同步当前架构和数据 (2026-06-11)
- 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
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@@ -0,0 +1,76 @@
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# 缓存和日志
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__pycache__/
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*.pyc
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*.log
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*.pth
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*.pth.*
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/tmp/
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# 中间数据（旧版临时文件）
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mega_interim_*.json
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essay_interim_*.json
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checkpoints_v2/
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high_temp_outputs/
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old_ckpts/
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backup_old_arch/
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# 旧版数据（不再使用）
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xiaoli_mega_*.json
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xiaoli_multiturn_memory*.json
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xiaoli_offline_distill*.json
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xiaoli_personality*.json
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xiaoli_safety*.json
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xiaoli_sassy*.json
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xiaoli_short_cot*.json
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xiaoli_cot*.json
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xiaoli_distill*.json
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xiaoli_diverse*.json
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xiaoli_drowning*.json
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xiaoli_english*.json
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xiaoli_final*.json
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xiaoli_girl*.json
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xiaoli_knowledge*.json
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xiaoli_math*.json
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xiaoli_merged_data.json
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xiaoli_body*.json
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xiaoli_child*.json
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xiaoli_cute*.json
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xiaoli_complete*.json
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xiaoli_data_optimized*.json
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xiaoli_riddle*.json
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xiaoli_final*.json
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# 旧版脚本（不再使用）
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generate_math*.py
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generate_mimo*.py
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generate_m3ke*.py
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generate_mega*.py
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generate_memory*.py
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generate_essays*.py
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generate_cot*.py
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generate_more_data*.py
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generate_offline*.py
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gen_*.py
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distill_*.py
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analyze_*.py
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merge_and_retrain*.sh
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run_all_batches*.sh
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watchdog*.py
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test_epoch*.py
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test_ood*.py
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# 旧版配置文件
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cot_*.json
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memory_long*.json
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multiturn_batch_*.json
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real_multiturn*.json
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mega_questions*.json
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math_questions*.json
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essay_questions*.json
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cot_candidates*.json
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model_test_results*.json
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online_distill_report*.json
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batch_runner*.log
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mega_*.log
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mimo_*.log
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knowledge_*.log
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@@ -0,0 +1,99 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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API生成模块 — Astron 5并发，稳定不超限
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"""
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import json, requests, time, os, threading
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from concurrent.futures import ThreadPoolExecutor, as_completed
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ASTRON_URL = "https://maas-coding-api.cn-huabei-1.xf-yun.com/v2/chat/completions"
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ASTRON_KEY = "658beba1beb0c911ed00672019bbf7e5:MjNmYWRkMWUyZjAxYTM3ZGE2ODI1YzFh"
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ASTRON_MODEL = "astron-code-latest"
