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 ir_checkpoints/step_lightning_9500.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.39 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/f4a42ca8468b524d75bbb694206b2f9390fcb040/ir_checkpoints/step_lightning_9500.pt
- Command line
-
hf download hf://dilip025/dummy-model@f4a42ca8468b524d75bbb694206b2f9390fcb040/ir_checkpoints/step_lightning_9500.pt
-
curl -L -o step_lightning_9500.pt https://huggingface.co/dilip025/dummy-model/resolve/f4a42ca8468b524d75bbb694206b2f9390fcb040/ir_checkpoints/step_lightning_9500.pt
3.39 GB
- Xet hash:
- 24355e0a71e7cf907e1f5a20182badb20eb97d2dd4755202773874ccb5ee012e
- Size of remote file:
- 3.39 GB
- SHA256:
- 429f992fdd02b89761dc1b39019960e62ebb62733dcec4d497fe96ae8b217eae
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