初始化项目，由ModelHub XC社区提供模型

Model: YeungNLP/LongQLoRA-Llama2-7b-8k
Source: Original Platform

README.md

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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**   |