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Speeding Up LLM Inference with Microsoft’s LLMLingua

Microsoft's LLMLingua reduces LLM inference costs and boosts performance by up to 20x with minimal performance loss using prompt compression.

Microsoft has unveiled an innovative method to significantly speed up the inference of Large Language Models (LLMs) while reducing their size by up to 20 times with minimal performance loss. This breakthrough is facilitated by their new library, LLMLingua, which can be implemented in just a couple of minutes. Here’s a comprehensive guide to get you started.

Introduction to LLMLingua

LLMLingua is a powerful library designed to compress prompts for LLMs, effectively reducing the token count and enhancing the speed of inference. This method uses a compact, well-trained language model (e.g., GPT-2 small, LLaMA-7B) to identify and remove non-essential tokens from prompts, making LLMs more efficient.


  • Cost Reduction: Achieves up to 20x compression, significantly lowering computational costs.
  • Performance Boost: Enhances LLM inference speed without a substantial loss in performance.
  • Ease of Use: Can be integrated with minimal code changes.

Quick Start Guide

Step 1: Install LLMLingua

To begin, install the LLMLingua library using pip:

Step 2: Import and Initialize LLMLingua

Import the necessary modules and initialize LLMLingua in your Python environment:

Step 3: Compress Prompts

You can now use LLMLingua to compress your prompts. Here’s an example of how to compress a sample prompt:

Step 4: Advanced Usage with Specific Models

LLMLingua supports various models and configurations. For instance, you can use the LLMLingua-2 model for more efficient prompt compression:

Step 5: Structured Prompt Compression

For more advanced scenarios, you can split your text into sections and apply different compression rates:


Microsoft’s LLMLingua provides a remarkable improvement in LLM inference efficiency, enabling up to 20x compression with minimal performance loss. This library is a game-changer for those looking to reduce costs and enhance performance in their AI applications.

For more details and advanced usage scenarios, visit the LLMLingua GitHub repository.

By integrating LLMLingua, you can seamlessly accelerate your language model applications and achieve substantial cost savings.


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