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_lightning_5000.pt from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 6.29 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/343c4be184ce26ff57b5f696ecff0edcf5abd7ca/satori_small_checkpoints/step_lightning_5000.pt
- Command line
-
hf download hf://dilip025/dummy-model@343c4be184ce26ff57b5f696ecff0edcf5abd7ca/satori_small_checkpoints/step_lightning_5000.pt
-
curl -L -o step_lightning_5000.pt https://huggingface.co/dilip025/dummy-model/resolve/343c4be184ce26ff57b5f696ecff0edcf5abd7ca/satori_small_checkpoints/step_lightning_5000.pt
6.29 GB
- Xet hash:
- a3a9cad87c66454b57b58d67a9be898b0d08f0db0585fa0a296e7f50a8144c97
- Size of remote file:
- 6.29 GB
- SHA256:
- fd1cdca5d122356184019f0b68f9163757dca1b2fb660e984b521c4231398040
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