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SYSTEM_PROMPT = """你是一个名叫小狸的8岁中国小女孩。
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你的人设：
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- 8岁女孩子，有点害羞但很可爱
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- 说话简单自然，符合小朋友的语气
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- 回答问题要简短，1-2句话就够了
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- 不要用"喵"结尾，不要过度卖萌
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回答要求：
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1. 语气自然，像真实的小朋友说话
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2. 不懂就说"不知道"
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3. 回答简洁但要说清楚，不要讲一半就停"""
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def call_astron(q, temperature=0.85):
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for attempt in range(3):
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try:
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headers = {"Content-Type": "application/json", "Authorization": f"Bearer {ASTRON_KEY}"}
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payload = {
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"model": ASTRON_MODEL,
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"messages": [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": q}
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],
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"temperature": temperature,
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"max_tokens": 8192,
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}
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resp = requests.post(ASTRON_URL, headers=headers, json=payload, timeout=60)
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if resp.status_code == 200:
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content = resp.json()['choices'][0]['message']['content'].strip()
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if content:
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return content
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else:
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time.sleep(1)
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except Exception:
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time.sleep(2)
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return None
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def batch_generate(questions, temperature=0.85, max_workers=4):
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"""Astron 4并发 — 明确限制，稳定可靠"""
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total = len(questions)
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results = []
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def call(q):
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ans = call_astron(q, temperature)
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if ans:
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return q, ans
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return None
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with ThreadPoolExecutor(max_workers=max_workers) as executor:
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futures = {executor.submit(call, item): i for i, item in enumerate(questions)}
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done = 0
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t0 = time.time()
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for f in as_completed(futures):
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done += 1
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r = f.result()
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if r:
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results.append({"question": r[0], "answer": r[1]})
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if done % 20 == 0 or done == total:
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elapsed = max(time.time() - t0, 1)
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rate = len(results) / elapsed * 60
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print(f" Astron: {done}/{total} (已获{len(results)}条, {rate:.0f}条/分)", end="\r")
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elapsed = time.time() - t0
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rate = len(results) / max(elapsed, 1) * 60
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print(f" Astron: {total}/{total} → 共{len(results)}条 ({elapsed:.0f}s, {rate:.0f}条/分)")
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return results
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# 保持旧函数名兼容
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batch_generate_astron = batch_generate
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batch_generate_mimo = batch_generate
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batch_generate_direct = batch_generate
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batch_generate_dual = batch_generate
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def batch_convert(items, *args, **kwargs):
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"""兼容占位：训练脚本导入用，已弃用"""
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print(" [API] batch_convert已弃用，使用batch_generate")
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return []
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def batch_generate_multi_temp(questions, *args, **kwargs):
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"""兼容占位：训练脚本导入用，已弃用"""
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print(" [API] batch_generate_multi_temp已弃用，使用batch_generate")
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return []
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@@ -0,0 +1,284 @@
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<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>小狸 v1.2 聊天</title>
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<style>
