docs(chapter5): 更新模型文档并添加数据处理脚本

- 更新LLaMA2模型文档，修正图片引用和编号
- 添加Attention结构示意图
- 新增数据处理脚本download_dataset.sh和deal_dataset.py
- 优化文档中的代码示例说明

docs/chapter5/code/deal_dataset.py

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+import os
+import json
+from tqdm import tqdm
+
+# pretrain_data 为运行download_dataset.sh时，下载的pretrain_data本地路径
+pretrain_data = 'your local pretrain_data'
+output_pretrain_data = 'seq_monkey_datawhale.jsonl'
+
+# sft_data 为运行download_dataset.sh时，下载的sft_data本地路径
+sft_data = 'your local sft_data'
+output_sft_data = 'BelleGroup_sft.jsonl'
+
+# 1 处理预训练数据
+def split_text(text, chunk_size=512):
+    """将文本按指定长度切分成块"""
+    return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
+
+with open(output_pretrain_data, 'a', encoding='utf-8') as pretrain:
+    with open(pretrain_data, 'r', encoding='utf-8') as f:
+        data = f.readlines()
+        for line in tqdm(data, desc=f"Processing lines in {pretrain_data}", leave=False):  # 添加行级别的进度条
+            line = json.loads(line)
+            text = line['text']
+            chunks = split_text(text)
+            for chunk in chunks:
+                pretrain.write(json.dumps({'text': chunk}, ensure_ascii=False) + '\n')
+
+# 2 处理SFT数据
+def convert_message(data):
+    """
+    将原始数据转换为标准格式
+    """
+    message = [
+        {"role": "system", "content": "你是一个AI助手"},
+    ]
+    for item in data:
+        if item['from'] == 'human':
+            message.append({'role': 'user', 'content': item['value']})
+        elif item['from'] == 'assistant':
+            message.append({'role': 'assistant', 'content': item['value']})
+    return message
+
+with open(output_sft_data, 'a', encoding='utf-8') as sft:
+    with open(sft_data, 'r') as f:
+        data = f.readlines()
+        for item in tqdm(data, desc="Processing", unit="lines"):
+            item = json.loads(item)
+            message = convert_message(item['conversations'])
+            sft.write(json.dumps(message, ensure_ascii=False) + '\n')