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 satori_small_checkpoints/step_final_small_5294.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/main/satori_small_checkpoints/step_final_small_5294.pt
- Command line
-
hf download hf://dilip025/dummy-model/satori_small_checkpoints/step_final_small_5294.pt
-
curl -L -o step_final_small_5294.pt https://huggingface.co/dilip025/dummy-model/resolve/main/satori_small_checkpoints/step_final_small_5294.pt
6.29 GB
- Xet hash:
- 850e7fee93a632c8b9594e35d21be63230c43c9133e6bd7e9c1dc99af2e2d2e7
- Size of remote file:
- 6.29 GB
- SHA256:
- 4667a40d8376d7c119a5d5fa334db927201c5ffaab9de0991f9b401daacb5fc3
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