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_tiny_checkpoints/step_final_small_2982.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 3.99 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/78e6f01c12a7a7f8d2d7a03bb07959b6e8c495cc/satori_tiny_checkpoints/step_final_small_2982.pt
- Command line
-
hf download hf://dilip025/dummy-model@78e6f01c12a7a7f8d2d7a03bb07959b6e8c495cc/satori_tiny_checkpoints/step_final_small_2982.pt
-
curl -L -o step_final_small_2982.pt https://huggingface.co/dilip025/dummy-model/resolve/78e6f01c12a7a7f8d2d7a03bb07959b6e8c495cc/satori_tiny_checkpoints/step_final_small_2982.pt
3.99 GB
- Xet hash:
- 3642d9cf0942145a6875c94267859ebc5cb8c733b1e5d60b3261c9c96247e560
- Size of remote file:
- 3.99 GB
- SHA256:
- 29bd5ee697dee7fe18281d97974287ae84758254c303fc7e463fbdc9343f257d
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