Instructions to use Raghavan/beit3_base_patch16_480_coco_captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Raghavan/beit3_base_patch16_480_coco_captioning with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import Beit3ForCaptioning model = Beit3ForCaptioning.from_pretrained("Raghavan/beit3_base_patch16_480_coco_captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Raghavan/beit3_base_patch16_480_coco_captioning: direct link, hf CLI and curl.
- Browser
- Download file 1.08 GB
-
https://huggingface.co/Raghavan/beit3_base_patch16_480_coco_captioning/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Raghavan/beit3_base_patch16_480_coco_captioning/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Raghavan/beit3_base_patch16_480_coco_captioning/resolve/main/pytorch_model.bin
1.08 GB
- Xet hash:
- 4373a99a9105da0696bd57d829ceb91493a8e4e5e9eea1224edf61dc67d0ec30
- Size of remote file:
- 1.08 GB
- SHA256:
- 94e8ce761123de7d2160dd12024cf68400403b07d77af2a3de76eedce0c83bcc
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