diff --git a/.cnb.yml b/.cnb.yml
new file mode 100644
index 0000000..fa90f13
--- /dev/null
+++ b/.cnb.yml
@@ -0,0 +1,15 @@
+version: "3"
+tasks:
+  - name: "训练小狸AI"
+    image:
+      dockerfile: .ide/Dockerfile
+    resources:
+      cpu: 8
+      memory: 32G
+      gpu: 1
+    env:
+      CUDA_VISIBLE_DEVICES: "0"
+      PYTHONUNBUFFERED: "1"
+    commands:
+      - cd /workspace && git clone https://cnb.cool/ETeam_sy/xiaoli-llm-chat.git
+      - cd xiaoli-llm-chat && python3 scripts/train_cnb.py --epochs 50 --batch_size 16
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index cdb757d..d00222d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
+# 已跟踪
 # 缓存和日志
 __pycache__/
 *.pyc
@@ -44,4 +45,17 @@ mega_*.log
 mimo_*.log
 knowledge_*.log
 train_*.log
-web_server*.log
\ No newline at end of file
+web_server*.log
+# CNB 配置 — 强制跟踪关键文件
+.ide/
+!/.ide/Dockerfile
+!/.cnb.yml
+
+# 训练输出目录
+general_outputs/
+high_temp_outputs/
+multiturn_outputs/
+
+# 本地临时
+fun_test.py
+*.log
diff --git a/.ide/Dockerfile b/.ide/Dockerfile
new file mode 100644
index 0000000..de9cb1c
--- /dev/null
+++ b/.ide/Dockerfile
@@ -0,0 +1,13 @@
+FROM pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime
+
+# 基础工具
+RUN apt-get update && apt-get install -y git curl wget nano && rm -rf /var/lib/apt/lists/*
+
+# tqdm用于进度条
+RUN pip install --no-cache-dir tqdm numpy requests
+
+# 设置工作目录
+WORKDIR /workspace
+
+# 验证CUDA
+RUN python3 -c "import torch; print(f'PyTorch {torch.__version__}, CUDA: {torch.cuda.is_available()}')"
\ No newline at end of file
diff --git a/scripts/train_cnb.py b/scripts/train_cnb.py
new file mode 100644
index 0000000..5f13467
--- /dev/null
+++ b/scripts/train_cnb.py
@@ -0,0 +1,180 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+小狸AI - CNB GPU训练脚本
+从 merged_clean_v3.json (977K对) 训练，自动GPU检测
+"""
+import sys, os, json, time, glob, re, random, argparse
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', buffering=1)
+
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+
+from llm_chat_improved import LLMModel, LLMChat, ConversationDataset, LLMTrainer, tokenizer_instance
+
+MODEL_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+def load_pairs(path):
+    with open(path, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+    pairs = []
+    for item in data:
+        if isinstance(item, dict):
+            q = item.get("question", "") or item.get("input", "")
+            a = item.get("answer", "") or item.get("output", "")
+            if q and a:
+                pairs.append((q, a))
+    return pairs
+
+def find_latest_ckpt(model_dir):
+    ckpts = glob.glob(os.path.join(model_dir, 'llm_model_v2_resume_epoch_*.pth'))
+    if not ckpts:
+        ckpts = glob.glob(os.path.join(model_dir, 'llm_model_v2_epoch_*.pth'))
+    if not ckpts:
+        return None, 0
+    nums = [(p, max([int(s) for s in re.findall(r'\d+', os.path.basename(p).replace('.pth',''))])) for p in ckpts]
+    best = max(nums, key=lambda x: x[1])
+    return best[0], best[1]
+
+def main():
+    parser = argparse.ArgumentParser(description='小狸AI CNB训练')
+    parser.add_argument('--epochs', type=int, default=100, help='训练轮数')
+    parser.add_argument('--batch_size', type=int, default=8, help='batch大小')
+    parser.add_argument('--lr', type=float, default=3e-4, help='学习率')
+    parser.add_argument('--accum', type=int, default=2, help='梯度累积步数')
+    parser.add_argument('--data', type=str, default='data/merged_clean_v3.json', help='训练数据路径')
+    parser.add_argument('--resume', action='store_true', help='断点续训')
+    parser.add_argument('--save_every', type=int, default=5, help='每N轮保存checkpoint')
+    args = parser.parse_args()
+
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    print("=" * 60)
+    print(f"小狸AI CNB训练")
+    print(f"  GPU: {torch.cuda.get_device_name(0) if device == 'cuda' else '无'}")
+    print(f"  设备: {device}")
+    print(f"  数据: {args.data}")
+    print(f"  epochs={args.epochs}, batch={args.batch_size}, lr={args.lr}")
+    print("=" * 60)
+
+    # ====== 加载数据 ======
+    data_path = args.data if os.path.isabs(args.data) else os.path.join(MODEL_DIR, args.data)
+    if not os.path.exists(data_path):
+        print(f"❌ 数据不存在: {data_path}")
+        return
+    
+    pairs = load_pairs(data_path)
+    random.seed(42)
+    random.shuffle(pairs)
+    split = int(len(pairs) * 0.95)
+    train_set = pairs[:split]
+    val_set = pairs[split:]
+    print(f"数据: {len(pairs)}对 | 训练: {len(train_set)} | 验证: {len(val_set)}")
+
