README.md

metadata

title: LLMLingua
emoji: 📝
colorFrom: red
colorTo: yellow
sdk: gradio
sdk_version: 4.36.0
app_file: app.py
pinned: false
license: mit

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

LLMLingua Series | Effectively Deliver Information to LLMs via Prompt Compression

| Project Page | LLMLingua | LongLLMLingua | LLMLingua-2 | LLMLingua Demo | LLMLingua-2 Demo |

News

• 🦚 We're excited to announce the release of LLMLingua-2, boasting a 3x-6x speed improvement over LLMLingua! For more information, check out our paper, visit the project page, and explore our demo.
• 🤳 Talk slides are available in AI Time Jan, 24.
• 🖥 EMNLP'23 slides are available in Session 5 and BoF-6.
• 📚 Check out our new blog post discussing RAG benefits and cost savings through prompt compression. See the script example here.
• 🎈 Visit our project page for real-world case studies in RAG, Online Meetings, CoT, and Code.
• 👨‍🦯 Explore our './examples' directory for practical applications, including RAG, Online Meeting, CoT, Code, and RAG using LlamaIndex.
• 👾 LongLLMLingua is now part of the LlamaIndex pipeline, a widely-used RAG framework.

TL;DR

LLMLingua utilizes a compact, well-trained language model (e.g., GPT2-small, LLaMA-7B) to identify and remove non-essential tokens in prompts. This approach enables efficient inference with large language models (LLMs), achieving up to 20x compression with minimal performance loss.

• LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models (EMNLP 2023)
  Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang and Lili Qiu

LongLLMLingua mitigates the 'lost in the middle' issue in LLMs, enhancing long-context information processing. It reduces costs and boosts efficiency with prompt compression, improving RAG performance by up to 21.4% using only 1/4 of the tokens.

• LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression (ICLR ME-FoMo 2024)
  Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang and Lili Qiu

LLMLingua-2, a small-size yet powerful prompt compression method trained via data distillation from GPT-4 for token classification with a BERT-level encoder, excels in task-agnostic compression. It surpasses LLMLingua in handling out-of-domain data, offering 3x-6x faster performance.

• LLMLingua-2: Context-Aware Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression (Under Review)
  Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, Qingwei Lin, Victor Ruhle, Yuqing Yang, Chin-Yew Lin, H. Vicky Zhao, Lili Qiu, Dongmei Zhang