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_11000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/main/satori_small_checkpoints/step_lightning_11000.pt
- Command line
-
hf download hf://dilip025/dummy-model/satori_small_checkpoints/step_lightning_11000.pt
-
curl -L -o step_lightning_11000.pt https://huggingface.co/dilip025/dummy-model/resolve/main/satori_small_checkpoints/step_lightning_11000.pt
6.29 GB
- Xet hash:
- 32810e39c7d8de1b8fd2b254de35a8318cc3c78af8efb9e485647d432af22f27
- Size of remote file:
- 6.29 GB
- SHA256:
- 4a3a32dd49dfa22176e209ed85931b9acb25672d35ebd9941cafff35b21638a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.