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_5000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.99 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/b5078e79f1d29e02785072065a9c061cdaec29e0/satori_tiny_checkpoints/step_lightning_5000.pt
- Command line
-
hf download hf://dilip025/dummy-model@b5078e79f1d29e02785072065a9c061cdaec29e0/satori_tiny_checkpoints/step_lightning_5000.pt
-
curl -L -o step_lightning_5000.pt https://huggingface.co/dilip025/dummy-model/resolve/b5078e79f1d29e02785072065a9c061cdaec29e0/satori_tiny_checkpoints/step_lightning_5000.pt
3.99 GB
- Xet hash:
- 67b9fabf1a80c7b7a4eae240cf175b3bcec0039cfcc43b2c9310bc41f96b3649
- Size of remote file:
- 3.99 GB
- SHA256:
- e5020bedccff4709dbb56eb3dea3e9ef9cbf448d03610120569c1ca69e06cdc1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.