Instructions to use c2p-cmd/Bart-Large-CNN-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use c2p-cmd/Bart-Large-CNN-int8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="c2p-cmd/Bart-Large-CNN-int8")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("c2p-cmd/Bart-Large-CNN-int8") model = AutoModelForSeq2SeqLM.from_pretrained("c2p-cmd/Bart-Large-CNN-int8") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("c2p-cmd/Bart-Large-CNN-int8")
model = AutoModelForSeq2SeqLM.from_pretrained("c2p-cmd/Bart-Large-CNN-int8")Quick Links
int8 bit quantized version of bart-large-cnn
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Model tree for c2p-cmd/Bart-Large-CNN-int8
Base model
facebook/bart-large-cnn
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="c2p-cmd/Bart-Large-CNN-int8")