Instructions to use magnustragardh/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use magnustragardh/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="magnustragardh/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("magnustragardh/dummy-model") model = AutoModelForMaskedLM.from_pretrained("magnustragardh/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sentencepiece.bpe.model from magnustragardh/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 811 kB
-
https://huggingface.co/magnustragardh/dummy-model/resolve/main/sentencepiece.bpe.model
- Command line
-
hf download hf://magnustragardh/dummy-model/sentencepiece.bpe.model
-
curl -L -o sentencepiece.bpe.model https://huggingface.co/magnustragardh/dummy-model/resolve/main/sentencepiece.bpe.model
811 kB
- Xet hash:
- 57d2beb07207a35b55bc6eedd16cd8c11992d807e423fa5a2fbc8eac5c7abde0
- Size of remote file:
- 811 kB
- SHA256:
- 988bc5a00281c6d210a5d34bd143d0363741a432fefe741bf71e61b1869d4314
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