智谱 GLM-5.2 深度解析：百万 Token 上下文 + MIT 开源，中国 AI 开源的里程碑

# 异步 Agent RL 框架伪代码
import asyncio
from collections import deque

class AsyncAgentRL:
    def __init__(self, model, env, buffer_size=10000):
        self.model = model          # 策略模型
        self.teacher = model.copy() # 教师模型快照
        self.env = env              # 环境（编译器/测试框架）
        self.buffer = deque(maxlen=buffer_size)
    
    async def inference_pipeline(self):
        """推理流水线：持续生成动作"""
        while True:
            state = self.env.get_latest_state()
            # 模型生成动作（不等待环境反馈）
            action = self.model.generate(state)
            # 同时计算教师模型的输出（用于蒸馏）
            teacher_action = self.teacher.generate(state)
            self.buffer.append({
                'state': state,
                'action': action,
                'teacher_action': teacher_action,
            })
            await asyncio.sleep(0)  # 让出控制权
    
    async def environment_pipeline(self):
        """环境交互流水线：异步执行动作"""
        while True:
            if len(self.buffer) > 0:
                experience = self.buffer[-1]
                # 在环境中执行动作
                result = await self.env.execute(experience['action'])
                # 计算奖励
                reward = self.compute_reward(result)
                experience['reward'] = reward
                experience['next_state'] = self.env.get_state()
            await asyncio.sleep(0)
    
    def train_step(self, batch):
        """训练步骤：RL 损失 + 蒸馏损失"""
        rl_loss = self.compute_rl_loss(batch)
        # 跨阶段蒸馏：防止灾难性遗忘
        distill_loss = self.compute_kl_divergence(
            self.model.output, batch['teacher_action']
        )
        # 加权组合
        total_loss = rl_loss + 0.3 * distill_loss
        total_loss.backward()
        self.model.optimizer.step()
    
    async def train(self, num_steps=100000):
        # 并行启动两条流水线
        await asyncio.gather(
            self.inference_pipeline(),
            self.environment_pipeline()
        )

# 安装 Ollama（如果尚未安装）
curl -fsSL https://ollama.com/install.sh | sh

# 拉取 GLM-5.2 模型（INT4 量化版）
ollama pull glm-5-2:quant4

# 启动交互式对话
ollama run glm-5-2:quant4

# 使用 API 模式
curl http://localhost:11434/api/generate -d '{
  "model": "glm-5-2:quant4",
  "prompt": "用 Python 实现一个快速排序算法",
  "stream": false
}'

# 配合 Claude Code 使用
# 设置环境变量指向本地 Ollama
export ANTHROPIC_BASE_URL=http://localhost:11434/v1
export ANTHROPIC_API_KEY=ollama
claude-code --model glm-5-2:quant4

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# 加载模型和分词器
model_name = "zhipu-ai/glm-5-2"  # HuggingFace 模型路径
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# 加载模型（使用 device_map 自动分配 GPU）
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

# 推理函数
def generate(prompt: str, max_tokens: int = 2048, 
             thinking: str = "standard") -> str:
    """使用 GLM-5.2 生成文本
    
    Args:
        prompt: 输入提示
        max_tokens: 最大生成 Token 数
        thinking: 思考强度 (standard/high/max)
    """
    # 构建思考强度提示
    thinking_prefix = {
        "standard": "",
        "high": "请深入思考后回答：",
        "max": "请逐步推理后回答："
    }
    
    full_prompt = thinking_prefix.get(thinking, "") + prompt
    
    inputs = tokenizer(full_prompt, return_tensors="pt")
    inputs = {k: v.to(model.device) for k, v in inputs.items()}
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_tokens,
            temperature=0.7,
            top_p=0.9,
        )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# 使用示例
result = generate(
    "用 Python 实现一个 LRU 缓存",
    max_tokens=4096,
    thinking="high"
)
print(result)

// GLM-5.2 TypeScript SDK 接入示例
import OpenAI from 'openai';

// 智谱 API 兼容 OpenAI 格式
const client = new OpenAI({
  apiKey: process.env.ZHIPU_API_KEY,
  baseURL: 'https://api.zhipuai.cc/v1',
});

