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_16500.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.35 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/753e038cc4fa96e26366e932b22f8c524c1ed8a6/satori_v2/step_lightning_16500.pt
- Command line
-
hf download hf://dilip025/dummy-model@753e038cc4fa96e26366e932b22f8c524c1ed8a6/satori_v2/step_lightning_16500.pt
-
curl -L -o step_lightning_16500.pt https://huggingface.co/dilip025/dummy-model/resolve/753e038cc4fa96e26366e932b22f8c524c1ed8a6/satori_v2/step_lightning_16500.pt
4.35 GB
- Xet hash:
- 0cc4715e5a87b83b53d6d33e4110ab782aacefb5885f4520d6d43dad8eec55a9
- Size of remote file:
- 4.35 GB
- SHA256:
- 93987b78265e188736e51db22247462c99fa0d1dac2d7ef115f53de4593e1395
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.