Instructions to use Veggissss/ltg_nort5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Veggissss/ltg_nort5-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Veggissss/ltg_nort5-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForConditionalGeneration model = AutoModelForConditionalGeneration.from_pretrained("Veggissss/ltg_nort5-base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 21a809aa06c29df96652f5865f4af67306ad9658cfdcab4ab32e1ffb4dcbb902
- Size of remote file:
- 911 MB
- SHA256:
- 6276c5edc40168136dfd85dc003fcf093eeac7759f4ab70d16b7e9e2918989c2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.