Instructions to use dilip025/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dilip025/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dilip025/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dilip025/dummy-model") model = AutoModelForMaskedLM.from_pretrained("dilip025/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download satori_small_checkpoints/step_lightning_11900.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/bbf8ed253f162fd7a66199b38894d5433daeb079/satori_small_checkpoints/step_lightning_11900.pt
- Command line
-
hf download hf://dilip025/dummy-model@bbf8ed253f162fd7a66199b38894d5433daeb079/satori_small_checkpoints/step_lightning_11900.pt
-
curl -L -o step_lightning_11900.pt https://huggingface.co/dilip025/dummy-model/resolve/bbf8ed253f162fd7a66199b38894d5433daeb079/satori_small_checkpoints/step_lightning_11900.pt
6.29 GB
- Xet hash:
- 5d19a7a4f1c48ec5b0a2b950340f199faa515ca7070dc439d9fc98865306f240
- Size of remote file:
- 6.29 GB
- SHA256:
- 5ce05db122184930d41223f2c70878626a1e901e82c1a5fcdfbb052b3e2aafb7
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