diff --git a/README.md b/README.md
index 180837a..831f19b 100644
--- a/README.md
+++ b/README.md
@@ -74,7 +74,7 @@
   - [ ] [Qwen2-vl-2B WebDemo 部署]() @赵伟
   - [ ] [Qwen2-vl-2B vLLM 部署]() @荞麦
   - [ ] [Qwen2-vl-2B Lora 微调]() @李柯辰
-  - [ ] [Qwen2-vl-2B Lora 微调 SwanLab 可视化记录版]()
+  - [x] [Qwen2-vl-2B Lora 微调 SwanLab 可视化记录版](./models/Qwen2-VL/05-Qwen2-VL-2B-Instruct%20Lora%20微调%20SwanLab%20可视化记录版.md) @林泽毅
 
 - [Qwen2.5](https://github.com/QwenLM/Qwen2.5)
   - [x] [Qwen2.5-7B-Instruct FastApi 部署调用](./models/Qwen2.5/01-Qwen2.5-7B-Instruct%20FastApi%20部署调用.md) @娄天奥
diff --git a/models/Qwen2-VL/05-Qwen2-VL-2B-Instruct Lora 微调 SwanLab 可视化记录版.md b/models/Qwen2-VL/05-Qwen2-VL-2B-Instruct Lora 微调 SwanLab 可视化记录版.md
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@@ -0,0 +1,587 @@
+# Qwen2-VL-2B-Instruct Lora 微调 SwanLab可视化记录版
+
+本节我们简要介绍基于 transformers、peft 等框架，使用 Qwen2-VL-2B-Instruct 模型在**COCO2014图像描述** 上进行Lora微调训练，同时使用 [SwanLab](https://github.com/swanhubx/swanlab) 监控训练过程与评估模型效果。
+
+Lora 是一种高效微调方法，深入了解其原理可参见博客：[知乎|深入浅出 Lora](https://zhuanlan.zhihu.com/p/650197598)。
+
+训练过程：<a href="https://swanlab.cn/@ZeyiLin/Qwen2-VL-finetune/runs/53vm3y7sp5h5fzlmlc5up/chart" target="_blank">Qwen2-VL-finetune
+</a>
+
+## 目录
+
+- [SwanLab简介](#-SwanLab简介)
+- [环境配置](#-环境配置)
+- [准备数据集](#-准备数据集)
+- [模型下载与加载](#-模型下载与加载)
+- [集成SwanLab](#-集成SwanLab)
+- [开始微调（完整代码）](#-开始微调)
+- [训练结果演示](#-训练结果演示)
+- [推理LoRA微调后的模型](#-推理LoRA微调后的模型)
+- [补充](#补充)
+
+## 👋 SwanLab简介
+
+![05-1](./images/05-1.jpg)
+
+[SwanLab](https://github.com/swanhubx/swanlab) 是一个开源的模型训练记录工具，常被称为"中国版 Weights&Biases + Tensorboard"。SwanLab面向AI研究者，提供了训练可视化、自动日志记录、超参数记录、实验对比、多人协同等功能。在SwanLab上，研究者能基于直观的可视化图表发现训练问题，对比多个实验找到研究灵感，并通过在线链接的分享与基于组织的多人协同训练，打破团队沟通的壁垒。
+
+**为什么要记录训练？**
+
+相较于软件开发，模型训练更像一个实验科学。一个品质优秀的模型背后，往往是成千上万次实验。研究者需要不断尝试、记录、对比，积累经验，才能找到最佳的模型结构、超参数与数据配比。在这之中，如何高效进行记录与对比，对于研究效率的提升至关重要。
+
+**可视化的价值在哪里？**
+
+机器学习模型训练往往伴随着大量的超参数、指标、日志等数据，很多关键信息往往存在于实验的中间而非结尾，如果不对连续的指标通过图表进行可视化，往往会错失发现问题的最佳时机，甚至错过关键信息。同时不进行可视化，也难以对比多个实验之间的差异。
+可视化也为AI研究者提供了良好的交流基础，研究者们可以基于图表进行沟通、分析与优化，而非以往看着枯燥的终端打印。这打破了团队沟通的壁垒，提高了整体的研发效率。
+
+
+## 🌍 环境配置
+
+环境配置分为三步：
+
+1. 确保你的电脑上至少有一张英伟达显卡，并已安装好了CUDA环境。
+
+2. 安装Python（版本>=3.8）以及能够调用CUDA加速的PyTorch。
+
+3. 安装Qwen2-VL微调相关的第三方库，可以使用以下命令：
+
+```bash
+python -m pip install --upgrade pip
+# 更换 pypi 源加速库的安装
+pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
+
+pip install modelscope==1.18.0
+pip install transformers==4.46.2
+pip install sentencepiece==0.2.0
+pip install accelerate==1.1.1
+pip install datasets==2.18.0
+pip install peft==0.13.2
+pip install swanlab==0.3.25
+pip install qwen-vl-utils==0.0.8
+```
+
+## 📚 准备数据集
+
+本节使用的是 [coco_2014_caption](https://modelscope.cn/datasets/modelscope/coco_2014_caption/summary) 数据集（中的500张图），该数据集主要用于多模态（Image-to-Text）任务。
+
+> 数据集介绍：COCO 2014 Caption数据集是Microsoft Common Objects in Context (COCO)数据集的一部分，主要用于图像描述任务。该数据集包含了大约40万张图像，每张图像都有至少1个人工生成的英文描述语句。这些描述语句旨在帮助计算机理解图像内容，并为图像自动生成描述提供训练数据。
+
+![05-2](./images/05-2.jpg)
+
+在本节的任务中，我们主要使用其中的前500张图像，并对它进行处理和格式调整，目标是组合成如下格式的json文件：
+
+```json
+[
+{
+    "id": "identity_1",
+    "conversations": [
+      {
+        "from": "user",
+        "value": "COCO Yes: <|vision_start|>图像文件路径<|vision_end|>"
+      },
+      {
+        "from": "assistant",
+        "value": "A snow skier assessing the mountain before starting to sky"
+      }
