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
File size: 292 Bytes
319f5a0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"_from_model_config": true,
"bos_token_id": 0,
"decoder_start_token_id": 2,
"early_stopping": true,
"eos_token_id": 2,
"forced_bos_token_id": 0,
"forced_eos_token_id": 2,
"no_repeat_ngram_size": 3,
"num_beams": 3,
"pad_token_id": 1,
"transformers_version": "4.38.2"
}
|