# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("arrow-hf/bart-large")
model = AutoModel.from_pretrained("arrow-hf/bart-large")Quick Links
BART-large (full mirror)
Full mirror of facebook/bart-large — includes model weights (pytorch_model.bin, ~971 MB), tokenizer files, config.
Mirrored via huggingface_hub.snapshot_download for archival; identical to the upstream snapshot at mirror time.
Usage
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("arrow-hf/bart-large")
tokenizer = AutoTokenizer.from_pretrained("arrow-hf/bart-large")
Related
The tokenizer is used by arrow-hf/xvla-robotwin-stack-bowls-two-40pct (max_length=50). The X-VLA model uses the BART tokenizer vocabulary; the Florence2 vision-language backbone has its own learned weights and does not load BART's weights directly.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="arrow-hf/bart-large")