---
license: apache-2.0
language:
- en
---

# LongQLoRA: Efficient and Effective Method to Extend Context Length of LLMs

## Technical Report

Technical Report: [LongQLoRA: Efficient and Effective Method to Extend Context Length of Large Language Models](https://arxiv.org/abs/2311.04879)

## Introduction
LongQLoRA is a memory-efficient and effective method to extend context length of Large Language Models with less training GPUs. 
**On a single 32GB V100 GPU**, LongQLoRA can extend the context length of LLaMA2 7B and 13B from 4096 to 8192 and even to 12k.
LongQLoRA achieves competitive perplexity performance on PG19 and Proof-pile dataset after only 1000 finetuning steps, our model outperforms LongLoRA and is very close to MPT-7B-8K.


 Evaluation perplexity on PG19 validation and Proof-pile test datasets in evaluation context length of 8192:

| Model               | PG19     | Proof-pile |
|---------------------|----------|------------|
| LLaMA2-7B           | \>1000   | \>1000     |
| MPT-7B-8K           | 7.98     | 2.67       |
| LongLoRA-LoRA-7B-8K | 8.20     | 2.78       |
| LongLoRA-Full-7B-8K | 7.93     | 2.73       |
| **LongQLoRA-7B-8K** | **7.96** | **2.73**   |