// 基础对话
async function chat(prompt: string, thinking: 'standard' | 'high' | 'max' = 'standard') {
  const response = await client.chat.completions.create({
    model: 'glm-5-2',
    messages: [
      { role: 'system', content: '你是一个专业的 AI 助手。' },
      { role: 'user', content: prompt },
    ],
    max_tokens: 4096,
    // 智谱扩展参数：思考强度
    extra_body: { thinking },
  });
  
  return response.choices[0].message.content;
}

// 代码生成（使用高思考强度）
async function generateCode(requirement: string) {
  return chat(`请根据以下需求生成代码：\n${requirement}`, 'high');
}

// 代码审查（使用最大思考强度）
async function reviewCode(code: string) {
  return chat(`请审查以下代码，指出潜在问题和改进建议：\n${code}`, 'max');
}

// 使用示例
async function main() {
  // 简单问答
  const answer = await chat('什么是 MoE 架构？');
  console.log('问答结果:', answer);
  
  // 代码生成
  const code = await generateCode('用 TypeScript 实现一个 LRU 缓存');
  console.log('生成代码:', code);
  
  // 代码审查
  const review = await reviewCode('function add(a, b) { return a + b; }');
  console.log('审查结果:', review);
}

main().catch(console.error);

# 方法 1：通过 Ollama 本地部署
export ANTHROPIC_BASE_URL=http://localhost:11434/v1
export ANTHROPIC_API_KEY=ollama
claude-code --model glm-5-2:quant4

# 方法 2：通过智谱 API（下周上线）
export ANTHROPIC_BASE_URL=https://api.zhipuai.cc/v1
export ANTHROPIC_API_KEY=your-zhipu-api-key
claude-code --model glm-5-2

// VS Code settings.json
{
  "cline.apiProvider": "openai",
  "cline.openAiBaseUrl": "https://api.zhipuai.cc/v1",
  "cline.openAiApiKey": "your-zhipu-api-key",
  "cline.openAiModelId": "glm-5-2"
}

# .github/workflows/code-review.yml
name: AI Code Review
on: [pull_request]

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: AI Code Review with GLM-5.2
        env:
          ZHIPU_API_KEY: \${{ secrets.ZHIPU_API_KEY }}
        run: |
          # 获取 PR diff
          git diff origin/main...HEAD > diff.txt
          
          # 调用 GLM-5.2 进行代码审查
          python review.py --model glm-5-2 \\
            --api-key $ZHIPU_API_KEY \\
            --diff diff.txt \\
            --thinking high \\
            --output review.md
          
          # 将审查结果添加到 PR 评论
          gh pr comment --body-file review.md

import os
import requests

ZHIPU_API_URL = "https://api.zhipuai.cc/v1/chat/completions"
API_KEY = "your-zhipu-api-key"

def migrate_file(source_path: str, target_language: str) -> str:
    """使用 GLM-5.2 将代码文件迁移到目标语言"""
    with open(source_path, 'r') as f:
        source_code = f.read()
    
    source_ext = os.path.splitext(source_path)[1].lstrip('.')
    
    response = requests.post(
        ZHIPU_API_URL,
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json"
        },
        json={
            "model": "glm-5-2",
            "messages": [
                {
                    "role": "system",
                    "content": f"你是一个专业的代码迁移工程师。将 {source_ext} 代码迁移到 {target_language}，保持相同的功能和逻辑结构。"
                },
                {
                    "role": "user",
                    "content": f"请将以下 {source_ext} 代码迁移到 {target_language}：\\n\\n{source_code}"
                }
            ],
            "max_tokens": 8192,
            "thinking": "high"
        }
    )
    
    return response.json()["choices"][0]["message"]["content"]

# 批量迁移
source_dir = "./src_python"
target_dir = "./src_rust"
os.makedirs(target_dir, exist_ok=True)

for filename in os.listdir(source_dir):
    if filename.endswith('.py'):
        result = migrate_file(
            os.path.join(source_dir, filename),
            "Rust"
        )
        output_path = os.path.join(
            target_dir,
            filename.replace('.py', '.rs')
        )
        with open(output_path, 'w') as f:
            f.write(result)
        print(f"✅ {filename} → {filename.replace('.py', '.rs')}")