diff --git a/frameworks/peft/0.18.1/Dockerfile b/frameworks/peft/0.18.1/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..da58ae7de9a678e6220b8787e8cd4eccfac3701a
--- /dev/null
+++ b/frameworks/peft/0.18.1/Dockerfile
@@ -0,0 +1,30 @@
+FROM opencloudos/opencloudos9-cuda-devel:12.8
+
+LABEL maintainer="OpenCloudOS Community"
+LABEL org.opencontainers.image.source="https://gitee.com/OpenCloudOS/ai-agent-container"
+LABEL org.opencontainers.image.description="PEFT (GPU) on OpenCloudOS 9"
+
+# 安装 Python 3.11 及依赖
+RUN dnf install -y \
+    python3.11 \
+    python3.11-pip \
+    && dnf clean all \
+    && rm -rf /var/cache/yum/* \
+    && ln -sf /usr/bin/python3.11 /usr/bin/python3
+
+# 安装 PEFT 及常用的 GPU 依赖
+RUN pip3.11 install --no-cache-dir uv \
+    && uv pip install --no-cache-dir \
+        peft==0.18.1 \
+        torch \
+        transformers \
+        accelerate \
+        safetensors \
+        --system
+
+# 设置 GPU 环境变量
+ENV NVIDIA_VISIBLE_DEVICES=all
+
+RUN echo $(date +"%Y-%m-%dT%H:%M:%S%z") > /opencloudos_build_date.txt
+
+CMD ["python3.11"]
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diff --git a/frameworks/peft/0.18.1/README.md b/frameworks/peft/0.18.1/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e27d6f7ed6b117832c31962e7c6e6d580eaa3ffa
--- /dev/null
+++ b/frameworks/peft/0.18.1/README.md
@@ -0,0 +1,60 @@
+# PEFT on OpenCloudOS 9
+
+## 基本信息
+- **框架版本**：v0.18.1
+- **基础镜像**：opencloudos/opencloudos9-cuda-devel:12.8
+- **Python 版本**：3.11
+- **CUDA 版本**：12.8
+
+## 简介
+
+PEFT（Parameter-Efficient Fine-Tuning）是 HuggingFace 开发的参数高效微调库，支持 LoRA、QLoRA、Prefix Tuning、P-Tuning 等多种微调方法，可在消费级 GPU 上微调大语言模型。
+
+## 构建
+
+```bash
+docker build -t oc9-peft:0.18.1 .
+```
+
+## 使用示例
+
+```bash
+# 验证 peft 版本
+docker run --rm --gpus all oc9-peft:0.18.1 python3 -c "import peft; print(peft.__version__)"
+
+# 验证 CUDA 可用性
+docker run --rm --gpus all oc9-peft:0.18.1 python3 -c "import torch; print(torch.cuda.is_available())"
+
+# LoRA 微调示例（需要联网下载模型）
+docker run --rm --gpus all oc9-peft:0.18.1 python3 -c "
+from peft import LoraConfig, get_peft_model, TaskType
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained('gpt2')
+lora_config = LoraConfig(
+    task_type=TaskType.CAUSAL_LM,
+    r=8,
+    lora_alpha=32,
+    lora_dropout=0.1,
+    target_modules=['c_attn', 'c_proj']
+)
+model = get_peft_model(model, lora_config)
+model.print_trainable_parameters()
+"
+```
+
+## 测试验证
+
+```bash
+# 执行测试脚本（需要 GPU 环境）
+bash test.sh oc9-peft:0.18.1
+```
+
+测试脚本会验证以下内容：
+- CUDA 环境是否可用
+- peft 是否正常导入
+- 核心组件（LoraConfig、get_peft_model、TaskType）是否可加载
+
+## 已知问题
+- 首次运行时需要联网下载模型权重，建议提前挂载本地模型缓存目录以加速启动。
+- QLoRA 微调需要安装 `bitsandbytes`，如需使用请额外安装。
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diff --git a/frameworks/peft/0.18.1/build.conf b/frameworks/peft/0.18.1/build.conf
new file mode 100644
index 0000000000000000000000000000000000000000..8ddb92079a2d6d0e084931281f1bea72c7d6c5b0
--- /dev/null
+++ b/frameworks/peft/0.18.1/build.conf
@@ -0,0 +1,4 @@
+# peft 0.18.1 on OpenCloudOS 9 (GPU)
+IMAGE_NAME=oc9-peft
+IMAGE_TAG=0.18.1
+GPU_TEST=true
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diff --git a/frameworks/peft/0.18.1/test.sh b/frameworks/peft/0.18.1/test.sh
new file mode 100644
index 0000000000000000000000000000000000000000..315d8b8002e6500866b6a5a432152efba089402f
--- /dev/null
+++ b/frameworks/peft/0.18.1/test.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+set -e
+
+IMAGE="${1:?ERROR: 缺少镜像参数。用法: bash test.sh <image:tag>}"
+
+echo "=== GPU 镜像功能测试 ==="
+
+# 1. 验证 CUDA 环境
+echo -n "检查 CUDA... "
+docker run --rm --gpus all "$IMAGE" python3 -c "
+import torch
+assert torch.cuda.is_available(), 'CUDA not available'
+print(f'GPU: {torch.cuda.get_device_name(0)}')
+" && echo "✓ 通过" || { echo "✗ 失败"; exit 1; }
+
+# 2. 验证 peft 导入
+echo -n "检查 peft import... "
+docker run --rm --gpus all "$IMAGE" python3 -c "
+import peft
+print(peft.__version__)
+" && echo "✓ 通过" || { echo "✗ 失败"; exit 1; }
+
+# 3. 验证核心组件可用
+echo -n "检查 peft 核心组件... "
+docker run --rm --gpus all "$IMAGE" python3 -c "
+from peft import LoraConfig, get_peft_model, TaskType
+import torch
+from transformers import AutoModelForCausalLM
+
+config = LoraConfig(
+    task_type=TaskType.CAUSAL_LM,
+    r=8,
+    lora_alpha=32,
+    lora_dropout=0.1
+)
+print(f'LoraConfig 创建成功: r={config.r}, alpha={config.lora_alpha}')
+" && echo "✓ 通过" || { echo "✗ 失败"; exit 1; }
+
+echo "=== 所有测试通过 ==="
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