Instructions to use lukecarlate/Yang_Num with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lukecarlate/Yang_Num with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lukecarlate/Yang_Num")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lukecarlate/Yang_Num") model = AutoModelForMaskedLM.from_pretrained("lukecarlate/Yang_Num", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from lukecarlate/Yang_Num: direct link, hf CLI and curl.
- Browser
- Download file 712 kB
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https://huggingface.co/lukecarlate/Yang_Num/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
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hf download hf://lukecarlate/Yang_Num@refs/pr/1/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/lukecarlate/Yang_Num/resolve/refs%2Fpr%2F1/tokenizer.json
712 kB
File too large to display, you can check the raw version instead.