Instructions to use centaur31/m2m100-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use centaur31/m2m100-ONNX with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("centaur31/m2m100-ONNX") model = AutoModelForSeq2SeqLM.from_pretrained("centaur31/m2m100-ONNX", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download decoder_model.onnx from centaur31/m2m100-ONNX: direct link, hf CLI and curl.
- Browser
- Download file 1.86 GB
-
https://huggingface.co/centaur31/m2m100-ONNX/resolve/main/decoder_model.onnx
- Command line
-
hf download hf://centaur31/m2m100-ONNX/decoder_model.onnx
-
curl -L -o decoder_model.onnx https://huggingface.co/centaur31/m2m100-ONNX/resolve/main/decoder_model.onnx
1.86 GB
- Xet hash:
- f803d5426fb220a2583f6aa6a86fb3cd26f65f40f3c828647213eb9e98651cbe
- Size of remote file:
- 1.86 GB
- SHA256:
- 505ff63aa087e2699ac79aec3dcd5048c23c0c96041a500f626dfccc69b90a15
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.