---
title: LLMLingua
emoji: 📝
colorFrom: red
colorTo: yellow
sdk: gradio
sdk_version: 3.47.1
app_file: app.py
pinned: false
license: mit
---

Check out the configuration reference at https://huggingface.co./docs/hub/spaces-config-reference


<div style="display: flex; align-items: center; ">  
    <div style="width: 100px; margin-right: 10px; height:auto;" align="left">  
        <img src="images/LLMLingua_logo.png" alt="LLMLingua" width="100" align="left">  
    </div>  
    <div style="flex-grow: 1;" align="center">  
        <h2 align="center">LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models & LongLLMLingua</h1>  
    </div>  
</div>

<p align="center">
| <a href="https://arxiv.org/abs/2310.05736"><b>LLMLingua Paper</b></a> | <a href="https://arxiv.org/abs/2310.06839"><b>LongLLMLingua Paper</b></a> | <a href="https://huggingface.co./spaces/microsoft/LLMLingua"><b>HF Space Demo</b></a> |
</p>

## Tl;DR

LLMLingua, that uses a well-trained small language model after alignment, such as GPT2-small or LLaMA-7B, to detect the unimportant tokens in the prompt and enable inference with the compressed prompt in black-box LLMs, achieving up to 20x compression with minimal performance loss.

[LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models](https://arxiv.org/abs/2310.05736) (EMNLP 2023).<br>
_Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang and Lili Qiu_

LongLLMLingua is a method that enhances LLMs' ability to perceive key information in long-context scenarios using prompt compression, achieveing up to $28.5 in cost savings per 1,000 samples while also improving performance.

[LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression](https://arxiv.org/abs/2310.06839) (Under Review).<br>
_Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang and Lili Qiu_