+    ]
+},
+...
+]
+```
+
+其中，"from"是角色（user代表人类，assistant代表模型），"value"是聊天的内容，其中`<|vision_start|>`和`<|vision_end|>`是Qwen2-VL模型识别图像的标记，中间可以放图像的文件路径，也可以是URL。
+
+**数据集下载与处理方式**
+
+1. **我们需要做四件事情：**
+    - 通过Modelscope下载coco_2014_caption数据集
+    - 加载数据集，将图像保存到本地
+    - 将图像路径和描述文本转换为一个csv文件
+    - 将csv文件转换为json文件
+
+2. **使用下面的代码完成从数据下载到生成csv的过程：**
+
+data2csv.py：
+
+```python
+# 导入所需的库
+from modelscope.msdatasets import MsDataset
+import os
+import pandas as pd
+
+MAX_DATA_NUMBER = 500
+
+# 检查目录是否已存在
+if not os.path.exists('coco_2014_caption'):
+    # 从modelscope下载COCO 2014图像描述数据集
+    ds =  MsDataset.load('modelscope/coco_2014_caption', subset_name='coco_2014_caption', split='train')
+    print(len(ds))
+    # 设置处理的图片数量上限
+    total = min(MAX_DATA_NUMBER, len(ds))
+
+    # 创建保存图片的目录
+    os.makedirs('coco_2014_caption', exist_ok=True)
+
+    # 初始化存储图片路径和描述的列表
+    image_paths = []
+    captions = []
+
+    for i in range(total):
+        # 获取每个样本的信息
+        item = ds[i]
+        image_id = item['image_id']
+        caption = item['caption']
+        image = item['image']
+        
+        # 保存图片并记录路径
+        image_path = os.path.abspath(f'coco_2014_caption/{image_id}.jpg')
+        image.save(image_path)
+        
+        # 将路径和描述添加到列表中
+        image_paths.append(image_path)
+        captions.append(caption)
+        
+        # 每处理50张图片打印一次进度
+        if (i + 1) % 50 == 0:
+            print(f'Processing {i+1}/{total} images ({(i+1)/total*100:.1f}%)')
+
+    # 将图片路径和描述保存为CSV文件
+    df = pd.DataFrame({
+        'image_path': image_paths,
+        'caption': captions
+    })
+    
+    # 将数据保存为CSV文件
+    df.to_csv('./coco-2024-dataset.csv', index=False)
+    
+    print(f'数据处理完成，共处理了{total}张图片')
+
+else:
+    print('coco_2014_caption目录已存在,跳过数据处理步骤')
+```
+
+
+**3. 在同一目录下，用以下代码，将csv文件转换为json文件：**
+
+csv2json.py：
+
+```python
+import pandas as pd
+import json
+
+# 载入CSV文件
+df = pd.read_csv('./coco-2024-dataset.csv')
+conversations = []
+
+# 添加对话数据
+for i in range(len(df)):
+    conversations.append({
+        "id": f"identity_{i+1}",
+        "conversations": [
+            {
+                "from": "user",
+                "value": f"COCO Yes: <|vision_start|>{df.iloc[i]['image_path']}<|vision_end|>"
+            },
+            {
+                "from": "assistant", 
+                "value": df.iloc[i]['caption']
+            }
+        ]
+    })
+
+# 保存为Json
+with open('data_vl.json', 'w', encoding='utf-8') as f:
+    json.dump(conversations, f, ensure_ascii=False, indent=2)
+```
+
+此时目录下会多出两个文件：
+- coco-2024-dataset.csv
+- data_vl.json
+
+至此，我们完成了数据集的准备。
+
+
+## 🤖 模型下载与加载
+
+这里我们使用modelscope下载Qwen2-VL-2B-Instruct模型，然后把它加载到Transformers中进行训练：
+
+```python
+from modelscope import snapshot_download, AutoTokenizer
+from transformers import TrainingArguments, Trainer, DataCollatorForSeq2Seq, Qwen2VLForConditionalGeneration, AutoProcessor
+import torch
+
+# 在modelscope上下载Qwen2-VL模型到本地目录下
+model_dir = snapshot_download("Qwen/Qwen2-VL-2B-Instruct", cache_dir="./", revision="master")
+
+# 使用Transformers加载模型权重
+tokenizer = AutoTokenizer.from_pretrained("./Qwen/Qwen2-VL-2B-Instruct/", use_fast=False, trust_remote_code=True)
+# 特别的，Qwen2-VL-2B-Instruct模型需要使用Qwen2VLForConditionalGeneration来加载
