Instructions to use quincyqiang/chtesla3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quincyqiang/chtesla3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="quincyqiang/chtesla3")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("quincyqiang/chtesla3") model = AutoModelForMaskedLM.from_pretrained("quincyqiang/chtesla3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ab22834aa8b1a3e7b5abc57acfb991f27d2b74c448526f0f68cbaf3fa9ded738
- Size of remote file:
- 408 MB
- SHA256:
- ae73c3ba3eb93b865561f5bbf8de0dd9e35b2126ddd3c0b5738e61c775be6557
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