Instructions to use duarteocarmo/flan-t5-base-tigger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use duarteocarmo/flan-t5-base-tigger with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("duarteocarmo/flan-t5-base-tigger") model = AutoModelForSeq2SeqLM.from_pretrained("duarteocarmo/flan-t5-base-tigger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from duarteocarmo/flan-t5-base-tigger: direct link, hf CLI and curl.
- Browser
- Download file 990 MB
-
https://huggingface.co/duarteocarmo/flan-t5-base-tigger/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://duarteocarmo/flan-t5-base-tigger/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/duarteocarmo/flan-t5-base-tigger/resolve/main/pytorch_model.bin
990 MB
- Xet hash:
- 8061074e436a3eba53b827fc01b929d0a864e735e9da52ff8cab395832db7123
- Size of remote file:
- 990 MB
- SHA256:
- 07bc37580b3593f494ae9eb21900708ecb622b73757910e0752151b5b7f23e16
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.