shuiyu1123
7f8f607f26
- 训练脚本: train_v2.py + llm_chat_improved.py - API生成: api_correction.py - 高温多样化: regenerate_all_high_temp.py - 每日数据生成: daily_data_gen.py - 数据: xiaoli_train_v2.json(40776条) + vocab_v2.json - 蒸馏问题: exam_distill_questions等 - Web服务: web_server.py + chat.html - 新增 .gitignore 排除旧版文件
128 lines
4.2 KiB
Python
128 lines
4.2 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""小狸v1.2 Web聊天服务器 - 当前训练中的模型"""
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import sys, os, json, time
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sys.path.insert(0, '/workspace/xiaoli-llm-chat')
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import torch
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from flask import Flask, request, jsonify, send_from_directory
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from llm_chat_improved import LLMModel, LLMChat, build_vocabulary
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print("=" * 50)
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print(" 小狸 v1.2 Web 服务加载中...")
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print("=" * 50)
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device = 'cpu'
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model_dir = '/workspace/xiaoli-llm-chat'
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# 构建当前vocab
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with open(f'{model_dir}/xiaoli_merged_data_v2.json', 'r', encoding='utf-8') as f:
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all_data = json.load(f)
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pairs = []
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for d in all_data:
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q = d.get('question','') or d.get('input','') or d.get('prompt','') or ''
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a = d.get('answer','') or d.get('output','') or d.get('response','') or ''
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if q and a:
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pairs.append((q, a))
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vocab = build_vocabulary(pairs)
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print(f"词汇表: {len(vocab)} | 数据: {len(all_data)}条")
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# 加载模型 (epoch 50 = 干净epoch 41 + vocab扩展)
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ckpt_path = f'{model_dir}/llm_model_v2_resume_epoch_50.pth'
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print(f"加载: {ckpt_path}")
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ckpt = torch.load(ckpt_path, map_location=device)
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old_vocab_size = ckpt.get('model_config', {}).get('vocab_size', 0)
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cfg = ckpt['model_config']
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print(f"断点vocab: {old_vocab_size}")
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model = LLMModel(
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vocab_size=len(vocab),
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d_model=cfg['d_model'], n_heads=cfg['n_heads'],
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n_layers=cfg['n_layers'], d_ff=cfg['d_ff'],
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max_len=cfg['max_len'], dropout=cfg.get('dropout', 0.1),
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use_memory=False, use_thinking=False, use_moe=False,
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num_experts=1, top_k=1,
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)
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# 迁移权重
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old_sd = ckpt['model_state_dict']
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new_sd = model.state_dict()
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if old_vocab_size != len(vocab):
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for key in new_sd:
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if key in old_sd:
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w = old_sd[key]
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if w.shape == new_sd[key].shape:
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new_sd[key] = w.clone()
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elif len(w.shape) == 2 and w.shape[0] == old_vocab_size:
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new_sd[key][:old_vocab_size] = w
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elif 'freq' in key:
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pass
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else:
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try: new_sd[key] = w.clone()
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except: pass
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model.load_state_dict(new_sd)
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else:
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model.load_state_dict(old_sd)
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model.to(device)
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model.eval()
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chat = LLMChat(model, vocab, device, max_length=cfg['max_len'])
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print(f"✅ 模型加载完成！ | voc={len(vocab)} | max_len={cfg['max_len']}")
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app = Flask(__name__)
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@app.route('/')
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def index():
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return send_from_directory(model_dir, 'chat.html')
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@app.route('/api/chat', methods=['POST'])
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def api_chat():
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data = request.get_json()
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question = data.get('question', '').strip()
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temp = float(data.get('temperature', 0.2))
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max_len = int(data.get('max_length', 100))
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history = data.get('history', None)
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if not question:
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return jsonify({'error': '问题不能为空'})
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# 如果有history，转换为对话格式
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history_pairs = None
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if history and isinstance(history, list) and len(history) > 0:
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history_pairs = [(h['q'], h['a']) for h in history]
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try:
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t0 = time.time()
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answer = chat.generate_response(question, max_length=max_len, temperature=temp, history=history_pairs)
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elapsed = time.time() - t0
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return jsonify({
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'question': question,
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'answer': answer,
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'time': f'{elapsed:.1f}s',
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'temperature': temp,
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})
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except Exception as e:
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return jsonify({'error': str(e)})
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@app.route('/api/info')
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def model_info():
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return jsonify({
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'name': '小狸 v1.2',
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'version': 'epoch 52 (架构扩展: 7.9M→21.3M, max_len=256)',
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'vocab_size': len(vocab),
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'max_length': cfg['max_len'],
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'd_model': cfg['d_model'],
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'layers': cfg['n_layers'],
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'heads': cfg['n_heads'],
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'd_ff': cfg['d_ff'],
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'data_size': f'{len(all_data):,}条',
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'training': '新架构训练中 epoch 1→200',
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'features': ['多轮对话(1,694条)', '长文本(758条)', '我不知道(1,140条)'],
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})
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if __name__ == '__main__':
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print("\n" + "=" * 50)
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print(" 小狸 v1.2 聊天服务已启动！")
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print(" 访问地址: http://0.0.0.0:5000")
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print(" 建议温度: 0.1 ~ 0.2")
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print("=" * 50)
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app.run(host='0.0.0.0', port=5000, debug=False, threaded=True) |