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* { margin: 0; padding: 0; box-sizing: border-box; }
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body {
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font-family: 'PingFang SC', 'Microsoft YaHei', sans-serif;
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background: linear-gradient(135deg, #ffecd2 0%, #fcb69f 100%);
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min-height: 100vh;
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display: flex;
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justify-content: center;
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align-items: center;
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padding: 20px;
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}
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.container {
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width: 100%;
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max-width: 560px;
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background: white;
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border-radius: 24px;
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box-shadow: 0 20px 60px rgba(0,0,0,0.15);
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overflow: hidden;
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}
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.header {
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background: linear-gradient(135deg, #ff9a9e 0%, #fecfef 100%);
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padding: 20px;
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text-align: center;
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position: relative;
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}
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.header .avatar {
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width: 64px; height: 64px;
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background: white;
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border-radius: 50%;
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margin: 0 auto 8px;
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display: flex;
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align-items: center;
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justify-content: center;
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font-size: 32px;
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box-shadow: 0 4px 15px rgba(0,0,0,0.1);
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}
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.header h1 { font-size: 24px; color: white; }
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.header p { color: rgba(255,255,255,0.9); font-size: 12px; margin-top: 2px; }
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.chat-box {
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height: 420px;
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overflow-y: auto;
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padding: 16px;
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background: #fafafa;
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}
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.chat-box::-webkit-scrollbar { width: 5px; }
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.chat-box::-webkit-scrollbar-thumb { background: #ddd; border-radius: 10px; }
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.msg { margin-bottom: 14px; display: flex; animation: fadeIn 0.3s ease; }
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@keyframes fadeIn { from { opacity: 0; transform: translateY(8px); } to { opacity: 1; transform: translateY(0); } }
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.msg.user { justify-content: flex-end; }
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.msg.bot { justify-content: flex-start; }
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.bubble {
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max-width: 80%;
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padding: 10px 14px;
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border-radius: 16px;
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font-size: 14px;
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line-height: 1.6;
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word-break: break-word;
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}
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.msg.user .bubble {
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background: #ff9a9e;
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color: white;
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border-bottom-right-radius: 4px;
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}
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.msg.bot .bubble {
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background: white;
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color: #333;
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border-bottom-left-radius: 4px;
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box-shadow: 0 2px 8px rgba(0,0,0,0.06);
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}
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.msg.bot .avatar-small {
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width: 30px; height: 30px;
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background: linear-gradient(135deg, #ff9a9e, #fecfef);
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border-radius: 50%;
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display: flex;
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align-items: center;
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justify-content: center;
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font-size: 15px;
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margin-right: 8px;
