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_10000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.39 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/753e038cc4fa96e26366e932b22f8c524c1ed8a6/ir_checkpoints/step_lightning_10000.pt
- Command line
-
hf download hf://dilip025/dummy-model@753e038cc4fa96e26366e932b22f8c524c1ed8a6/ir_checkpoints/step_lightning_10000.pt
-
curl -L -o step_lightning_10000.pt https://huggingface.co/dilip025/dummy-model/resolve/753e038cc4fa96e26366e932b22f8c524c1ed8a6/ir_checkpoints/step_lightning_10000.pt
3.39 GB
- Xet hash:
- fb02f54d9137de33e0458c927f1a5aaa171cda12cd1821e7a48313e00f5340e8
- Size of remote file:
- 3.39 GB
- SHA256:
- c3f43eb881b2913638a6d76e361e6ab4742e5f1d5a8a234ffef86d9b82b15fd4
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