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_prajna_checkpoints/step_lightning_3000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 2.81 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/2336634a69ce3d41b12686a252cabda4883da02a/satori_prajna_checkpoints/step_lightning_3000.pt
- Command line
-
hf download hf://dilip025/dummy-model@2336634a69ce3d41b12686a252cabda4883da02a/satori_prajna_checkpoints/step_lightning_3000.pt
-
curl -L -o step_lightning_3000.pt https://huggingface.co/dilip025/dummy-model/resolve/2336634a69ce3d41b12686a252cabda4883da02a/satori_prajna_checkpoints/step_lightning_3000.pt
2.81 GB
- Xet hash:
- 2e54f54219e0defd4c7a8541f42c6d03ecb41deb83776ff998d6d2452af9fa99
- Size of remote file:
- 2.81 GB
- SHA256:
- c076866f28f8172cb81305f8d5da65655871d4f9a81db7a3e2a3771a6494c1b9
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