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_medium_checkpoints/step_lightning_2000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.57 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/main/satori_medium_checkpoints/step_lightning_2000.pt
- Command line
-
hf download hf://dilip025/dummy-model/satori_medium_checkpoints/step_lightning_2000.pt
-
curl -L -o step_lightning_2000.pt https://huggingface.co/dilip025/dummy-model/resolve/main/satori_medium_checkpoints/step_lightning_2000.pt
3.57 GB
- Xet hash:
- fd0a649523aefb6cfac40f6d3f69a19eff5d0100860993b5fab716e743225036
- Size of remote file:
- 3.57 GB
- SHA256:
- 9644dd8c162b7b1f798c480b11a2d7494909abab001b14ac650bd98a7258692e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.