Instructions to use MingZhong/DialogLED-base-16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MingZhong/DialogLED-base-16384 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MingZhong/DialogLED-base-16384") model = AutoModelForSeq2SeqLM.from_pretrained("MingZhong/DialogLED-base-16384") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb24df74d7c2487945ce3af6bc91d50750c8aed6ec0b12a4427b60e29c27d3b9
- Size of remote file:
- 14.6 kB
- SHA256:
- 65103cdf9183f36e97211a7f1c1fbbcbbde5275ff69d7459d0dfcf63d7a8a933
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.