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")# 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_lightning_10500.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/main/satori_small_checkpoints/step_lightning_10500.pt
- Command line
-
hf download hf://dilip025/dummy-model/satori_small_checkpoints/step_lightning_10500.pt
-
curl -L -o step_lightning_10500.pt https://huggingface.co/dilip025/dummy-model/resolve/main/satori_small_checkpoints/step_lightning_10500.pt
6.29 GB
- Xet hash:
- df8925036ee35c3fd7283e9b873aca0aea0bd98f6c44f2050eb7e5c2c140cefc
- Size of remote file:
- 6.29 GB
- SHA256:
- f9f733e0d908a340320f2c2b7e03b5c039d960dd2f700fa8c160857d6addb915
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