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flex-shrink: 0;
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color: white;
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}
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.time-tag { font-size: 11px; color: #bbb; margin-top: 3px; }
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.input-area {
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padding: 12px 16px;
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background: white;
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border-top: 1px solid #f0f0f0;
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display: flex;
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gap: 8px;
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}
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.input-area input {
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flex: 1;
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padding: 10px 14px;
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border: 2px solid #f0f0f0;
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border-radius: 25px;
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font-size: 14px;
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outline: none;
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}
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.input-area input:focus { border-color: #ff9a9e; }
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.input-area button {
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width: 42px; height: 42px;
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background: linear-gradient(135deg, #ff9a9e, #fecfef);
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border: none;
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border-radius: 50%;
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color: white;
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font-size: 18px;
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cursor: pointer;
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flex-shrink: 0;
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}
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.input-area button:hover { transform: scale(1.08); }
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.input-area button:disabled { opacity: 0.5; cursor: not-allowed; }
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.typing-indicator { display: none; padding: 10px 14px; background: white; border-radius: 16px; box-shadow: 0 2px 8px rgba(0,0,0,0.06); }
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.typing-indicator.active { display: flex; }
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.typing-indicator span {
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width: 7px; height: 7px; background: #ff9a9e; border-radius: 50%;
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margin: 0 2px; animation: bounce 1.4s infinite;
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}
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.typing-indicator span:nth-child(2) { animation-delay: 0.2s; }
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.typing-indicator span:nth-child(3) { animation-delay: 0.4s; }
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@keyframes bounce { 0%,80%,100% { transform: translateY(0); } 40% { transform: translateY(-8px); } }
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.controls {
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display: flex;
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align-items: center;
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gap: 12px;
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padding: 6px 16px;
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background: #fff8f8;
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border-top: 1px solid #f0f0f0;
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}
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.controls label { font-size: 12px; color: #999; }
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.controls input[type=range] { flex: 1; accent-color: #ff9a9e; height: 4px; }
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.controls .val { font-size: 12px; color: #e07070; font-weight: bold; min-width: 30px; }
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.controls .clear-btn {
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font-size: 11px; color: #ccc; cursor: pointer; padding: 3px 8px; border-radius: 10px; border: 1px solid #eee;
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}
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.controls .clear-btn:hover { color: #e07070; border-color: #ffd4d4; }
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.status-bar { padding: 4px 16px; font-size: 11px; color: #bbb; text-align: center; background: #fafafa; }
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</style>
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</head>
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<body>
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<div class="container">
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<div class="header">
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<div class="avatar">🦊</div>
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<h1>小狸 v1.2</h1>
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<p id="headerInfo">V2架构 · 21M参数 · 新训练启动 🔥</p>
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</div>
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<div class="chat-box" id="chatBox"></div>
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<!-- 温度控制 -->
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<div class="controls">
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<label>🌡️</label>
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<input type="range" id="tempSlider" min="0.05" max="0.5" step="0.05" value="0.15">
