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_7000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.39 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/568f850dd799c1a52f6e2d8e67765269d1804cbc/ir_checkpoints/step_lightning_7000.pt
- Command line
-
hf download hf://dilip025/dummy-model@568f850dd799c1a52f6e2d8e67765269d1804cbc/ir_checkpoints/step_lightning_7000.pt
-
curl -L -o step_lightning_7000.pt https://huggingface.co/dilip025/dummy-model/resolve/568f850dd799c1a52f6e2d8e67765269d1804cbc/ir_checkpoints/step_lightning_7000.pt
3.39 GB
- Xet hash:
- beae31b14440f00aed54905c12d1e95440e3b8038cc913c2e90b5a1ea08ee8b6
- Size of remote file:
- 3.39 GB
- SHA256:
- 749b282ea0231a47f74f120a0cfeec493c6f241ed4954541e8003e0299095d04
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