+    # ====== 加载词表 ======
+    vocab_path = os.path.join(MODEL_DIR, 'vocab.json')
+    with open(vocab_path, 'r', encoding='utf-8') as f:
+        vocab = json.load(f)
+    print(f"词表: {len(vocab)} (BPE)")
+    tokenizer_instance.char_to_id = vocab.copy()
+    tokenizer_instance.id_to_char = {v: k for k, v in vocab.items()}
+    for attr in ['_multi_trie', '_multi_keys', '_single_map']:
+        if hasattr(tokenizer_instance, attr):
+            delattr(tokenizer_instance, attr)
+
+    # ====== 模型 ======
+    model = LLMModel(vocab_size=len(vocab), d_model=320, n_heads=8,
+                     n_layers=6, d_ff=1536, max_len=256, dropout=0.1,
+                     use_memory=False, use_thinking=False, use_moe=False)
+    total = sum(p.numel() for p in model.parameters())
+    print(f"模型: {total:,} 参数 ({total/1e6:.2f}M)")
+
+    start_epoch = 0
+    if args.resume:
+        ckpt_path, last_epoch = find_latest_ckpt(MODEL_DIR)
+        if ckpt_path:
+            ckpt = torch.load(ckpt_path, map_location=device)
+            old_cfg = ckpt.get('model_config', {})
+            if (old_cfg.get('vocab_size', 0) == len(vocab)
+                and old_cfg.get('d_model', 0) == 320
+                and old_cfg.get('n_layers', 0) == 6):
+                model.load_state_dict(ckpt['model_state_dict'])
+                start_epoch = last_epoch
+                print(f"断点续训: epoch {start_epoch} ← {os.path.basename(ckpt_path)}")
+            else:
+                print(f"⚠️ 架构不匹配，从头训练")
+        else:
+            print("无断点，从头训练")
+    
+    model = model.to(device)
+
+    # ====== DataLoader ======
+    train_ds = ConversationDataset(train_set, vocab, max_length=256)
+    train_loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=True)
+    val_ds = ConversationDataset(val_set, vocab, max_length=256)
+    val_loader = DataLoader(val_ds, batch_size=args.batch_size, shuffle=False)
+    print(f"批次: 训练{len(train_loader)} | 验证{len(val_loader)}")
+
+    # ====== Trainer ======
+    config = {
+        'batch_size': args.batch_size,
+        'gradient_accumulation_steps': args.accum,
+        'epochs': args.epochs,
+        'd_model': 320, 'n_heads': 8, 'n_layers': 6,
+        'd_ff': 1536, 'max_length': 256, 'dropout': 0.1,
+        'use_amp': device == 'cuda',
+    }
+    trainer = LLMTrainer(model, device, lr=args.lr, use_amp=config['use_amp'])
+
+    # ====== 训练 ======
+    print(f"\n开始训练 epoch {start_epoch+1} → {args.epochs}")
+    print("=" * 60)
+    t_start = time.time()
+    best_val = float('inf')
+
+    for epoch in range(start_epoch + 1, args.epochs + 1):
+        t0 = time.time()
+        train_loss = trainer.train_epoch(train_loader, epoch, vocab, config)
+        val_loss = trainer.evaluate(val_loader, config)
+        dt = time.time() - t0
+
+        marker = " ★" if val_loss < best_val else ""
+        if val_loss < best_val:
+            best_val = val_loss
+            trainer.save_model(os.path.join(MODEL_DIR, 'xiaoli_cnb_best.pth'), vocab, epoch)
+
+        elapsed = time.time() - t_start
+        eta = (elapsed / (epoch - start_epoch)) * (args.epochs - epoch) if epoch > start_epoch else 0
+        
+        print(f"E{epoch}/{args.epochs} | train={train_loss:.4f} val={val_loss:.4f}{marker} | "
+              f"{dt:.0f}s/epoch | {elapsed/60:.1f}min已用 ETA{eta/60:.0f}min")
+
+        # 保存
+        if epoch % args.save_every == 0:
+            ckpt_path = os.path.join(MODEL_DIR, f'llm_model_v2_resume_epoch_{epoch}.pth')
+            trainer.save_model(ckpt_path, vocab, epoch)
+            # 清理旧resume
+            for f in glob.glob(os.path.join(MODEL_DIR, 'llm_model_v2_resume_*.pth')):
+                en = max([int(s) for s in re.findall(r'\d+', os.path.basename(f).replace('.pth',''))])
+                if en <= epoch - 15:
+                    try: os.remove(f)
+                    except: pass
+            print(f"  💾 已保存 epoch {epoch}")
+
+    # ====== 完成 ======
+    final_path = os.path.join(MODEL_DIR, 'xiaoli_cnb_final.pth')
+    trainer.save_model(final_path, vocab, args.epochs)
+    total_time = (time.time() - t_start) / 60
+    print(f"\n✅ 训练完成! 耗时 {total_time:.1f}min")
+    
+    # 测试
+    print("\n测试:")
+    chat = LLMChat(model, vocab, device, max_length=256)
+    for q in ["你好", "你是谁", "你叫什么名字", "今天天气真好", "我有点难过"]:
+        r = chat.generate_response(q, max_length=60, temperature=0.3)
+        print(f"  Q: {q}")
+        print(f"  A: {r}\n")
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file