Instructions to use h2thez3/sam_busi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use h2thez3/sam_busi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="h2thez3/sam_busi")# Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("h2thez3/sam_busi") model = AutoModelForMaskGeneration.from_pretrained("h2thez3/sam_busi") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c521a20a6a1f052ccddbba6f52c0555d103fe95f9c5bcc2c1709ab22b995ca7
- Size of remote file:
- 375 MB
- SHA256:
- 6c5229a5e722980f4d6642d2c7f01cf2ff8cd0fc8aeb34856ff83656488e83c0
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