diff --git a/README.md b/README.md
index be019fa9..0fdea14c 100644
--- a/README.md
+++ b/README.md
@@ -415,7 +415,7 @@ To enable FlashAttention-2 on the Windows platform, you need to install the prec
 
 <details><summary>For Ascend NPU users</summary>
 
-To install LLaMA Factory on Ascend NPU devices, please specify extra dependencies: `pip install -e ".[torch-npu,metrics]"`. Additionally, you need to install the **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**. Please follow the [installation tutorial](https://www.hiascend.com/document/detail/en/CANNCommunityEdition/600alphaX/softwareinstall/instg/atlasdeploy_03_0031.html) or use the following commands:
+To install LLaMA Factory on Ascend NPU devices, please upgrade Python to version 3.10 or higher and specify extra dependencies: `pip install -e ".[torch-npu,metrics]"`. Additionally, you need to install the **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**. Please follow the [installation tutorial](https://www.hiascend.com/document/detail/en/CANNCommunityEdition/600alphaX/softwareinstall/instg/atlasdeploy_03_0031.html) or use the following commands:
 
 ```bash
 # replace the url according to your CANN version and devices
@@ -444,6 +444,33 @@ If you cannot infer model on NPU devices, try setting `do_sample: false` in the
 
 Download the pre-built Docker images: [32GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html) | [64GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
 
+To use nf4 QLoRA quantization based on bitsandbytes in Ascend NPU, please follow these 3 steps:
+
+1. Manually compile bnb: Refer to [the installation documentation](https://huggingface.co/docs/bitsandbytes/installation?backend=Ascend+NPU&platform=Ascend+NPU) for the NPU version of bitsandbytes to complete the compilation and installation of bnb. The compilation requires a cmake version of at least 3.22.1 and a g++ version of at least 12.x.
+```bash
+# Install bitsandbytes from source
+# Clone bitsandbytes repo, Ascend NPU backend is currently enabled on multi-backend-refactor branch
+git clone -b multi-backend-refactor https://github.com/bitsandbytes-foundation/bitsandbytes.git
+cd bitsandbytes/
+
+# Install dependencies
+pip install -r requirements-dev.txt
+
+# Install the dependencies for the compilation tools. Note that the commands for this step may vary depending on the operating system. The following are provided for reference
+apt-get install -y build-essential cmake
+
+# Compile & install  
+cmake -DCOMPUTE_BACKEND=npu -S .
+make
+pip install -e .
+```
+2. Install and use the main branch version of transformers.
+```
+git clone -b https://github.com/huggingface/transformers.git
+cd transformers
+pip install .
+```
+3. Set the double_quantization parameter to false in the training configuration. You can refer to the [example](examples/train_qlora/llama3_lora_sft_otfq_npu.yaml) for guidance.
 </details>
 
 ### Data Preparation
diff --git a/README_zh.md b/README_zh.md
index 50ec1acc..72e090f6 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -416,7 +416,7 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
 
 <details><summary>昇腾 NPU 用户指南</summary>
 
-在昇腾 NPU 设备上安装 LLaMA Factory 时，需要指定额外依赖项，使用 `pip install -e ".[torch-npu,metrics]"` 命令安装。此外，还需要安装 **[Ascend CANN Toolkit 与 Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**，安装方法请参考[安装教程](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC2alpha002/quickstart/quickstart/quickstart_18_0004.html)或使用以下命令：
+在昇腾 NPU 设备上安装 LLaMA Factory 时，请升级Python到3.10及以上，并需要指定额外依赖项，使用 `pip install -e ".[torch-npu,metrics]"` 命令安装。此外，还需要安装 **[Ascend CANN Toolkit 与 Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**，安装方法请参考[安装教程](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC2alpha002/quickstart/quickstart/quickstart_18_0004.html)或使用以下命令：
 
 ```bash
 # 请替换 URL 为 CANN 版本和设备型号对应的 URL
@@ -445,6 +445,33 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
 
 下载预构建 Docker 镜像：[32GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html) | [64GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
 
+如果要在 Ascend NPU中使用 基于bitsandbytes 的nf4 QLoRA量化，请执行如下3个步骤
+1. 手动编译bnb：请参考 bitsandbytes npu版本的[安装文档](https://huggingface.co/docs/bitsandbytes/installation?backend=Ascend+NPU&platform=Ascend+NPU)完成bnb的编译安装，编译要求环境cmake版本不低于3.22.1，g++版本不低于12.x
+```
+# 从源码安装bitsandbytes
+# 克隆bitsandbytes仓库, Ascend NPU目前在multi-backend-refactor中支持
+git clone -b multi-backend-refactor https://github.com/bitsandbytes-foundation/bitsandbytes.git
+cd bitsandbytes/
+
+# 安装依赖
+pip install -r requirements-dev.txt
+
+# 安装编译工具依赖，该步骤在不同系统上命令有所不同，供参考
+apt-get install -y build-essential cmake
+
+# 编译 & 安装
+cmake -DCOMPUTE_BACKEND=npu -S .
+make
+pip install -e .
+```
+2. 安装使用transformers的main分支版本
+```
+git clone -b https://github.com/huggingface/transformers.git
+cd transformers
+pip install .
+```
+3. 设置训练参数中的double_quantization参数为false，可参考[示例](examples/train_qlora/llama3_lora_sft_otfq_npu.yaml)
+
 </details>
 
 ### 数据准备
diff --git a/examples/train_qlora/llama3_lora_sft_otfq_npu.yaml b/examples/train_qlora/llama3_lora_sft_otfq_npu.yaml
new file mode 100644
index 00000000..983acd39
--- /dev/null
+++ b/examples/train_qlora/llama3_lora_sft_otfq_npu.yaml
@@ -0,0 +1,43 @@
+### model
+model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
+quantization_bit: 4
+quantization_method: bitsandbytes  # choices: [bitsandbytes (4/8), hqq (2/3/4/5/6/8), eetq (8)]
+double_quantization: false
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_target: all
+
+### dataset
+dataset: identity,alpaca_en_demo
+template: llama3
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+
+### output
+output_dir: saves/llama3-8b/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 8
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+
+### eval
+val_size: 0.1
+per_device_eval_batch_size: 1
+eval_strategy: steps
+eval_steps: 500
diff --git a/src/llamafactory/model/model_utils/longlora.py b/src/llamafactory/model/model_utils/longlora.py
index 89457846..53043c2b 100644
--- a/src/llamafactory/model/model_utils/longlora.py
+++ b/src/llamafactory/model/model_utils/longlora.py
@@ -23,14 +23,7 @@ from typing import TYPE_CHECKING, Optional, Tuple
 import torch
 import torch.nn as nn
 import transformers
-from transformers.models.llama.modeling_llama import (
-    Cache,
-    LlamaAttention,
-    LlamaFlashAttention2,
-    LlamaSdpaAttention,
-    apply_rotary_pos_emb,
-    repeat_kv,
-)
+from transformers.models.llama.modeling_llama import Cache, apply_rotary_pos_emb, repeat_kv
 
 from ...extras import logging
 from ...extras.constants import SUPPORTED_CLASS_FOR_S2ATTN
@@ -38,6 +31,10 @@ from ...extras.misc import check_version
 from ...extras.packages import is_transformers_version_greater_than
 
 
+if not is_transformers_version_greater_than("4.48.0"):
+    from transformers.models.llama.modeling_llama import LlamaAttention, LlamaFlashAttention2, LlamaSdpaAttention
+
+
 if TYPE_CHECKING:
     from transformers import PretrainedConfig