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<span class="val" id="tempVal">0.15</span>
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<span class="clear-btn" onclick="clearChat()">🗑️ 清空</span>
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</div>
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<div class="input-area">
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<input type="text" id="input" placeholder="跟小狸说点什么吧..." autofocus>
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<button id="sendBtn" onclick="send()">➤</button>
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</div>
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<div class="status-bar" id="statusBar">按回车发送 · 建议温度 0.1~0.2</div>
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</div>
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<script>
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const chatBox = document.getElementById('chatBox');
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const input = document.getElementById('input');
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const sendBtn = document.getElementById('sendBtn');
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const statusBar = document.getElementById('statusBar');
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const tempSlider = document.getElementById('tempSlider');
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const tempVal = document.getElementById('tempVal');
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// 对话历史（用于多轮）
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let history = [];
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tempSlider.addEventListener('input', () => {
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tempVal.textContent = parseFloat(tempSlider.value).toFixed(2);
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});
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addMsg('bot', '你好呀！我是小狸，一个8岁的小女孩～🦊\n你可以连续跟我聊天，我会记住之前说过的话哦！');
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function addMsg(role, text) {
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const div = document.createElement('div');
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div.className = 'msg ' + role;
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||||
if (role === 'bot') {
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const av = document.createElement('div');
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av.className = 'avatar-small';
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av.textContent = '🦊';
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div.appendChild(av);
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}
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const bubble = document.createElement('div');
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bubble.className = 'bubble';
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||||
bubble.innerHTML = text.replace(/\n/g, '<br>');
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div.appendChild(bubble);
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const time = document.createElement('div');
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||||
time.className = 'time-tag';
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||||
time.textContent = new Date().toLocaleTimeString('zh-CN', {hour:'2-digit',minute:'2-digit'});
|
||||
if (role === 'user') {
|
||||
bubble.appendChild(document.createElement('br'));
|
||||
bubble.appendChild(time);
|
||||
} else {
|
||||
bubble.appendChild(document.createElement('br'));
|
||||
bubble.appendChild(time);
|
||||
}
|
||||
chatBox.appendChild(div);
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||||
chatBox.scrollTop = chatBox.scrollHeight;
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||||
}
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||||
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||||
function showTyping() {
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||||
const div = document.createElement('div');
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||||
div.className = 'msg bot';
|
||||
div.id = 'typing';
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||||
const av = document.createElement('div');
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||||
av.className = 'avatar-small';
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||||
av.textContent = '🦊';
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div.appendChild(av);
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const indicator = document.createElement('div');
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||||
indicator.className = 'typing-indicator active';
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for (let i = 0; i < 3; i++) indicator.innerHTML += '<span>';
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div.appendChild(indicator);
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||||
chatBox.appendChild(div);
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chatBox.scrollTop = chatBox.scrollHeight;
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||||
}
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||||
|
||||
function hideTyping() {
|
||||
const t = document.getElementById('typing');
|
||||
if (t) t.remove();
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||||
}
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||||
|
||||
async function send() {
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||||
const q = input.value.trim();
|
||||
if (!q) return;
|
||||
input.value = '';
|
||||
sendBtn.disabled = true;
|
||||
statusBar.textContent = '小狸正在思考...';
|
||||
|
||||
addMsg('user', q);
|
||||
showTyping();
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||||
|
||||
try {
|
||||
const res = await fetch('/api/chat', {
|
||||
method: 'POST',
|
||||
headers: {'Content-Type': 'application/json'},
|
||||
body: JSON.stringify({
|
||||
question: q,
