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:
- f4227db317a7b07e0c001aba140b5da556b9cad329210d81cc99dd6184ec68f1
- Size of remote file:
- 2.29 kB
- SHA256:
- 5d70b8677e87771be20aa9f28f3de175be2f36566b878e01865c74f4cf65338d
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