Instructions to use alibidaran/General_image_captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alibidaran/General_image_captioning 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="alibidaran/General_image_captioning")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("alibidaran/General_image_captioning") model = AutoModelForMultimodalLM.from_pretrained("alibidaran/General_image_captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e9f32aa7ce87ffa9fad06ce89898840bd17c8de245a0e49f6b3952560445d64
- Size of remote file:
- 707 MB
- SHA256:
- c1d3890b98c3dc6e249e7ef46f52bff569caf685ee50ac8490550d0e83f5e169
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.