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 satori_prajna_checkpoints/step_lightning_34000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 2.81 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/b9ce41b003b596a204f4383abf667a877c501fe9/satori_prajna_checkpoints/step_lightning_34000.pt
- Command line
-
hf download hf://dilip025/dummy-model@b9ce41b003b596a204f4383abf667a877c501fe9/satori_prajna_checkpoints/step_lightning_34000.pt
-
curl -L -o step_lightning_34000.pt https://huggingface.co/dilip025/dummy-model/resolve/b9ce41b003b596a204f4383abf667a877c501fe9/satori_prajna_checkpoints/step_lightning_34000.pt
2.81 GB
- Xet hash:
- 8efdb68bb89b2987fe2341fafd7d840d55134afac9a734deec4f51264acf87e3
- Size of remote file:
- 2.81 GB
- SHA256:
- 4306dd066b31b7fc00735ca2708286faf3d6c458a80d44897e09e9f68a45cd1e
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