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 ir_checkpoints/step_lightning_9000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.39 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/ed10108ac2b9d53434695278f66eaffe49f977af/ir_checkpoints/step_lightning_9000.pt
- Command line
-
hf download hf://dilip025/dummy-model@ed10108ac2b9d53434695278f66eaffe49f977af/ir_checkpoints/step_lightning_9000.pt
-
curl -L -o step_lightning_9000.pt https://huggingface.co/dilip025/dummy-model/resolve/ed10108ac2b9d53434695278f66eaffe49f977af/ir_checkpoints/step_lightning_9000.pt
3.39 GB
- Xet hash:
- 184de353ea162aaa6aefc8bc9e1d92f287edebb7a0ec775b0dd3910c0deb3dcf
- Size of remote file:
- 3.39 GB
- SHA256:
- 6eb71f82b4f97b06d4b7b0d23e553301cbac968906517bb36d7d023143566052
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