+model = Qwen2VLForConditionalGeneration.from_pretrained("./Qwen/Qwen2-VL-2B-Instruct/", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True,)
+model.enable_input_require_grads()  # 开启梯度检查点时，要执行该方法
+```
+
+模型大小为 4.5GB，下载模型大概需要 5 分钟。
+
+## 🐦‍ 集成SwanLab
+
+SwanLab与Transformers已经做好了集成，用法是在Trainer的`callbacks`参数中添加`SwanLabCallback`实例，就可以自动记录超参数和训练指标，简化代码如下：
+
+```python
+from swanlab.integration.transformers import SwanLabCallback
+from transformers import Trainer
+
+swanlab_callback = SwanLabCallback()
+
+trainer = Trainer(
+    ...
+    callbacks=[swanlab_callback],
+)
+```
+
+首次使用SwanLab，需要先在[官网](https://swanlab.cn)注册一个账号，然后在用户设置页面复制你的API Key，然后在训练开始提示登录时粘贴即可，后续无需再次登录：
+
+![05-3](./images/05-3.jpg)
+
+更多用法可参考[快速开始](https://docs.swanlab.cn/zh/guide_cloud/general/quick-start.html)、[Transformers集成](https://docs.swanlab.cn/zh/guide_cloud/integration/integration-huggingface-transformers.html)。
+
+
+## 🚀 开始微调
+
+查看可视化训练过程：<a href="https://swanlab.cn/@ZeyiLin/Qwen2-VL-finetune/runs/53vm3y7sp5h5fzlmlc5up/chart" target="_blank">Qwen2-VL-finetune</a>
+
+
+**本节代码做了以下几件事：**
+1. 下载并加载Qwen2-VL-2B-Instruct模型
+2. 加载数据集，取前496条数据参与训练，4条数据进行主观评测
+3. 配置Lora，参数为r=64, lora_alpha=16, lora_dropout=0.05
+4. 使用SwanLab记录训练过程，包括超参数、指标和最终的模型输出结果
+5. 训练2个epoch
+
+开始执行代码时的目录结构应该是：
+```
+|———— train.py
+|———— coco_2014_caption
+|———— coco-2024-dataset.csv
+|———— data_vl.json
+|———— data2csv.py
+|———— csv2json.py
+```
+
+
+**完整代码如下**
+
+train.py：
+
+```python
+import torch
+from datasets import Dataset
+from modelscope import snapshot_download, AutoTokenizer
+from swanlab.integration.transformers import SwanLabCallback
+from qwen_vl_utils import process_vision_info
+from peft import LoraConfig, TaskType, get_peft_model, PeftModel
+from transformers import (
+    TrainingArguments,
+    Trainer,
+    DataCollatorForSeq2Seq,
+    Qwen2VLForConditionalGeneration,
+    AutoProcessor,
+)
+import swanlab
+import json
+
+
+def process_func(example):
+    """
+    将数据集进行预处理
+    """
+    MAX_LENGTH = 8192
+    input_ids, attention_mask, labels = [], [], []
+    conversation = example["conversations"]
+    input_content = conversation[0]["value"]
+    output_content = conversation[1]["value"]
+    
+    instruction = tokenizer(
+        f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n{input_content}<|im_end|>\n<|im_start|>assistant\n",
+        add_special_tokens=False,
+    )
+    response = tokenizer(f"{output_content}", add_special_tokens=False)
+    input_ids = (
+        instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id]
+    )
+    attention_mask = instruction["attention_mask"] + response["attention_mask"] + [1]
+    labels = (
+        [-100] * len(instruction["input_ids"])
+        + response["input_ids"]
+        + [tokenizer.pad_token_id]
+    )
+    
+    if len(input_ids) > MAX_LENGTH:  # 做一个截断
+        input_ids = input_ids[:MAX_LENGTH]
+        attention_mask = attention_mask[:MAX_LENGTH]
+        labels = labels[:MAX_LENGTH]
+        
+    return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels}
+
+
+def predict(messages, model):
+    # 准备推理
+    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    image_inputs, video_inputs = process_vision_info(messages)
+    inputs = processor(
+        text=[text],
+        images=image_inputs,
+        videos=video_inputs,
+        padding=True,
+        return_tensors="pt",
+    )
