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:
- 11e94ffcb8be738aa7468abf5d22187d540260d932c688e0805631871ce77cad
- Size of remote file:
- 1.88 kB
- SHA256:
- 304f31fd9e5cf3f337533bb36b04b750b4b16afa122fe774fcc1a771f4e28fac
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