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---
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** |

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