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_small_checkpoints/step_final_small_30138.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/c2e0a39cfb2006f81fab42d021f60d7cc3d20ec3/satori_small_checkpoints/step_final_small_30138.pt
- Command line
-
hf download hf://dilip025/dummy-model@c2e0a39cfb2006f81fab42d021f60d7cc3d20ec3/satori_small_checkpoints/step_final_small_30138.pt
-
curl -L -o step_final_small_30138.pt https://huggingface.co/dilip025/dummy-model/resolve/c2e0a39cfb2006f81fab42d021f60d7cc3d20ec3/satori_small_checkpoints/step_final_small_30138.pt
6.29 GB
- Xet hash:
- cfb60df042f474e644ae1d4dcd764c3e04e3a9b5da4eff5d7cf2245552cde8d3
- Size of remote file:
- 6.29 GB
- SHA256:
- 931186635b68d81ef9abf50657d785e5df48ef4c0c764c4973d6d75cf1c86fdf
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