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_tiny_checkpoints/step_lightning_450.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 1.64 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/670a35a7b351cdc5f26af908e9fea35bd5be76fe/satori_tiny_checkpoints/step_lightning_450.pt
- Command line
-
hf download hf://dilip025/dummy-model@670a35a7b351cdc5f26af908e9fea35bd5be76fe/satori_tiny_checkpoints/step_lightning_450.pt
-
curl -L -o step_lightning_450.pt https://huggingface.co/dilip025/dummy-model/resolve/670a35a7b351cdc5f26af908e9fea35bd5be76fe/satori_tiny_checkpoints/step_lightning_450.pt
1.64 GB
- Xet hash:
- 76b6c6d7bcb198de19255875c5deaf7e349ad5f5c733b7f953bc204265123e22
- Size of remote file:
- 1.64 GB
- SHA256:
- 02b6abe597b60e9398ac55ebbdc4230716ed43b0b8265eb7332931dfe7b01243
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