Instructions to use quincyqiang/tesla2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quincyqiang/tesla2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="quincyqiang/tesla2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("quincyqiang/tesla2") model = AutoModelForMaskedLM.from_pretrained("quincyqiang/tesla2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2f5548b630b971678b6b0e986a727db2a6c511892eae4596b8309bdc86f360c
- Size of remote file:
- 399 MB
- SHA256:
- 57f44b497c5b7bdfcc494c631791b68ab4733240d7fb7098137751a537327c7f
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