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_4000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/a4a814cce3854f06e00e7791950c2dced8c8f3f4/satori_small_checkpoints/step_lightning_4000.pt
- Command line
-
hf download hf://dilip025/dummy-model@a4a814cce3854f06e00e7791950c2dced8c8f3f4/satori_small_checkpoints/step_lightning_4000.pt
-
curl -L -o step_lightning_4000.pt https://huggingface.co/dilip025/dummy-model/resolve/a4a814cce3854f06e00e7791950c2dced8c8f3f4/satori_small_checkpoints/step_lightning_4000.pt
6.29 GB
- Xet hash:
- eba1a5a468c4fd03a4fbbe626f3eafb20096986a4c445d8c96a30e6df3433863
- Size of remote file:
- 6.29 GB
- SHA256:
- 333b894451d465c02cad050d60f9416cbeb5ea5f807247f2f39ee3b3fc7e3945
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