases

• fine-tune a vision-language model for object detection
• train Florence-2 on custom captioning data
• fine-tune Qwen2.5-VL to extract JSON from images
• adapt PaliGemma 2 for visual question answering
• run LoRA fine-tuning of VLMs on a free Colab GPU
• fine-tune a multimodal model without writing training loop code

When to choose

• you want a simple, recipe-driven way to fine-tune supported VLMs like Florence-2, PaliGemma 2, or Qwen2.5-VL
• you need LoRA/QLoRA fine-tuning for vision-language tasks like detection, captioning, VQA, or JSON extraction
• you prefer best-practice defaults over hand-rolling a training pipeline

When to avoid

• you need to fine-tune models outside the supported list (Florence-2, PaliGemma 2, Qwen2.5-VL, Phi-3-vision)
• you require full control over every aspect of the training loop or custom architectures
• you need text-only LLM fine-tuning with no vision component

Facets

library · maturity active

llm-training machine-learning deep-learning image-processing cli machine-learning deep-learning computer-vision artificial-intelligence large-language-models python cli fine-tuning vision-language-models lora qlora paligemma florence-2 qwen2-vl multimodal object-detection vqa image-captioning transformers gpu

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markdown · JSON · MCP: product_card(name="roboflow/maestro")