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:
- 69bc2e3f26bb2dbf23eb8d81b2a0033832d0e09c8c22a5fa3d624f33af6d98ae
- Size of remote file:
- 5.11 kB
- SHA256:
- c59c2c6fed3462bfa31726a4bc20d8bc0b5bfee4a292b451105e334219f94819
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