Instructions to use psxjp5/mt5-small_large_lr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use psxjp5/mt5-small_large_lr with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("psxjp5/mt5-small_large_lr") model = AutoModelForSeq2SeqLM.from_pretrained("psxjp5/mt5-small_large_lr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from psxjp5/mt5-small_large_lr: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/psxjp5/mt5-small_large_lr/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://psxjp5/mt5-small_large_lr/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/psxjp5/mt5-small_large_lr/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- d093c9a5ecdcea363c9ad851278cf25299e79fed1ef9a397a7adedc45dd19351
- Size of remote file:
- 1.2 GB
- SHA256:
- 054cb0957c50072776e0ae1e101d599f3325b534c5bb87c0eed68b2291b82e5d
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