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_v2/step_lightning_6500.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 4.35 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/34e2cc6866811b97c022610e42523dedd39b7f5f/satori_v2/step_lightning_6500.pt
- Command line
-
hf download hf://dilip025/dummy-model@34e2cc6866811b97c022610e42523dedd39b7f5f/satori_v2/step_lightning_6500.pt
-
curl -L -o step_lightning_6500.pt https://huggingface.co/dilip025/dummy-model/resolve/34e2cc6866811b97c022610e42523dedd39b7f5f/satori_v2/step_lightning_6500.pt
4.35 GB
- Xet hash:
- f392bf321504f9f28e9df85edbd0cae8f9561c6d24bca439cf6b2327444ed6ce
- Size of remote file:
- 4.35 GB
- SHA256:
- 5e0a7ba46f18d9e3fc062dfd3e6813361eac2362eb181d08794418e6ed46d0c4
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