Image-to-Text
Transformers
ONNX
florence2
image-text-to-text
florence-2
vision-language
image-captioning
ocr
object-detection
int8
quantized
Instructions to use Heliosoph/florence-2-base-ft-quantized-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Heliosoph/florence-2-base-ft-quantized-onnx with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="Heliosoph/florence-2-base-ft-quantized-onnx")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Heliosoph/florence-2-base-ft-quantized-onnx") model = AutoModelForImageTextToText.from_pretrained("Heliosoph/florence-2-base-ft-quantized-onnx") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7610e426dbe4c27e59f182ca2559803fb869d3225cb321b721e373f85e6b8ef8
- Size of remote file:
- 93.7 MB
- SHA256:
- 3b79d54f23f666f731549db23cb070c35a979ce19cbd9720e90e67a78dc9768c
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