Instructions to use hf-internal-testing/tiny-random-MT5EncoderModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MT5EncoderModel with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MT5EncoderModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-MT5EncoderModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-MT5EncoderModel: direct link, hf CLI and curl.
- Browser
- Download file 32.1 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-MT5EncoderModel/resolve/refs%2Fpr%2F5/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-MT5EncoderModel@refs/pr/5/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-MT5EncoderModel/resolve/refs%2Fpr%2F5/model.safetensors
32.1 MB
- Xet hash:
- 47903cba00612764c4ffe968e5a85350a2c2dac044a654cd83eb780194b9e48a
- Size of remote file:
- 32.1 MB
- SHA256:
- 95f6a6e32c3705f21388493ceaf261ecac578c7c73303e997754f2909a881d2c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.