LLMLingua

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LLMLingua is an AI Frameworks & Infra tool that compresses prompts and KV-caches for Large Language Models. It achieves up to 20x compression with minimal performance loss, speeding up inference and enhancing key information perception.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is LLMLingua?

LLMLingua is a powerful tool designed to optimize the performance of Large Language Models (LLMs) by significantly compressing prompts and KV-caches. This innovative approach, detailed in research papers presented at EMNLP'23 and ACL'24, allows for up to 20x compression while maintaining minimal performance degradation. The tool helps overcome common LLM challenges such as token limits, high API costs, and the 'lost in the middle' issue for long contexts. LLMLingua offers various versions, including LongLLMLingua for enhanced long-context processing and LLMLingua-2 for faster, task-agnostic compression. It also features SecurityLingua, a safety guardrail model for detecting and mitigating jailbreak attacks through security-aware prompt compression. The tool is integrated into popular frameworks like LangChain and LlamaIndex, making it accessible for developers working on LLM-based applications.

Best used for

Ideal for developers and data scientists who need to optimize LLM performance, reduce inference latency, and manage token costs. Especially valuable for those building RAG applications, handling long-context scenarios, or implementing security measures against LLM jailbreak attacks.

Common actions

compress prompts

accelerate LLM inference

reduce LLM costs

enhance LLM context

secure LLM applications

deepfakeopen-sourceworkflowscollaborationlow-code/no-codeautomated workflowgithub copilotface swapping"AI Agents"

Capabilities

Key features

Prompt compression
KV-cache compression
Long-context enhancement
Task-agnostic compression
Security-aware compression
Integrates with RAG frameworks

Target Audience

developerdata scientiststartup founder

Integrations

langchainllamaindexprompt-flow

Pricing & Plans

Open Source

Free

FAQs

What is the primary benefit of using LLMLingua?

LLMLingua's main benefit is speeding up LLM inference and reducing operational costs by compressing prompts and KV-caches. It achieves up to 20x compression with minimal performance loss, making LLMs more efficient and cost-effective for various applications.

How does LLMLingua address the 'lost in the middle' problem?

LongLLMLingua, a variant of LLMLingua, specifically mitigates the 'lost in the middle' issue by enhancing long-context information processing. It uses prompt compression to improve RAG performance by up to 21.4% while using only a quarter of the tokens.

Can LLMLingua help with LLM security?

Yes, SecurityLingua, another variant, acts as a safety guardrail model. It uses security-aware prompt compression to reveal malicious intentions behind jailbreak attacks, enabling LLMs to detect attacks and generate safe responses with negligible overhead.

What is the difference between LLMLingua and LLMLingua-2?

LLMLingua-2 offers a 3x-6x speed improvement over the original LLMLingua. It is a small-size, powerful prompt compression method trained via data distillation from GPT-4, excelling in task-agnostic compression and outperforming LLMLingua with out-of-domain data.

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This tool also appears in

Coding & Development › Open Source & Models Coding & Development › Prompt Engineering

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