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 eval_results.csv from casualprogrammer/shadow_mbart: direct link, hf CLI and curl.
- Browser
- Download file 60.2 MB
-
https://huggingface.co/casualprogrammer/shadow_mbart/resolve/main/eval_results.csv
- Command line
-
hf download hf://casualprogrammer/shadow_mbart/eval_results.csv
-
curl -L -o eval_results.csv https://huggingface.co/casualprogrammer/shadow_mbart/resolve/main/eval_results.csv
60.2 MB
- Xet hash:
- 181b74163718e4b1ef349c2c14f9321d2dfa27ea081cd4e821a8eaec9dc5b48f
- Size of remote file:
- 60.2 MB
- SHA256:
- a4f03ad6105b04dd845462d153ba6eeb909d474375f0cb400307cb7e9020d435
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