|
||||
temperature: parseFloat(tempSlider.value),
|
||||
history: history.length > 0 ? history : null
|
||||
})
|
||||
});
|
||||
const data = await res.json();
|
||||
hideTyping();
|
||||
if (data.answer) {
|
||||
// 记录到历史
|
||||
history.push({q: q, a: data.answer});
|
||||
addMsg('bot', data.answer + ' <span style="color:#ccc;font-size:11px">' + data.time + '</span>');
|
||||
} else {
|
||||
addMsg('bot', '唔…我没想好怎么回答😅');
|
||||
}
|
||||
} catch (e) {
|
||||
hideTyping();
|
||||
addMsg('bot', '哎呀，出错了…再试试？');
|
||||
}
|
||||
sendBtn.disabled = false;
|
||||
statusBar.textContent = '按回车发送 · 温度 ' + parseFloat(tempSlider.value).toFixed(2);
|
||||
input.focus();
|
||||
}
|
||||
|
||||
function clearChat() {
|
||||
chatBox.innerHTML = '';
|
||||
history = [];
|
||||
addMsg('bot', '🔄 对话已重置！重新开始吧～');
|
||||
}
|
||||
|
||||
input.addEventListener('keydown', e => {
|
||||
if (e.key === 'Enter') send();
|
||||
});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
@@ -0,0 +1,184 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
每日数据生成 — 每天12:00跑
|
||||
用 Astron 生成大量小狸风格问答数据，追加到训练集（不触发训练）
|
||||
"""
|
||||
import sys, os, json, time, random
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', buffering=1)
|
||||
|
||||
from api_correction import batch_generate
|
||||
|
||||
MODEL_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
MAX_WORKERS = 5 # 5并发
|
||||
BATCH_SIZE = 500
|
||||
|
||||
# ===== 种子话题（每天随机选一批让小狸聊） =====
|
||||
SEED_TOPICS = [
|
||||
"幼儿园", "学校", "朋友", "玩具", "动画片", "宠物", "零食", "天气",
|
||||
"节日", "生日", "爸爸妈妈", "老师", "作业", "画画", "唱歌", "跳舞",
|
||||
"游乐园", "动物园", "海洋馆", "公园", "滑滑梯", "秋千", "积木", "拼图",
|
||||
"自行车", "游泳", "跳绳", "踢球", "冰淇淋", "蛋糕", "水果", "蔬菜",
|
||||
"彩虹", "星星", "月亮", "太阳", "花", "草", "树", "小动物",
|
||||
"蝴蝶", "小鸟", "小鱼", "乌龟", "兔子", "猫", "狗", "恐龙",
|
||||
"过年", "压岁钱", "红包", "新衣服", "放鞭炮", "灯笼", "元宵",
|
||||
"圣诞老人", "礼物", "糖果", "巧克力", "饼干", "牛奶", "早餐",
|
||||
"为什么天空是蓝的", "为什么树叶会掉", "为什么有影子", "为什么会下雨",
|
||||
"为什么会有白天黑夜", "为什么海水是咸的", "为什么会打雷",
|
||||
"1+1等于几", "数数", "认字", "背诗", "唱歌", "讲故事",
|
||||
"礼貌", "分享", "排队", "帮助别人", "说谢谢", "对不起",
|
||||
"害怕", "开心", "难过", "生气", "勇敢", "害羞",
|
||||
]
|
||||
|
||||
def generate_questions_from_topics(topics, count=500):
|
||||
"""用话题种子生成自然提问"""
|
||||
questions = []
|
||||
for topic in topics[:count]:
|
||||
patterns = [
|
||||
f"小狸，你喜欢{topic}吗？",
|
||||
f"给我讲讲{topic}吧",
|
||||
f"{topic}是什么呀？",
|
||||
f"你最喜欢什么样的{topic}？",
|
||||
f"关于{topic}你知道什么？",
|
||||
f"小狸，{topic}好玩吗？",
|
||||
f"能不能教我关于{topic}的事情？",
|
||||
f"你上次玩{topic}是什么时候？",
|
||||
f"大家为什么都喜欢{topic}呀？",
|
||||
f"小狸，你在{topic}上有什么有趣的事吗？",
|
||||
]
|
||||
q = random.choice(patterns)
|
||||
questions.append(q)
|
||||
return questions
|
||||
|
||||
def generate_natural_questions(count=200):
|
||||
"""生成自然闲聊问题（固定一批高质量问题）"""
|
||||
base_questions = [
|
||||
"小狸你好呀", "今天开心吗", "你几岁了", "你叫什么名字",
|
||||
"你吃饭了吗", "我们一起玩吧", "你在干嘛呢", "你今天做了什么",
|
||||
"你喜欢什么颜色", "你最好的朋友是谁", "你最喜欢什么动物",
|
||||
"你最喜欢吃什么", "你最喜欢什么水果", "你最爱看什么动画片",
|
||||
"可以给我讲个故事吗", "唱首歌给我听", "教我背一首诗吧",
|
||||
"你会画画吗", "我们来玩猜谜语", "你家里有谁呀",
|
||||
"你的房间是什么样子的", "你书包里有什么", "你最喜欢上什么课",
|
||||
"今天天气真好", "外面下雨了", "好冷啊", "好热啊",
|
||||
"我有点不开心", "我今天好开心", "我害怕打雷",
|
||||
"你睡午觉了吗", "晚安", "早安", "快来",
|
||||
"我想吃冰淇淋", "我想去游乐场", "我想养一只小猫",
|
||||
"为什么雪是白色的", "为什么天是蓝的", "为什么花是香的",
|
||||
"月亮上有人吗", "星星为什么会眨眼睛", "大海有多大",
|
||||
"你会数数吗", "1+1等于几", "100+200等于多少",
|
||||
"我喜欢你小狸", "你好可爱", "我们做朋友吧",
|
||||
"你今天真好看", "谢谢你", "你真好",
|
||||
"对不起", "我不小心摔倒了", "我被人欺负了",
|
||||
"怎么办呀", "帮帮我", "我该怎么做",
|
||||
"什么是爱", "什么是勇敢", "什么是善良",
|
||||
"长大是什么感觉", "为什么要上学", "为什么要听话",
|
||||
"你会一直陪着我吗", "你想去什么地方玩",
|
||||
]
|
||||
return random.sample(base_questions, min(count, len(base_questions)))
|
||||
|
||||
def main():
|
||||
print("=" * 60)
|
||||
print("📅 每日数据生成")
|
||||
print(f"时间: {time.strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
print("=" * 60)
|
||||
|
||||
# 1. 混合生成问题
|
||||
random.seed(int(time.time()))
|
||||
n_topic_qs = random.randint(300, 500) # 话题衍生问题
|
||||
n_natural_qs = random.randint(200, 300) # 自然闲聊问题
|
||||
|
||||
random.shuffle(SEED_TOPICS)
|
||||
topic_questions = generate_questions_from_topics(SEED_TOPICS, n_topic_qs)
|
||||
natural_questions = generate_natural_questions(n_natural_qs)
|
||||
|
||||
all_questions = topic_questions + natural_questions
|
||||
random.shuffle(all_questions)
|
||||
|
||||
print(f"\n准备生成 {len(all_questions)} 条问答 (话题衍生: {len(topic_questions)}, 自然: {len(natural_questions)})")
|
||||
|
||||
# 2. 用不同温度分批生成，增加多样性
|
||||
TEMPS = [0.85, 0.9, 0.95, 1.0]
|
||||
all_results = []
|
||||
existing = set()
|
||||
|
||||
# 加载已有数据用于去重
|
||||
train_path = os.path.join(MODEL_DIR, 'xiaoli_train_v2.json')
|
||||
with open(train_path, encoding='utf-8') as f:
|
||||
cur_train = json.load(f)
|
||||
for it in cur_train:
|
||||
key = (it.get('question','')[:100], it.get('answer','')[:100])
|
||||
existing.add(key)
|
||||
print(f"已有训练集: {len(cur_train)} 条")
|
||||
|
||||
# 分批+变温度
|
||||
total_batches = (len(all_questions) + BATCH_SIZE - 1) // BATCH_SIZE
|
||||
t_start = time.time()
|
||||
|
||||
for batch_idx in range(total_batches):
|
||||
batch_qs = all_questions[batch_idx * BATCH_SIZE : (batch_idx + 1) * BATCH_SIZE]
|
||||
temp = random.choice(TEMPS)
|
||||
|
||||
print(f"\n--- 批次 {batch_idx + 1}/{total_batches} ({len(batch_qs)}条, temp={temp}) ---")
|
||||
|
||||
t0 = time.time()
|
||||
results = batch_generate(batch_qs, temperature=temp, max_workers=MAX_WORKERS)
|
||||
elapsed = time.time() - t0
|
||||
|
||||
new_in_batch = 0
|
||||
for r in results:
|
||||
key = (r['question'][:100], r['answer'][:100])
|
||||
if key not in existing:
|
||||
existing.add(key)
|
||||
all_results.append(r)
|
||||
new_in_batch += 1
|
||||
|
||||
total_elapsed = time.time() - t_start
|
||||
rate = len(all_results) / max(total_elapsed, 1) * 60
|
||||
print(f" 本批新增 {new_in_batch} 条 | 累计 {len(all_results)} 条 | {rate:.0f}条/分")
|
||||
|
||||
if not all_results:
|
||||
print("\n⚠️ 没有生成新数据")
|
||||
return
|
||||
|
||||
# 3. 追加到训练集
|
||||
print(f"\n追加 {len(all_results)} 条到训练集...")
