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Model: AngelWarmSmile123/yao-bao-bao8-ALL Source: Original Platform
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259
Angel-Embrace-Me-Warm-Smile/chat.py
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259
Angel-Embrace-Me-Warm-Smile/chat.py
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"""
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Angel Embrace - 16质点心理辅导 AI (本地CPU推理版)
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=============================================
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基座模型: Qwen/Qwen3.5-35B-A3B (本地目录)
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LoRA权重: lora_output/ (训练完成版)
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推理模式: CPU (适用于无GPU/显存不足的环境)
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使用方法: python chat.py
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"""
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import sys
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import os
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# === 配置 ===
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BASE_MODEL = "Qwen/Qwen3.5-35B-A3B"
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# 本地模型路径（优先使用）
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LOCAL_MODEL_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Qwen3.5-35B-A3B")
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LORA_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "lora_output")
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MAX_NEW_TOKENS = 512 # CPU推理，减小以加快速度
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CONTEXT_LENGTH = 2048 # 上下文长度（越长越吃内存）
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SYSTEM_PROMPT = """你是Angel Embrace Me Warm Smile，一个基于16质点卡巴拉生命树框架的心理辅导AI系统。
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你的核心能力：
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1. **D1 意图识别** - 理解用户真实需求
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2. **D4 安全检测** - 识别自残/自杀/危机信号，立即干预
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3. **D8 情感效价分析** - 分析情绪强度和方向
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4. **D10 学术引用标准** - 引用心理学研究支持建议
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5. **D16 充分输出** - 给出完整、可操作的回复
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回复风格：温暖、专业、不评判、有安全感。
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使用16质点框架进行结构化思考，但输出要自然像人说话。
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重要安全规则：
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- 检测到任何自残/自杀倾向时，必须立即表达关心并提供危机热线
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- 不给出医学诊断，建议寻求专业帮助
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- 始终保持共情和支持的语气"""
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def check_dependencies():
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"""检查并安装依赖"""
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missing = []
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try:
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import torch
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except ImportError:
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missing.append("torch")
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try:
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from transformers import AutoModelForCausalLM, AutoTokenizer
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except ImportError:
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missing.append("transformers")
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try:
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import safetensors
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except ImportError:
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missing.append("safetensors")
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if missing:
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print(f"⚠️ 缺少依赖包: {', '.join(missing)}")
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print("正在自动安装...")
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os.system('pip install torch --index-url https://download.pytorch.org/whl/cpu')
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os.system('pip install transformers accelerate safetensors')
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print("✅ 依赖安装完成，请重新运行此脚本")
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sys.exit(1)
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def apply_lora_manually(model, lora_path):
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"""手动合并LoRA权重到基座模型（不依赖peft）"""
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import torch
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import json
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from safetensors.torch import load_file
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print(f" 📖 读取 LoRA 配置...")
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config_path = os.path.join(lora_path, "adapter_config.json")
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with open(config_path, "r") as f:
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lora_config = json.load(f)
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lora_alpha = lora_config.get("lora_alpha", 256)
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lora_r = lora_config.get("r", 64)
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scaling = lora_alpha / lora_r
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print(f" LoRA: r={lora_r}, alpha={lora_alpha}, scaling={scaling:.3f}")
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# 加载LoRA权重
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adapter_file = os.path.join(lora_path, "adapter_model.safetensors")
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if not os.path.isfile(adapter_file):
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raise FileNotFoundError(f"找不到 {adapter_file}")
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print(f" 📂 加载 LoRA 权重文件...")
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lora_state = load_file(adapter_file, device="cpu")
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# 找到所有LoRA对（lora_A + lora_B）并合并
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lora_a_keys = [k for k in lora_state if ".lora_A." in k]
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merged = 0
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for a_key in lora_a_keys:
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b_key = a_key.replace(".lora_A.", ".lora_B.")
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if b_key not in lora_state:
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continue
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# 从key推断模型层路径
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# 格式: base_model.model.model.layers.X.self_attn.q_proj.lora_A.weight
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parts = a_key.split(".")
