Instructions to use SLPL/t5-fa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SLPL/t5-fa with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SLPL/t5-fa") model = AutoModelForSeq2SeqLM.from_pretrained("SLPL/t5-fa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download opt_state.msgpack from SLPL/t5-fa: direct link, hf CLI and curl.
- Browser
- Download file 2.18 MB
-
https://huggingface.co/SLPL/t5-fa/resolve/main/opt_state.msgpack
- Command line
-
hf download hf://SLPL/t5-fa/opt_state.msgpack
-
curl -L -o opt_state.msgpack https://huggingface.co/SLPL/t5-fa/resolve/main/opt_state.msgpack
2.18 MB
- Xet hash:
- cbbe6b4e7573498a89db69e9b8db9f61ab245b4613bcc0405254b0179b389763
- Size of remote file:
- 2.18 MB
- SHA256:
- 6dfdd51123aea9901f6c3f3cbc6f1fab346c12c7b44c37eb9f6e40ebd36b0265
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.