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_19500.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.35 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/b0cabf8afeef7eb6b72f215e699e99e655515084/satori_v2/step_lightning_19500.pt
- Command line
-
hf download hf://dilip025/dummy-model@b0cabf8afeef7eb6b72f215e699e99e655515084/satori_v2/step_lightning_19500.pt
-
curl -L -o step_lightning_19500.pt https://huggingface.co/dilip025/dummy-model/resolve/b0cabf8afeef7eb6b72f215e699e99e655515084/satori_v2/step_lightning_19500.pt
4.35 GB
- Xet hash:
- 73871fafdc0a1c1fddefc776c557ba4bbda62ec55c43dbaf0582d6e75023cdc9
- Size of remote file:
- 4.35 GB
- SHA256:
- df1aa55e2fde7196b852e6926a7c5908b108b3016e3041ff182b3e9387335b21
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.