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_medium_checkpoints/step_lightning_2600.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.57 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/74c5d58574f0b162d61db5e70069a0a0333efda7/satori_medium_checkpoints/step_lightning_2600.pt
- Command line
-
hf download hf://dilip025/dummy-model@74c5d58574f0b162d61db5e70069a0a0333efda7/satori_medium_checkpoints/step_lightning_2600.pt
-
curl -L -o step_lightning_2600.pt https://huggingface.co/dilip025/dummy-model/resolve/74c5d58574f0b162d61db5e70069a0a0333efda7/satori_medium_checkpoints/step_lightning_2600.pt
3.57 GB
- Xet hash:
- 991576765cb40c03f8a1af890d3578cf7622570b3bb3e6a36c6ace2f50d6c5a6
- Size of remote file:
- 3.57 GB
- SHA256:
- 21e9ca0d2f8d36e80b3f1e4c9ea814b59bfa0e5768b6295712d54c530dde4291
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