Instructions to use google/pix2struct-widget-captioning-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/pix2struct-widget-captioning-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="google/pix2struct-widget-captioning-base")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("google/pix2struct-widget-captioning-base") model = AutoModelForMultimodalLM.from_pretrained("google/pix2struct-widget-captioning-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download spiece.model from google/pix2struct-widget-captioning-base: direct link, hf CLI and curl.
- Browser
- Download file 851 kB
-
https://huggingface.co/google/pix2struct-widget-captioning-base/resolve/main/spiece.model
- Command line
-
hf download hf://google/pix2struct-widget-captioning-base/spiece.model
-
curl -L -o spiece.model https://huggingface.co/google/pix2struct-widget-captioning-base/resolve/main/spiece.model
851 kB
- Xet hash:
- 41159b3239a5325e41944e1e687047c54b422d9f3a0839da86d07beb93ac1e71
- Size of remote file:
- 851 kB
- SHA256:
- 7fd650335add59bed55a432186ca0437a09e185c2d241faab468a538fe6bcf94
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.