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 texts.json from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 2.07 GB
-
https://huggingface.co/dilip025/dummy-model/resolve/7b91afb29832525dd46d9ee32d9aa1a1cbfd30f0/texts.json
- Command line
-
hf download hf://dilip025/dummy-model@7b91afb29832525dd46d9ee32d9aa1a1cbfd30f0/texts.json
-
curl -L -o texts.json https://huggingface.co/dilip025/dummy-model/resolve/7b91afb29832525dd46d9ee32d9aa1a1cbfd30f0/texts.json
2.07 GB
- Xet hash:
- e7d3b2d82a79817800f1d132770b8a296e2f91d483e9ee26e4a4ea47f75bac3a
- Size of remote file:
- 2.07 GB
- SHA256:
- eda2d9542860e4b97f5dab4006ee898d5b7e6b11c0d2ff449113901f5d1acdd4
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