Instructions to use casualprogrammer/shadow_mbart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use casualprogrammer/shadow_mbart with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50") model = PeftModel.from_pretrained(base_model, "casualprogrammer/shadow_mbart") - Transformers
How to use casualprogrammer/shadow_mbart with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("casualprogrammer/shadow_mbart") model = AutoModelForSeq2SeqLM.from_pretrained("casualprogrammer/shadow_mbart", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from casualprogrammer/shadow_mbart: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/casualprogrammer/shadow_mbart/resolve/main/tokenizer.json
- Command line
-
hf download hf://casualprogrammer/shadow_mbart/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/casualprogrammer/shadow_mbart/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- bf1c0ef9103433c7a61e04152a8ba0a50fb3ebd758366e773cf3d553afdc9c20
- Size of remote file:
- 17.1 MB
- SHA256:
- 539a6fc9fc66bb000c4a560cb8db87e5bb673a0786cb028b080ebd787025f2d4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.