Trae AI IDE深度评测：全栈开发者的智能编程革命

# DeepSeek R1生成基础架构
arch_prompt = """设计支持分布式调度的异步爬虫框架"""
r1_response = await client.query(
    model="deepseek_r1",
    prompt=arch_prompt,
    temperature=0.3
)
<h1>豆包1.5优化异常处理</h1><p>optimize_prompt = f"""优化以下代码的异常处理机制：n{r1_response.code}"""
doubao_response = await client.query(
model="doubao_1.5",
prompt=optimize_prompt,
format="diff"
)</p><h1>自定义模型进行安全检查</h1><p>security_report = client.analyze_code(
model="my_llama3",
code=doubao_response.code,
scan_level="strict"
)</p><p>return security_report.safe_code

该示例展示了：</p><ul><li>温度系数调控：R1使用低temperature保证架构稳定性</li><li>Diff格式输出：豆包大模型的代码迭代能力</li><li>安全沙箱机制：本地模型进行最终验证</li></ul><p>三、自定义模型集成：从HuggingFace到生产部署
Trae支持多种自定义模型集成方式，以下演示如何接入微调后的CodeLlama：</p><p>代码语言：python
代码运行次数：0
<svg fill="none" height="16" viewbox="0 0 16 16" width="16" xmlns="<a href="https://www.php.cn/link/c9041cfd2a40932691855abd98fd219a">http://www.w3.org/2000/svg"><path</a> d="M6.66666 10.9999L10.6667 7.99992L6.66666 4.99992V10.9999ZM7.99999 1.33325C4.31999 1.33325 1.33333 4.31992 1.33333 7.99992C1.33333 11.6799 4.31999 14.6666 7.99999 14.6666C11.68 14.6666 14.6667 11.6799 14.6667 7.99992C14.6667 4.31992 11.68 1.33325 7.99999 1.33325ZM7.99999 13.3333C5.05999 13.3333 2.66666 10.9399 2.66666 7.99992C2.66666 5.05992 5.05999 2.66659 7.99999 2.66659C10.94 2.66659 13.3333 5.05992 13.3333 7.99992C13.3333 10.9399 10.94 13.3333 7.99999 13.3333Z" fill="currentcolor"></path></svg>运行
<svg fill="none" height="16" viewbox="0 0 16 16" width="16" xmlns="<a href="https://www.php.cn/link/c9041cfd2a40932691855abd98fd219a">http://www.w3.org/2000/svg"><path</a> clip-rule="evenodd" d="M4.5 15.5V3.5H14.5V15.5H4.5ZM12.5 5.5H6.5V13.5H12.5V5.5ZM9.5 2.5H3.5V12.5H1.5V0.5H11.5V2.5H9.5Z" fill="currentcolor" fill-rule="evenodd"></path></svg>复制</p><pre class="brush:php;toolbar:false">
from trae.custom_models import register_model
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

class MyCodeLlamaAdapter:
    def __init__(self):
        self.tokenizer = AutoTokenizer.from_pretrained(
            "codellama/CodeLlama-7b-hf")
        self.model = AutoModelForCausalLM.from_pretrained(
            "codellama/CodeLlama-7b-hf",
            torch_dtype=torch.float16,
            device_map="auto")

    @register_model("codellama-7b")
    def generate(self, prompt, **kwargs):
        inputs = self.tokenizer(
            prompt,
            return_tensors="pt",
            max_length=4096,
            truncation=True
        ).to(self.model.device)

        outputs = self.model.generate(
            **inputs,
            max_new_tokens=512,
            temperature=kwargs.get('temp', 0.2),
            do_sample=True
        )

        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

# 在.traerc中配置：
# [models.custom]
# codellama-7b = { "class": "mymodule.MyCodeLlamaAdapter" }</code>

<code>function invite(address invitee) external {
    require(credits[msg.sender] >= 100, "Insufficient credits");
    credits[msg.sender] += 50;
    credits[invitee] += 100;
}

function claimReward(uint rewardId) external {
    require(rewardId = reward.creditCost, "Not enough credits");

    credits[msg.sender] -= reward.creditCost;
    emit RewardClaimed(msg.sender, rewardId);
}</code>

<code>

未来路线图显示，Trae计划在Q3支持：

- 实时协作编程模式
- 强化学习驱动的代码优化
- 硬件加速的本地128K上下文支持

立即体验：https://juejin.cn/loyalty-program-v1?inviteCode=5aRvC6mKRkzmByjQE9wsvCzitz4H5ycj&utm_campaign=jifen_invite

（声明：本文测试数据基于Trae v0.9.3预览版，实际效果可能因配置不同有所差异）</code>