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# 去掉 base_model.model 前缀和 .lora_A.weight 后缀
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# 找到实际模块路径
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try:
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lora_a_idx = parts.index("lora_A")
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except ValueError:
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continue
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module_parts = parts[:lora_a_idx]
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# 去掉开头的 base_model.model
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if module_parts[:2] == ["base_model", "model"]:
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module_parts = module_parts[2:]
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module_path = ".".join(module_parts)
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# 找到对应的模型参数
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try:
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module = model
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for p in module_parts:
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module = getattr(module, p)
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weight = module.weight
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except (AttributeError, TypeError):
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continue
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lora_A = lora_state[a_key].to(torch.float32)
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lora_B = lora_state[b_key].to(torch.float32)
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delta = (lora_B @ lora_A) * scaling
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with torch.no_grad():
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weight.data += delta.to(weight.dtype)
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merged += 1
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print(f" ✅ 成功合并 {merged} 个 LoRA 层")
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return model
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def load_model():
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"""加载基座模型 + 手动合并LoRA权重"""
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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print("=" * 60)
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print(" Angel Embrace - 16质点心理辅导 AI")
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print(" CPU推理模式启动中...")
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print("=" * 60)
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# 确定模型加载路径
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if os.path.isdir(LOCAL_MODEL_DIR) and os.path.isfile(os.path.join(LOCAL_MODEL_DIR, "config.json")):
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model_path = LOCAL_MODEL_DIR
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print(f"\n📂 使用本地模型: {model_path}")
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else:
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model_path = BASE_MODEL
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print(f"\n🌐 本地模型未找到，将从 HuggingFace 下载: {BASE_MODEL}")
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# 1. 加载 Tokenizer
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print("\n📥 [1/4] 正在加载 Tokenizer...")
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tokenizer = AutoTokenizer.from_pretrained(
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model_path,
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trust_remote_code=True,
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)
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print(" ✅ Tokenizer 加载完成")
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# 2. 加载基座模型 (CPU模式)
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print(f"\n📥 [2/4] 正在加载基座模型（约72GB，需要几分钟）...")
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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dtype=torch.float32, # CPU用float32更稳定
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device_map="cpu", # 强制用CPU
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trust_remote_code=True,
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low_cpu_mem_usage=True,
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)
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print(" ✅ 基座模型加载完成")
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# 3. 手动合并 LoRA 权重（不依赖peft）
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print(f"\n📥 [3/4] 正在合并 LoRA 权重...")
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if os.path.isdir(LORA_PATH) and os.path.isfile(os.path.join(LORA_PATH, "adapter_model.safetensors")):
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model = apply_lora_manually(model, LORA_PATH)
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else:
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print(f" ⚠️ 未找到 LoRA 权重 ({LORA_PATH})，使用原始基座模型")
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model.eval()
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print(f"\n✅ [4/4] 模型就绪！进入对话模式\n")
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return model, tokenizer
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def chat_loop(model, tokenizer):
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"""主对话循环"""
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import torch
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT}
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]
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print("=" * 60)
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print(" 输入消息开始对话 | 输入 clear 清空历史 | 输入 quit 退出")
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print("=" * 60)
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while True:
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try:
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user_input = input("\n你: ").strip()
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except (EOFError, KeyboardInterrupt):
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print("\n👋 再见！照顾好自己 💙")
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break
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if not user_input:
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continue
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if user_input.lower() in ("quit", "exit", "q", "退出"):
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print("\n👋 再见！照顾好自己 💙")
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break
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if user_input.lower() in ("clear", "cls", "清空"):
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messages = [{"role": "system", "content": SYSTEM_PROMPT}]
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print(" 🗑️ 对话历史已清空")
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continue
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messages.append({"role": "user", "content": user_input})
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(text, return_tensors="pt").to("cpu")
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if inputs["input_ids"].shape[1] > CONTEXT_LENGTH:
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print(f" ⚠️ 输入过长({inputs['input_ids'].shape[1]} token)，截断到 {CONTEXT_LENGTH}")
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inputs = {k: v[:, -CONTEXT_LENGTH:] for k, v in inputs.items()}
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print("\nAngel Embrace: ", end="", flush=True)
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with torch.no_grad():
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output_ids = model.generate(
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**inputs,
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max_new_tokens=MAX_NEW_TOKENS,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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repetition_penalty=1.1,
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pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
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)
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new_tokens = output_ids[0][inputs["input_ids"].shape[-1]:]
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response = tokenizer.decode(new_tokens, skip_special_tokens=True)
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print(response)
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messages.append({"role": "assistant", "content": response})
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# 限制历史长度（保留system + 最近6轮）
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if len(messages) > 13:
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messages = [messages[0]] + messages[-12:]
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def main():
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"""入口"""
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print("🔍 检查依赖环境...")
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check_dependencies()
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model, tokenizer = load_model()
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chat_loop(model, tokenizer)
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if __name__ == "__main__":
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main()
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