Instructions to use cointegrated/SONAR-encoder-distilled-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cointegrated/SONAR-encoder-distilled-v0 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, M2M100Encoder tokenizer = AutoTokenizer.from_pretrained("cointegrated/SONAR-encoder-distilled-v0") model = M2M100Encoder.from_pretrained("cointegrated/SONAR-encoder-distilled-v0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cointegrated/SONAR-encoder-distilled-v0: direct link, hf CLI and curl.
- Browser
- Download file 1.35 GB
-
https://huggingface.co/cointegrated/SONAR-encoder-distilled-v0/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cointegrated/SONAR-encoder-distilled-v0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cointegrated/SONAR-encoder-distilled-v0/resolve/main/pytorch_model.bin
1.35 GB
- Xet hash:
- 2c076c6b1d9664b22263a9c0b6cd2f59b823ebc4c1f6c1d49f9c0d6a0b08bbb9
- Size of remote file:
- 1.35 GB
- SHA256:
- ac5f18eba60ec86f7673b92f6e8748f56219a016510296576ada11527357c104
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