个人玩家-Mac部署新版Qwen3-30B模型不完全指南

# -*- coding: utf-8 -*- # ai_models/lm_model.py —— LM Studio 本地部署 Qwen 模型调用（支持流式回调 + token & 耗时统计） import asyncio import os import inspect from typing import Optional, Callable, Dict, Any import aiohttp import json import time class QwenModelConfig: """ 封装 LM Studio 本地部署的 Qwen 模型（支持 qwen3-30b-a3b-2507 和 qwen3-coder-30b）。 使用 LM Studio 的 REST API (beta) 接口。 支持流式输出、同步/异步回调、token 统计与耗时分析。 """ def __init__( self, model_name: str = "qwen/qwen3-30b-a3b-2507", # 可选: "qwen/qwen3-30b-a3b-2507" 或 "qwen/qwen3-coder-30b" host: str = "192.168.1.104", port: int = 9851, system_prompt: str = "You are a helpful assistant.", timeout: int = 300 ): self.host = host self.port = port self.base_url = f"http://{host}:{port}" self.model_name = model_name self.system_prompt = system_prompt self.timeout = timeout self.session: Optional[aiohttp.ClientSession] = None # 模型名验证 valid_models = [ "qwen/qwen3-30b-a3b-2507", "qwen/qwen3-coder-30b" ] if model_name not in valid_models: raise ValueError( f"不支持的模型名称！请从以下模型中选择：{valid_models}" ) async def __aenter__(self): self.session = aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=self.timeout)) return self async def __aexit__(self, exc_type, exc_val, exc_tb): if self.session: await self.session.close() async def _get_available_models(self) -> list: """获取 LM Studio 本地加载的模型列表""" url = f"{self.base_url}/api/v0/models" try: async with self.session.get(url) as resp: if resp.status != 200: raise ConnectionError(f"获取模型列表失败: {resp.status} - {await resp.text()}") data = await resp.json() return [m["id"] for m in data.get("data", [])] except Exception as e: raise ConnectionError(f"连接 LM Studio API 失败: {e}") async def _check_model_loaded(self) -> bool: """检查指定模型是否已加载""" available_models = await self._get_available_models() return self.model_name in available_models async def _get_model_info(self) -> Dict[str, Any]: """获取指定模型的详细信息""" url = f"{self.base_url}/api/v0/models/{self.model_name}" try: async with self.session.get(url) as resp: if resp.status != 200: raise ConnectionError(f"获取模型信息失败: {resp.status} - {await resp.text()}") return await resp.json() except Exception as e: raise ConnectionError(f"连接模型信息接口失败: {e}") async def _estimate_tokens(self, text: str) -> int: """简单的 token 估算（中文约 1.5 字符/token，英文约 4 字符/token）""" if not text: return 0 # 统计中文字符数 chinese_chars = sum(1 for char in text if '\u4e00' <= char <= '\u9fff') other_chars = len(text) - chinese_chars # 估算 token 数：中文 1.5 字符/token，其他 4 字符/token estimated_tokens = int(chinese_chars / 1.5 + other_chars / 4) return max(estimated_tokens, 1) async def process_input( self, prompt: str, model_name: Optional[str] = None, temperature: float = 0.7, max_tokens: int = 1024, stream: bool = True, on_stream: Optional[Callable[[str], None]] = None ) -> "QwenResponse": model = model_name or self.model_name messages = [ {"role": "system", "content": self.system_prompt}, {"role": "user", "content": prompt}千问 Qwen 教程 ] # 检查模型是否可用 if not await self._check_model_loaded(): raise RuntimeError( f"模型 '{model}' 未加载到 LM Studio，请先在 LM Studio 中加载该模型。" ) # 构造请求体 payload = { "model": model, "messages": messages, "temperature": temperature, "max_tokens": max_tokens, "stream": stream } url = f"{self.base_url}/api/v0/chat/completions" collected_text = "" headers = {"Content-Type": "application/json"} # 开始计时 start_time = time.time() ttf_time = None first_token_received = False try: async with self.session.post(url, json=payload, headers=headers) as resp: if resp.status != 200: error_text = await resp.text() raise ConnectionError(f"API 请求失败: {resp.status} - {error_text}") if not stream: data = await resp.json() return QwenResponse(data, start_time) # 流式处理 final_data = {} usage = {} output_token_count = 0 # 手动计算输出 token 数 async for line in resp.content: if not line.strip(): continue if line.startswith(b"data: "): line = line[6:].strip() if line == b"[DONE]": break try: json_line = json.loads(line) # 处理流式内容 if "choices" in json_line and len(json_line["choices"]) > 0: choice = json_line["choices"][0] delta = choice.get("delta", {}).get("content", "") if delta: if not first_token_received: ttf_time = time.time() - start_time first_token_received = True collected_text += delta output_token_count += await self._estimate_tokens(delta) if on_stream: result = on_stream(delta) if inspect.isawaitable(result): await result # 检查是否有 finish_reason finish_reason = choice.get("finish_reason") if finish_reason: final_data["finish_reason"] = finish_reason # 尝试从流式数据中提取 usage（通常在最后一个数据包中） if "usage" in json_line: usage.update(json_line["usage"]) print(f" 从流式数据中获取到 usage: {usage}") except json.JSONDecodeError as e: print(f"JSON 解析错误: {e}, 原始数据: {line}") except Exception as e: print(f"解析流式数据出错: {e}") # 如果流式数据中没有 usage，则手动估算 if not usage: print("⚠️ 流式响应中未找到 usage 信息，使用估算值") input_text = self.system_prompt + " " + prompt estimated_input_tokens = await self._estimate_tokens(input_text) estimated_output_tokens = await self._estimate_tokens(collected_text) usage = { "prompt_tokens": estimated_input_tokens, "completion_tokens": estimated_output_tokens, "total_tokens": estimated_input_tokens + estimated_output_tokens } print(f" 估算的 token 统计: {usage}") # 构造最终响应 end_time = time.time() generation_time = end_time - start_time final_data = { "choices": [{ "message": {"content": collected_text}, "finish_reason": final_data.get("finish_reason", "stop") }], "usage": usage } # 计算性能统计 raw_stats = { "generation_time": generation_time, "time_to_first_token": ttf_time or 0, "tokens_per_second": usage.get("completion_tokens", 0) / generation_time if generation_time > 0 else 0 } return QwenResponse(final_data, start_time, raw_stats) except Exception as e: print(f"请求异常: {e}") return QwenResponse({"error": str(e)}, start_time) class QwenResponse: """增强版 Qwen 响应对象，支持 token 与耗时统计""" def __init__( self, response_data: Dict[str, Any], start_time: float, raw_stats: Optional[Dict[str, Any]] = None ): self.raw_response = response_data self.start_time = start_time # 提取文本 if "choices" in response_data and len(response_data["choices"]) > 0: self.text = response_data["choices"][0]["message"]["content"] else: self.text = str(response_data) # 从 response_data 提取 usage usage = response_data.get("usage", {}) self.input_tokens = usage.get("prompt_tokens", 0) self.output_tokens = usage.get("completion_tokens", 0) self.total_tokens = usage.get("total_tokens", 0) # 从 raw_stats 或 response_data 的 stats 字段提取 stats = raw_stats or response_data.get("stats", {}) self.generation_time = stats.get("generation_time", time.time() - start_time) self.time_to_first_token = stats.get("time_to_first_token", 0) self.tokens_per_second = stats.get("tokens_per_second", 0) # 可选：finish_reason if "choices" in response_data and len(response_data["choices"]) > 0: self.finish_reason = response_data["choices"][0].get("finish_reason", "unknown") else: self.finish_reason = "unknown" def __str__(self) -> str: return f"QwenResponse(text='{self.text[:50]}...')" def print_stats(self): """打印 token 与耗时统计信息""" print("\n" + "="*60) print(" LM Studio 模型推理统计信息") print("="*60) print(f" 输入 token 数量: {self.input_tokens}") print(f" 输出 token 数量: {self.output_tokens}") print(f" 总 token 数量: {self.total_tokens}") print(f" 生成耗时: {self.generation_time:.2f} 秒") print(f" 首个 token 耗时: {self.time_to_first_token:.3f} 秒") print(f" 吞吐量: {self.tokens_per_second:.1f} tokens/秒") print(f" 完成原因: {self.finish_reason}") print("="*60) # ======================= 自测入口 ======================= def main(): async def test(): async with QwenModelConfig( model_name="qwen/qwen3-coder-30b", # 将这里的域名改成你的局域网的IP即可： host="192.168.1.xx", port=9851 ) as qwen: print("\n>>> 流式模式测试") def printer(delta): print(delta, end="", flush=True) prompt = "你好，请简单介绍一下你自己" # 测试流式模式 response = await qwen.process_input( prompt=prompt, stream=True, # 改为 True 测试流式 on_stream=printer, temperature=0.5, max_tokens=1024 ) print("\n\n[最终汇总] ", response.text[:100] + "..." if len(response.text) > 100 else response.text) response.print_stats() print("\n" + "="*60) print(">>> 非流式模式测试") # 测试非流式模式 response2 = await qwen.process_input( prompt=prompt, stream=False, temperature=0.5, max_tokens=1024 ) print(f"[非流式结果] {response2.text[:100]}..." if len(response2.text) > 100 else response2.text) response2.print_stats() asyncio.run(test()) if __name__ == "__main__": main() 

