Instructions to use google/flan-t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/flan-t5-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base") model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/flan-t5-base: direct link, hf CLI and curl.
- Browser
- Download file 990 MB
-
https://huggingface.co/google/flan-t5-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/flan-t5-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/flan-t5-base/resolve/main/flax_model.msgpack
990 MB
- Xet hash:
- c3429570d2c62daf1270b165fa2aeb9090606d348d6d825a5baad1b0c72fdefa
- Size of remote file:
- 990 MB
- SHA256:
- e08fabc4ea8c1393e8d2bfb2d047afef7ee57ce4e70bedf7daaa28f9874c8a00
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.