Instructions to use canwenxu/laprador with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use canwenxu/laprador with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="canwenxu/laprador")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("canwenxu/laprador") model = AutoModel.from_pretrained("canwenxu/laprador", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ecf663b21a2df60afad1ea3cdee020d2d6389aaa414962538da00da85ce76c0
- Size of remote file:
- 265 MB
- SHA256:
- 910bcae9b526e9f5f7176bc9c74ce699731b9c80673bf43cdac985c7641cb140
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