Instructions to use north/t5_base_NCC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use north/t5_base_NCC with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("north/t5_base_NCC") model = AutoModelForSeq2SeqLM.from_pretrained("north/t5_base_NCC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from north/t5_base_NCC: direct link, hf CLI and curl.
- Browser
- Download file 2.33 GB
-
https://huggingface.co/north/t5_base_NCC/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://north/t5_base_NCC/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/north/t5_base_NCC/resolve/main/flax_model.msgpack
2.33 GB
- Xet hash:
- 21e143c5965368e9a78d43c8ff5708bef4c1beadde0278db978ac6b9609d9376
- Size of remote file:
- 2.33 GB
- SHA256:
- bf2aa12e9aff5e8ab5f8a4acc72191cfc1c16d62f1e7c857c6582f58d6266c9d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.