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_18000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 2.81 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/8a143942e9b57543e9fd3cbe3dd68ccf31ca5459/satori_prajna_checkpoints/step_lightning_18000.pt
- Command line
-
hf download hf://dilip025/dummy-model@8a143942e9b57543e9fd3cbe3dd68ccf31ca5459/satori_prajna_checkpoints/step_lightning_18000.pt
-
curl -L -o step_lightning_18000.pt https://huggingface.co/dilip025/dummy-model/resolve/8a143942e9b57543e9fd3cbe3dd68ccf31ca5459/satori_prajna_checkpoints/step_lightning_18000.pt
2.81 GB
- Xet hash:
- 7e898937763c755919ea67b94e6cee93dfb4a20fe7593efeb6a3f9d530790714
- Size of remote file:
- 2.81 GB
- SHA256:
- 8538e8919ddf1ff676043155a52588d36dd18cec65b6144e428f9c3cc5dd51bb
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