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_11800.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.99 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/57d1502d2d14cc46378e657659b8f137bae8a8d0/satori_tiny_checkpoints/step_lightning_11800.pt
- Command line
-
hf download hf://dilip025/dummy-model@57d1502d2d14cc46378e657659b8f137bae8a8d0/satori_tiny_checkpoints/step_lightning_11800.pt
-
curl -L -o step_lightning_11800.pt https://huggingface.co/dilip025/dummy-model/resolve/57d1502d2d14cc46378e657659b8f137bae8a8d0/satori_tiny_checkpoints/step_lightning_11800.pt
3.99 GB
- Xet hash:
- 81958897caadead4c421d6486562b14778777dce84aa8b3aa79f179b3eb45c6d
- Size of remote file:
- 3.99 GB
- SHA256:
- 270c4f4100bd3776824f12704b350c1b0b7cf2a1edb59764ef2513848b4f6e4d
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