Overview

DistilBART-CNN-6-6 is a streamlined version of the BART architecture, specifically optimized for extractive and abstractive summarization. By leveraging knowledge distillation, it maintains a high degree of the original model's performance while significantly reducing latency and memory overhead. For developers, this makes it an ideal candidate for edge deployment or high-throughput pipelines where full-scale transformer models are too resource-intensive. It excels at condensing long-form articles into concise summaries, integrating seamlessly with the Hugging Face ecosystem and Transformers library. Compared to larger models, it offers a superior balance of speed and accuracy for standard NLP summarization tasks without requiring extensive hardware acceleration.

Highlights

Optimized for fast, low-latency text summarization tasks
Reduced memory footprint for efficient edge deployment
Full compatibility with Hugging Face Transformers library
Maintains high accuracy through advanced knowledge distillation
Apache-2.0 license ensures flexible commercial integration

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("Xenova/distilbart-cnn-6-6")
tokenizer = AutoTokenizer.from_pretrained("Xenova/distilbart-cnn-6-6")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download Xenova/distilbart-cnn-6-6

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download Xenova/distilbart-cnn-6-6 config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('Xenova/distilbart-cnn-6-6')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/Xenova/distilbart-cnn-6-6

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Xenova/distilbart-cnn-6-6

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('Xenova/distilbart-cnn-6-6')
tokenizer = AutoTokenizer.from_pretrained('Xenova/distilbart-cnn-6-6')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance

pip install modelscope

CLI Download

Download the full repository

modelscope download --model Xenova/distilbart-cnn-6-6

Download a single file to a local folder (e.g. README.md into ./dir)

modelscope download --model Xenova/distilbart-cnn-6-6 README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('Xenova/distilbart-cnn-6-6')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://www.modelscope.cn/Xenova/distilbart-cnn-6-6.git

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/Xenova/distilbart-cnn-6-6.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'Xenova/distilbart-cnn-6-6')

Full Documentation

来源: HuggingFace

---
base_model: sshleifer/distilbart-cnn-6-6
library_name: transformers.js
license: apache-2.0
pipeline_tag: summarization
---

https://huggingface.co/sshleifer/distilbart-cnn-6-6 with ONNX weights to be compatible with Transformers.js.

Usage (Transformers.js)

If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

bash

npm i @huggingface/transformers

Example: Summarization.

js

import { pipeline } from '@huggingface/transformers';
const generator = await pipeline('summarization', 'Xenova/distilbart-cnn-6-6');
const text = 'The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, ' +
  'and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. ' +
  'During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest ' +
  'man-made structure in the world, a title it held for 41 years until the Chrysler Building in New ' +
  'York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to ' +
  'the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the ' +
  'Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second ' +
  'tallest free-standing structure in France after the Millau Viaduct.';
const output = await generator(text, {
  max_new_tokens: 100,
});

Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

distilbart cnn 6 6

Overview

Highlights

Usage

Hugging Face Download

CLI Download

SDK Download

Git Download

PyTorch / Transformers Usage

Model Download

CLI Download

SDK Download

Git Download

Notebook Quickstart

Full Documentation

Usage (Transformers.js)