+    inputs = inputs.to("cuda")
+
+    # 生成输出
+    generated_ids = model.generate(**inputs, max_new_tokens=128)
+    generated_ids_trimmed = [
+        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+    ]
+    output_text = processor.batch_decode(
+        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+    )
+    
+    return output_text[0]
+
+
+# 在modelscope上下载Qwen2-VL模型到本地目录下
+model_dir = snapshot_download("Qwen/Qwen2-VL-2B-Instruct", cache_dir="./", revision="master")
+
+# 使用Transformers加载模型权重
+tokenizer = AutoTokenizer.from_pretrained("./Qwen/Qwen2-VL-2B-Instruct/", use_fast=False, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained("./Qwen/Qwen2-VL-2B-Instruct")
+
+model = Qwen2VLForConditionalGeneration.from_pretrained("./Qwen/Qwen2-VL-2B-Instruct/", device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True,)
+model.enable_input_require_grads()  # 开启梯度检查点时，要执行该方法
+
+# 处理数据集：读取json文件
+# 拆分成训练集和测试集，保存为data_vl_train.json和data_vl_test.json
+train_json_path = "data_vl.json"
+with open(train_json_path, 'r') as f:
+    data = json.load(f)
+    train_data = data[:-4]
+    test_data = data[-4:]
+
+with open("data_vl_train.json", "w") as f:
+    json.dump(train_data, f)
+
+with open("data_vl_test.json", "w") as f:
+    json.dump(test_data, f)
+
+train_ds = Dataset.from_json("data_vl_train.json")
+train_dataset = train_ds.map(process_func)
+
+# 配置LoRA
+config = LoraConfig(
+    task_type=TaskType.CAUSAL_LM,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    inference_mode=False,  # 训练模式
+    r=64,  # Lora 秩
+    lora_alpha=16,  # Lora alaph，具体作用参见 Lora 原理
+    lora_dropout=0.05,  # Dropout 比例
+    bias="none",
+)
+
+# 获取LoRA模型
+peft_model = get_peft_model(model, config)
+
+# 配置训练参数
+args = TrainingArguments(
+    output_dir="./output/Qwen2-VL-2B",
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=4,
+    logging_steps=10,
+    num_train_epochs=2,
+    save_steps=100,
+    learning_rate=1e-4,
+    save_on_each_node=True,
+    gradient_checkpointing=True,
+    report_to="none",
+)
+        
+# 设置SwanLab回调
+swanlab_callback = SwanLabCallback(
+    project="Qwen2-VL-finetune",
+    experiment_name="qwen2-vl-coco2014",
+    config={
+        "model": "https://modelscope.cn/models/Qwen/Qwen2-VL-2B-Instruct",
+        "dataset": "https://modelscope.cn/datasets/modelscope/coco_2014_caption/quickstart",
+        "github": "https://github.com/datawhalechina/self-llm",
+        "prompt": "COCO Yes: ",
+        "train_data_number": len(train_data),
+        "lora_rank": 64,
+        "lora_alpha": 16,
+        "lora_dropout": 0.1,
+    },
+)
+
+# 配置Trainer
+trainer = Trainer(
+    model=peft_model,
+    args=args,
+    train_dataset=train_dataset,
+    data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
+    callbacks=[swanlab_callback],
+)
+
+# 开启模型训练
+trainer.train()
+
+# ====================测试模式===================
+# 配置测试参数
+val_config = LoraConfig(
+    task_type=TaskType.CAUSAL_LM,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    inference_mode=True,  # 训练模式
+    r=64,  # Lora 秩
+    lora_alpha=16,  # Lora alaph，具体作用参见 Lora 原理
+    lora_dropout=0.05,  # Dropout 比例
+    bias="none",
+)
+
+# 获取测试模型
+val_peft_model = PeftModel.from_pretrained(model, model_id="./output/Qwen2-VL-2B/checkpoint-62", config=val_config)
+
+# 读取测试数据
