Instructions to use google/long-t5-tglobal-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/long-t5-tglobal-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-base") model = AutoModelForSeq2SeqLM.from_pretrained("google/long-t5-tglobal-base") - Notebooks
- Google Colab
- Kaggle
Finetuning issue
#4
by amnasher - opened
Hello I am trying to finetune this model on colab free version but I get cuda out of memory issue which is I think due to model size what can I do to avoid this?
Probably try finetuning the model by applying some Quantization(either in BFloat16 or 8-bit or 4-bit) or by using the PEFT Library from HuggingFace by adding some re-parameterization/additive approaches.
It is simple to use and allows for significantly reducing the memory footprint requirement.
https://huggingface.co/blog/peft