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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Heliosoph/florence-2-base-ft-quantized-onnx") model = AutoModelForMultimodalLM.from_pretrained("Heliosoph/florence-2-base-ft-quantized-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download decoder_model_quantized.onnx from Heliosoph/florence-2-base-ft-quantized-onnx: direct link, hf CLI and curl.
- Browser
- Download file 97.8 MB
-
https://huggingface.co/Heliosoph/florence-2-base-ft-quantized-onnx/resolve/main/decoder_model_quantized.onnx
- Command line
-
hf download hf://Heliosoph/florence-2-base-ft-quantized-onnx/decoder_model_quantized.onnx
-
curl -L -o decoder_model_quantized.onnx https://huggingface.co/Heliosoph/florence-2-base-ft-quantized-onnx/resolve/main/decoder_model_quantized.onnx
97.8 MB
- Xet hash:
- 3f8d6312c643194366d3322835bc4b2b66f53f8769954b1e74060f4029ccbf09
- Size of remote file:
- 97.8 MB
- SHA256:
- c529b26bafce2ee76f886f3a0e374bb646b07a6d8b7640fd8a50d7a48843dd67
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