DeepSeek V4-Flash正式版深度评测：13B小模型打趴1.6T旗舰，后训练才是真正的护城河

# 传统的SFT：模型生成代码，不看执行结果
prompt = "编写一个Python函数，计算斐波那契数列的第n项"
response = model.generate(prompt)
# 训练信号：模型输出 vs 参考答案

# 执行反馈SFT：模型生成代码，执行后看结果
prompt = "编写一个Python函数，计算斐波那契数列的第n项"
response = model.generate(prompt)
try:
    exec(response)  # 实际执行代码
    result = test_fibonacci(response)  # 运行测试
    # 训练信号：模型输出 + 执行结果（成功/失败/错误信息）
except Exception as e:
    # 训练信号：模型输出 + 错误信息

# 过程奖励模型伪代码
class ProcessRewardModel:
    def score_step(self, step_input, step_output, context):
        '''
        对单个步骤进行评分
        返回0-1之间的分数
        '''
        features = self.extract_features(step_input, step_output, context)
        score = self.model.predict(features)
        return score
    
    def score_trajectory(self, steps):
        '''
        对整个执行轨迹进行评分
        返回每个步骤的分数列表
        '''
        scores = []
        context = []
        for step in steps:
            score = self.score_step(step.input, step.output, context)
            scores.append(score)
            context.append(step)
        return scores

import requests

API_KEY = "your-api-key"
API_BASE = "https://api.deepseek.com/v1"

def chat_with_thinking(prompt):
    '''使用思考模式调用DeepSeek V4-Flash'''
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    
    payload = {
        "model": "deepseek-v4-flash",
        "messages": [
            {"role": "user", "content": prompt}
        ],
        "thinking": {
            "type": "enabled",
            "reasoning_effort": "high"
        },
        "temperature": 0.1
    }
    
    response = requests.post(
        f"{API_BASE}/chat/completions",
        headers=headers,
        json=payload
    )
    
    result = response.json()
    
    # 提取思考过程和最终回答
    if "thinking" in result["choices"][0]["message"]:
        thinking = result["choices"][0]["message"]["thinking"]
        print(f"思考过程: {thinking}")
    
    answer = result["choices"][0]["message"]["content"]
    return answer

# 使用示例
result = chat_with_thinking(
    "有三个盒子，一个装有两个金币，一个装有两个银币，"
    "一个装有一个金币和一个银币。你随机选了一个盒子，"
    "从中随机取出一个硬币，发现是金币。"
    "请问这个盒子另一个硬币也是金币的概率是多少？"
)
print(f"最终回答: {result}")

import requests
import json
from datetime import datetime

API_KEY = "your-api-key"
API_BASE = "https://api.deepseek.com/v1"

# 定义工具函数
TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "search_web",
            "description": "搜索互联网获取最新信息",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "搜索关键词"
                    },
                    "num_results": {
                        "type": "integer",
                        "description": "返回结果数量",
                        "default": 5
                    }
                },
                "required": ["query"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "calculate",
            "description": "执行数学计算",
            "parameters": {
                "type": "object",
                "properties": {
                    "expression": {
                        "type": "string",
                        "description": "数学表达式，如 '2 + 3 * 4'"
                    }
                },
                "required": ["expression"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "get_current_time",
            "description": "获取当前日期和时间",
            "parameters": {
                "type": "object",
                "properties": {
                    "timezone": {
                        "type": "string",
                        "description": "时区，如 'Asia/Shanghai'",
                        "default": "UTC"
                    }
                }
            }
        }
    }
]

def execute_tool(tool_name, arguments):
    '''执行工具函数'''
    if tool_name == "search_web":
        # 模拟搜索
        return json.dumps({
            "results": [
                {"title": "结果1", "snippet": f"关于 {arguments['query']} 的最新信息"},
                {"title": "结果2", "snippet": f"{arguments['query']} 的详细分析"}
            ]
        }, ensure_ascii=False)
    elif tool_name == "calculate":
        try:
            result = eval(arguments["expression"])
            return json.dumps({"result": result})
        except Exception as e:
            return json.dumps({"error": str(e)})
    elif tool_name == "get_current_time":
        return json.dumps({"time": datetime.now().isoformat()})
    return json.dumps({"error": "Unknown tool"})

def agent_loop(user_query):
    '''Agent主循环'''
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    
    messages = [{"role": "user", "content": user_query}]
    max_iterations = 10
    
    for i in range(max_iterations):
        payload = {
            "model": "deepseek-v4-flash",
            "messages": messages,
            "tools": TOOLS,
            "temperature": 0.1
        }
        
        response = requests.post(
            f"{API_BASE}/chat/completions",
            headers=headers,
            json=payload
        ).json()
        
        message = response["choices"][0]["message"]
        messages.append(message)
        
        # 检查是否有工具调用
        if message.get("tool_calls"):
            for tool_call in message["tool_calls"]:
                tool_name = tool_call["function"]["name"]
                arguments = json.loads(tool_call["function"]["arguments"])
                
                print(f"[Agent] 调用工具: {tool_name}({arguments})")
                
                result = execute_tool(tool_name, arguments)
                
                messages.append({
                    "role": "tool",
                    "tool_call_id": tool_call["id"],
                    "content": result
                })
        else:
            # 没有工具调用，返回最终回答
            return message["content"]
    
    return "Agent达到最大迭代次数，未能完成任务。"

# 使用示例
result = agent_loop(
    "请帮我搜索2026年AI领域的最新进展，"
    "然后用计算器算一下 (365 + 240) * 3.14 的结果，"
    "最后告诉我当前时间。"
)
print(f"
最终回答: {result}")