Instructions to use whatdhack/mt5-small-finetuned-amazon-en-es-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whatdhack/mt5-small-finetuned-amazon-en-es-1 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("whatdhack/mt5-small-finetuned-amazon-en-es-1") model = AutoModelForSeq2SeqLM.from_pretrained("whatdhack/mt5-small-finetuned-amazon-en-es-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from whatdhack/mt5-small-finetuned-amazon-en-es-1: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/whatdhack/mt5-small-finetuned-amazon-en-es-1/resolve/af0311be6996c21187d9ea28db3e8a03ad953a43/pytorch_model.bin
- Command line
-
hf download hf://whatdhack/mt5-small-finetuned-amazon-en-es-1@af0311be6996c21187d9ea28db3e8a03ad953a43/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/whatdhack/mt5-small-finetuned-amazon-en-es-1/resolve/af0311be6996c21187d9ea28db3e8a03ad953a43/pytorch_model.bin
1.2 GB
- Xet hash:
- b3f3b59ad46f5212bcdc699c941a1827673ac86dc4ac255da2e8968f1cc0a8b4
- Size of remote file:
- 1.2 GB
- SHA256:
- 2468264674523e9c2a00b2972337806aa108ae05577234f678eea831f7150af3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.