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")# pip install -U transformers accelerate # 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_tiny_checkpoints/step_lightning_1200.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 1.64 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/8ed7f88baac94baa95af27ee7d9072eab2d759dc/satori_tiny_checkpoints/step_lightning_1200.pt
- Command line
-
hf download hf://dilip025/dummy-model@8ed7f88baac94baa95af27ee7d9072eab2d759dc/satori_tiny_checkpoints/step_lightning_1200.pt
-
curl -L -o step_lightning_1200.pt https://huggingface.co/dilip025/dummy-model/resolve/8ed7f88baac94baa95af27ee7d9072eab2d759dc/satori_tiny_checkpoints/step_lightning_1200.pt
1.64 GB
- Xet hash:
- bc2532fcb88d8c8356cafdbb90edafcfb7e9de8b57229520bce292fea4b25b11
- Size of remote file:
- 1.64 GB
- SHA256:
- 7306d98e92b0c721b1ac9f49b837421a229aa65b99ac608ae1bc1419ca5ad153
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.