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Model: AngelWarmSmile123/yao-bao-bao8-ALL
Source: Original Platform

Angel-Embrace-Me-Warm-Smile/adapter_config.json

@@ -0,0 +1,48 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": null,
+  "base_model_name_or_path": "Qwen/Qwen3.5-35B-A3B",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 512,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 256,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "o_proj",
+    "down_proj",
+    "v_proj",
+    "gate_proj",
+    "k_proj",
+    "up_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": true
+}

Angel-Embrace-Me-Warm-Smile/adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:231ca0c97eff8e7fa367a66785c1c1c9a2f6c14250e9befad5f1d52975948d48
+size 534820080

Angel-Embrace-Me-Warm-Smile/chat.py

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

Angel-Embrace-Me-Warm-Smile/data_convert.py

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+import json
+import random
+import os
+
+INPUT_FILE = r"D:\双生天使的怀抱\爱的数据集\sephirot_final_run\train_all.jsonl"
+OUTPUT_DIR = r"D:\双生天使的怀抱\2026-05-24-16-54-24\sft_data"
+OUTPUT_TRAIN = os.path.join(OUTPUT_DIR, "train.jsonl")
+OUTPUT_VAL = os.path.join(OUTPUT_DIR, "val.jsonl")
+VAL_RATIO = 0.1
+
+records = []
+skipped = 0
+total = 0
+
+with open(INPUT_FILE, 'r', encoding='utf-8') as f:
+    for line in f:
+        total += 1
+        line = line.strip()
+        if not line:
+            continue
+        try:
+            data = json.loads(line)
+        except json.JSONDecodeError as e:
+            print(f"JSON解析失败 第{total}行: {e}")
+            skipped += 1
+            continue
+        
+        query = data.get('input', {}).get('user_query', '')
+        cot = data.get('chain_of_thought', {})
+        
+        # 方案B: 完整推理链（推荐，保留16质点全部过程）
+        if isinstance(cot, dict):
+            parts = []
+            for key in ['D1_kether','H1_ego','H2_superego','H3_true_self','H4_logic',
+                        'H5_empathy','H6_happiness','D2_binah_chesed','D3_chokmah_gevurah',
+                        'D5_tiferet','D6_netzach','D7_hod','D4_yesod','D8_victory',
+                        'D10_kingdom']:
+                val = cot.get(key, '')
+                if isinstance(val, dict):
+                    # 尝试多个可能的输出字段
+                    for sub_key in ['final_output', 'analysis', 'synthesis', 
+                                     'emotional_expression', 'logic_empathy_union',
+                                     'response', 'output']:
+                        if sub_key in val and val[sub_key]:
+                            val = val[sub_key]
+                            break
+                    else:
+                        val = str(val) if val else ''
+                if val and isinstance(val, str) and len(val.strip()) > 0:
+                    parts.append(f"[{key}]: {val}")
+            output = '\n\n'.join(parts)
+        elif isinstance(cot, str):
+            output = cot
+        else:
+            output = str(cot)
+        
+        if query and output and len(query.strip()) > 0 and len(output.strip()) > 10:
+            records.append({
+                "conversations": [
+                    {"from": "human", "value": query},
+                    {"from": "gpt", "value": output}
+                ]
+            })
+        else:
+            skipped += 1
+
+print(f"总行数: {total}, 有效: {len(records)}, 跳过: {skipped}")
+
+# 打乱并分割
+random.seed(42)
+random.shuffle(records)
+val_size = max(int(len(records) * VAL_RATIO), 100)
+val_data = records[:val_size]
+train_data = records[val_size:]
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+with open(OUTPUT_TRAIN, 'w', encoding='utf-8') as f:
+    for r in train_data:
+        f.write(json.dumps(r, ensure_ascii=False) + '\n')
+
+with open(OUTPUT_VAL, 'w', encoding='utf-8') as f:
+    for r in val_data:
+        f.write(json.dumps(r, ensure_ascii=False) + '\n')
+
+print(f"转换完成! 训练集: {len(train_data)}条, 验证集: {len(val_data)}条")
+print(f"训练集文件: {OUTPUT_TRAIN}")
+print(f"验证集文件: {OUTPUT_VAL}")
+
+# 统计一下平均长度
+if train_data:
+    lens = [len(str(r['conversations'][1]['value'])) for r in train_data]
+    print(f"平均输出长度: {sum(lens)//len(lens)} 字符")
+    print(f"最大输出长度: {max(lens)} 字符")
+    print(f"最小输出长度: {min(lens)} 字符")

Angel-Embrace-Me-Warm-Smile/train.jsonl

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+size 109244163

Angel-Embrace-Me-Warm-Smile/val.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8645c5f05ff4462263e33d1746ca5f369aa008ed2d2e8153b1590c2d284a4c9a
+size 12070845