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florence-2

florence-2

Use Maestro Florence-2 for fine-tuning command/API construction, checkpoint load/save, inference, object-detection formatting, LoRA/freeze/none choices, and troubleshooting.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: florence-2 description: "Use Maestro Florence-2 for fine-tuning command/API construction, checkpoint load/save, inference, object-detection formatting, LoRA/freeze/none choices, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Florence-2 sub-skill

Use this sub-skill when the task is specifically about Maestro's Florence-2 integration: maestro florence_2 train, the maestro.trainer.models.florence_2 Python APIs, Florence-2 checkpoint loading/saving, inference calls, or the Florence object-detection text format.

Start here

  1. Confirm the package and model-specific dependencies are available. For root install and CLI probing guidance, use installation and CLI.
  2. Confirm the data layout before building a training command. For JSONL/COCO split rules, Roboflow identifiers, and generic metric details, route to datasets-and-metrics.
  3. Build Florence-2 commands and API calls from workflows and exact signatures in API reference.
  4. For object detection, use the <OD> prefix and the Florence <loc_*> suffix rules in detection formats.
  5. If a CLI/API/data/model issue appears, check Florence-2 troubleshooting before changing code or broadening dependencies.

Critical defaults and boundaries

  • Default model id: microsoft/Florence-2-base-ft.
  • Default model revision: refs/pr/20.
  • Supported Florence-2 optimization strategies are exactly lora, freeze, and none. Do not use or recommend qlora for Florence-2 in Maestro.
  • Florence object detection uses prefix <OD> and suffix fragments of the form class<loc_xmin><loc_ymin><loc_xmax><loc_ymax> with coordinates normalized to the 0..1000 range.
  • This sub-skill owns Florence-specific collate, train, load/save, inference, and formatter decisions. It routes shared dataset validation, Roboflow downloads, COCO/JSONL layout, and metric semantics to datasets-and-metrics.
  • Full fine-tuning or model inference may download Hugging Face model files and usually needs a GPU. The bundled smoke script only checks deterministic text formatting and does not load a model.

Safe local checks

From the generated Maestro skill tree, run:

python sub-skills/florence-2/scripts/smoke_florence_detection_format.py --help
python sub-skills/florence-2/scripts/smoke_florence_detection_format.py --json

These checks import only Maestro's Florence detection formatter functions and NumPy. They do not open datasets, call Roboflow, contact Hugging Face, download weights, or start training.

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