Instructions to use Shamima/diffusion_prompt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shamima/diffusion_prompt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Shamima/diffusion_prompt")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Shamima/diffusion_prompt") model = AutoModelForMaskedLM.from_pretrained("Shamima/diffusion_prompt") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2bf6c72a66dae428e3feff4f7af32a2069c4ef535bf942c83779518e82f89157
- Size of remote file:
- 329 MB
- SHA256:
- 6c76e21757c70e8291b478fc0419dcf1b3efe52a68a601c1d6d48edcded614e1
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