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_v2/step_lightning_23000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.35 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/main/satori_v2/step_lightning_23000.pt
- Command line
-
hf download hf://dilip025/dummy-model/satori_v2/step_lightning_23000.pt
-
curl -L -o step_lightning_23000.pt https://huggingface.co/dilip025/dummy-model/resolve/main/satori_v2/step_lightning_23000.pt
4.35 GB
- Xet hash:
- f77fc2ef5b4a1c39c0de1906df822708325200d9fd4abd67c2ceffab44d41b51
- Size of remote file:
- 4.35 GB
- SHA256:
- 21a40669ae52da78a43205b0ce333b0b6004bbf28f78a352bb9c5f55ff10239d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.