Instructions to use Vikhrmodels/vikhr_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vikhrmodels/vikhr_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Vikhrmodels/vikhr_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Vikhrmodels/vikhr_encoder") model = AutoModel.from_pretrained("Vikhrmodels/vikhr_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b9c4ded0c212bd169db465be89d44ae648baa99b7b800101fc17df8cf108a79
- Size of remote file:
- 3.91 GB
- SHA256:
- e8a694626b59b658b2ebb2e1fef1f385f7e6a1f757c78e59e727484307e42235
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.