+with open("data_vl_test.json", "r") as f:
+    test_dataset = json.load(f)
+
+test_image_list = []
+for item in test_dataset:
+    input_image_prompt = item["conversations"][0]["value"]
+    # 去掉前后的<|vision_start|>和<|vision_end|>
+    origin_image_path = input_image_prompt.split("<|vision_start|>")[1].split("<|vision_end|>")[0]
+    
+    messages = [{
+        "role": "user", 
+        "content": [
+            {
+                "type": "image", 
+                "image": origin_image_path
+            },
+            {
+                "type": "text",
+                "text": "COCO Yes:"
+            }
+        ]}]
+    
+    response = predict(messages, val_peft_model)
+    messages.append({"role": "assistant", "content": f"{response}"})
+    print(messages[-1])
+
+    test_image_list.append(swanlab.Image(origin_image_path, caption=response))
+
+swanlab.log({"Prediction": test_image_list})
+
+# 在Jupyter Notebook中运行时要停止SwanLab记录，需要调用swanlab.finish()
+swanlab.finish()
+```
+
+看到下面的进度条即代表训练开始：
+
+![05-4](./images/05-4.jpg)
+
+
+## 💻 训练结果演示
+
+![05-5](./images/05-5.jpg)
+
+从SwanLab图表中我们可以看到，lr的下降策略是线性下降，loss随epoch呈现下降趋势，而grad_norm则在上升。这种形态往往反映了模型有过拟合的风险，训练不要超过2个epoch。
+
+在`Prediction`图表中记录着模型最终的输出结果，可以看到模型在回答的风格上是用的COCO数据集的简短英文风格进行的描述：
+
+![05-6](./images/05-6.jpg)
+
+而同样的图像，没有被微调的模型输出结果如下：
+
+```
+1-没有微调：The image depicts a cozy living room with a rocking chair in the center, a bookshelf filled with books, and a table with a vase and a few other items. The walls are decorated with wallpaper, and there are curtains on the windows. The room appears to be well-lit, with sunlight streaming in from the windows.
+1-微调后：A living room with a rocking chair, a bookshelf, and a table with a vase and a bowl.
+
+2-没有微调：It looks like a family gathering or a party in a living room. There are several people sitting around a dining table, eating pizza. The room has a cozy and warm atmosphere.
+2-微调后：A group of people sitting around a dining table eating pizza.
+```
+
+可以明显看到微调后风格的变化。
+
+
+## 🧐 推理LoRA微调后的模型
+
+加载lora微调后的模型，并进行推理：
+
+```python
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+from qwen_vl_utils import process_vision_info
+from peft import PeftModel, LoraConfig, TaskType
+
+config = LoraConfig(
+    task_type=TaskType.CAUSAL_LM,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+    inference_mode=True,
+    r=64,  # Lora 秩
+    lora_alpha=16,  # Lora alaph，具体作用参见 Lora 原理
+    lora_dropout=0.05,  # Dropout 比例
+    bias="none",
+)
+
+# default: Load the model on the available device(s)
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "./Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto"
+)
+model = PeftModel.from_pretrained(model, model_id="./output/Qwen2-VL-2B/checkpoint-62", config=config)
+processor = AutoProcessor.from_pretrained("./Qwen/Qwen2-VL-2B-Instruct")
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "测试图像路径",
+            },
+            {"type": "text", "text": "COCO Yes:"},
+        ],
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+
+
+## 补充
+
+### 详细硬件配置和参数说明
+
+使用4张A100 40GB显卡，batch size为4，gradient accumulation steps为4，训练2个epoch的用时为1分钟57秒。
+
+![05-7](./images/05-7.jpg)
+
+### 注意
+
+- 在微调脚本中，`val_peft_model`加载的是一共固定的checkpoint文件，如果你添加了数据或超参数，请根据实际情况修改checkpoint文件路径。
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