Instructions to use MingZhong/DialogLED-large-5120 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MingZhong/DialogLED-large-5120 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MingZhong/DialogLED-large-5120") model = AutoModelForSeq2SeqLM.from_pretrained("MingZhong/DialogLED-large-5120", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from MingZhong/DialogLED-large-5120: direct link, hf CLI and curl.
- Browser
- Download file 3.38 GB
-
https://huggingface.co/MingZhong/DialogLED-large-5120/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://MingZhong/DialogLED-large-5120@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/MingZhong/DialogLED-large-5120/resolve/refs%2Fpr%2F1/optimizer.pt
3.38 GB
- Xet hash:
- de20e3a343157838668173a2b91337c06200fda645f8f5ba8aea0a550b233003
- Size of remote file:
- 3.38 GB
- SHA256:
- 030e327e57779cb8fc841a508f95a0faf36be77001c9420335469c9b86e08d68
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