llmlingua 0.1.1.dev3

To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.

LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models [paper] & LongLLMLingua [paper]

https://github.com/microsoft/LLMLingua/assets/30883354/ef52995c-ef3c-4eac-a9fd-1acb491c325b

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 (EMNLP 2023). 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 (Under Review). Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang and Lili Qiu

🎥 Overview

• Have you ever tried to input a long text and ask ChatGPT to summarize it, only to be told that it exceeds the token limit? ​
• Have you ever spent a lot of time fine-tuning the personality of ChatGPT, only to find that it forgets the previous instructions after a few rounds of dialogue? ​
• Have you ever used the GPT3.5/4 API for experiments, and got good results, but also received a huge bill after a few days? ​

Large language models, such as ChatGPT and GPT-4, impress us with their amazing generalization and reasoning abilities, but they also come with some drawbacks, such as the prompt length limit and the prompt-based pricing scheme.​

Now you can use LLMLingua & LongLLMLingua!​

A simple and efficient method to compress prompt up to 20x.​

• 💰 Saving cost, not only prompt, but also the generation length;​
• 📝 Support longer contexts while delivering enhanced performance;​
• ⚖️ Robustness, no need any training for the LLMs;​
• 🕵️ Keeping the original prompt knowledge like ICL, reasoning, etc.​
• 📜 KV-Cache compression, speedup inference;​

If you find this repo helpful, please cite the following paper:

@inproceedings{jiang-etal-2023-llmlingua,
    title = "LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models",
    author = "Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang and Lili Qiu",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    publisher = "Association for Computational Linguistics",
}

@inproceedings{jiang-etal-2023-longllmlingua,
    title = "LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression",
    author = "Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang and Lili Qiu",
}

🎯 Quick Start

Install LLMLingua,

pip install llmlingua

Then, you can use LLMLingua to compress your prompt,

from llmlingua import PromptCompressor

llm_lingua = PromptCompressor()
compressed_prompt = llm_lingua.compress_prompt(prompt, instruction="", question="", target_token=200)

# > {'compressed_prompt': 'Question: Sam bought a dozen boxes, each with 30 highlighter pens inside, for $10 each box. He reanged five of boxes into packages of sixlters each and sold them $3 per. He sold the rest theters separately at the of three pens $2. How much did make in total, dollars?\nLets think step step\nSam bought 1 boxes x00 oflters.\nHe bought 12 * 300ters in total\nSam then took 5 boxes 6ters0ters.\nHe sold these boxes for 5 *5\nAfterelling these  boxes there were 3030 highlighters remaining.\nThese form 330 / 3 = 110 groups of three pens.\nHe sold each of these groups for $2 each, so made 110 * 2 = $220 from them.\nIn total, then, he earned $220 + $15 = $235.\nSince his original cost was $120, he earned $235 - $120 = $115 in profit.\nThe answer is 115',
#  'origin_tokens': 2365,
#  'compressed_tokens': 211,
#  'ratio': '11.2x',
#  'saving': ', Saving $0.1 in GPT-4.'}

You can refer to this document for more recommendations on how to use LLMLingua effectively.

Frequently Asked Questions

show in Transparency_FAQ.md

Contributing

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