>>> 流式模式测试 你好！我是通义千问，阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我能够回答问题、创作文字，比如写故事、写公文、写邮件、写剧本、逻辑推理、编程等等，还能表达观点，玩游戏等。如果你有任何问题或需要帮助，欢迎随时告诉我！⚠️ 流式响应中未找到 usage 信息，使用估算值 估算的 token 统计: {'prompt_tokens': 15, 'completion_tokens': 69, 'total_tokens': 84} [最终汇总] 你好！我是通义千问，阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我能够回答问题、创作文字，比如写故事、写公文、写邮件、写剧本、逻辑推理、编程等等，还能表达观点，玩游戏等。如果你有任何问题或... ============================================================ LM Studio 模型推理统计信息 ============================================================ 输入 token 数量: 15 输出 token 数量: 69 总 token 数量: 84 生成耗时: 1.60 秒 首个 token 耗时: 0.515 秒 吞吐量: 43.2 tokens/秒 完成原因: stop ============================================================ ============================================================ >>> 非流式模式测试 [非流式结果] 你好！我是通义千问，阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。我能够回答问题、创作文字，比如写故事、写公文、写邮件、写剧本、逻辑推理、编程等等，还能表达观点，玩游戏等。如果你有任何问题或... ============================================================ LM Studio 模型推理统计信息 ============================================================ 输入 token 数量: 24 输出 token 数量: 67 总 token 数量: 91 生成耗时: 1.19 秒 首个 token 耗时: 0.038 秒 吞吐量: 57.0 tokens/秒 完成原因: stop ============================================================

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