Instructions to use antoinelouis/camembert-L8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use antoinelouis/camembert-L8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="antoinelouis/camembert-L8")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("antoinelouis/camembert-L8") model = AutoModel.from_pretrained("antoinelouis/camembert-L8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 35abd29b2d2e02a0f7d15428b654754e85b93b30b41a22fbf0cbc6e1e1609b7d
- Size of remote file:
- 329 MB
- SHA256:
- 34d7ff51d7b47ee5aec30392db4f5436f9e0992679ffda0548f0f42291ebd082
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.