Instructions to use IB13/sft_t5_base_processed_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IB13/sft_t5_base_processed_model with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("IB13/sft_t5_base_processed_model") model = AutoModelForSeq2SeqLM.from_pretrained("IB13/sft_t5_base_processed_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b5b5659e936f9de6e7db68545d53676d2e4a9593d8360e950d592218e7f5fdf
- Size of remote file:
- 4.35 kB
- SHA256:
- ed78f4f1a4f7e6971f07dbf939d66453074b40a41ffd8ec9f193ee8619fa4103
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.