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 checkpoint-10498/optimizer.pt from casualprogrammer/shadow_mbart: direct link, hf CLI and curl.
- Browser
- Download file 69.5 MB
-
https://huggingface.co/casualprogrammer/shadow_mbart/resolve/main/checkpoint-10498/optimizer.pt
- Command line
-
hf download hf://casualprogrammer/shadow_mbart/checkpoint-10498/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/casualprogrammer/shadow_mbart/resolve/main/checkpoint-10498/optimizer.pt
69.5 MB
- Xet hash:
- 755bbb468921591a965bd7dfc19a584aace6693b378e36b63d9da25211768a0b
- Size of remote file:
- 69.5 MB
- SHA256:
- 526e7ea4d278f947167e312366109d266ca501948ee3617a01bc7e9f033565e5
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