Model Details
Full Model IDXenova/distilbart-cnn-6-6
Pipeline / Tasksummarization
Librarytransformers.js
Downloads (all-time)11.8K
Likes9
Last Modified7/22/2025
Author / OrgXenova
PrivateNo � public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("summarization", model="Xenova/distilbart-cnn-6-6")
# Run inference
result = pipe("Your input here")
print(result)����� Tags
transformers.jsonnxbarttext2text-generationsummarizationbase_model:sshleifer/distilbart-cnn-6-6base_model:quantized:sshleifer/distilbart-cnn-6-6license:apache-2.0region:us
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Open on Hugging Face →Browse Model Files ↗�� Browse All Models�� Task: Summarization
This model is designed for the Summarization task. Explore more models for this use case.
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⬇ Downloads11.8K
����� Community Likes9
🛠�� Requirements
- →Install: pip install transformers.js
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.