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_tienie_checkpoints/step_lightning_1200.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 2.26 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/57d1502d2d14cc46378e657659b8f137bae8a8d0/satori_tienie_checkpoints/step_lightning_1200.pt
- Command line
-
hf download hf://dilip025/dummy-model@57d1502d2d14cc46378e657659b8f137bae8a8d0/satori_tienie_checkpoints/step_lightning_1200.pt
-
curl -L -o step_lightning_1200.pt https://huggingface.co/dilip025/dummy-model/resolve/57d1502d2d14cc46378e657659b8f137bae8a8d0/satori_tienie_checkpoints/step_lightning_1200.pt
2.26 GB
- Xet hash:
- 10fcf4799b4c633b57378020872615b19ca646d04cd583137db7d7a1ac430864
- Size of remote file:
- 2.26 GB
- SHA256:
- bb94ba88d0b2585e6092c43aa9eb4c5640ae1156d4065116a655ad5f3f5a2e84
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