Instructions to use Evuv/mt5-small-torch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Evuv/mt5-small-torch with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Evuv/mt5-small-torch") model = AutoModelForSeq2SeqLM.from_pretrained("Evuv/mt5-small-torch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Evuv/mt5-small-torch: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/Evuv/mt5-small-torch/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Evuv/mt5-small-torch/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Evuv/mt5-small-torch/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- 98d92b69bfcfdecf10ef9b8c434a3530a59805475277a23c5af0235bf87e3915
- Size of remote file:
- 1.2 GB
- SHA256:
- 917a7819947811427a86b757f9160d53f00b96d0035f90e358ac91e96f679bc4
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