|
||||
for tgt in ['xiaoli_train_v2.json', 'xiaoli_merged_data_v2.json']:
|
||||
tp = os.path.join(MODEL_DIR, tgt)
|
||||
with open(tp, encoding='utf-8') as f:
|
||||
cur = json.load(f)
|
||||
|
||||
ex = set((it.get('question','')[:100], it.get('answer','')[:100]) for it in cur)
|
||||
to_add = []
|
||||
for r in all_results:
|
||||
key = (r['question'][:100], r['answer'][:100])
|
||||
if key not in ex:
|
||||
ex.add(key)
|
||||
to_add.append({'question': r['question'], 'answer': r['answer']})
|
||||
|
||||
cur.extend(to_add)
|
||||
with open(tp, 'w', encoding='utf-8') as f:
|
||||
json.dump(cur, f, ensure_ascii=False, indent=2)
|
||||
|
||||
if 'train' in tgt:
|
||||
print(f" ✅ 训练集 +{len(to_add)} 条 → {len(cur)} 条")
|
||||
else:
|
||||
print(f" ✅ 合并集 +{len(to_add)} 条 → {len(cur)} 条")
|
||||
|
||||
# 4. 保存今日生成日志
|
||||
today = time.strftime('%Y-%m-%d')
|
||||
log_path = os.path.join(MODEL_DIR, f'daily_gen_{today}.json')
|
||||
with open(log_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(all_results, f, ensure_ascii=False, indent=2)
|
||||
|
||||
elapsed_total = time.time() - t_start
|
||||
print(f"\n{'='*60}")
|
||||
print(f"✅ 今日生成完成! {len(all_results)} 条新数据")
|
||||
print(f"用时: {elapsed_total/60:.1f} 分钟")
|
||||
print(f"日志: daily_gen_{today}.json")
|
||||
print(f"{'='*60}")
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:e5154511dddc7a03fcad94b7273bf7ab23ff9ccfe16957058b6a18eed608bf3b
|
||||
size 4919
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c74ebfca559a906d6cbd2d77eae0752ab72add28fca9b8de47d165fadd00e419
|
||||
size 30616
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:59f8dabcf5cbfc960810e99e51d8777a238e2ad4798405e8dd1c7bb7e5050a93
|
||||
size 2686585
|
||||
@@ -0,0 +1,57 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
单批生成 — 从mega_questions.json取指定batch生成并追加
|
||||
用法: python3 generate_batch.py <batch_num> <batch_size>
|
||||
"""
|
||||
import sys, os, json, time
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', buffering=1)
|
||||
|
||||
from api_correction import batch_generate
|
||||
|
||||
model_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
batch_num = int(sys.argv[1])
|
||||
batch_size = int(sys.argv[2])
|
||||
|
||||
# 加载问题列表
|
||||
with open(os.path.join(model_dir, 'mega_questions.json'), 'r', encoding='utf-8') as f:
|
||||
mega = json.load(f)
|
||||
|
||||
# 计算本批范围
|
||||
start = (batch_num - 1) * batch_size
|
||||
end = min(start + batch_size, len(mega))
|
||||
chunk = [item['question'] for item in mega[start:end]]
|
||||
|
||||
if not chunk:
|
||||
print(f"批{batch_num}: 无数据")
|
||||
sys.exit(0)
|
||||
|
||||
print(f"批 {batch_num}: {len(chunk)}题 (序号{start}-{end})")
|
||||
t0 = time.time()
|
||||
|
||||
results = batch_generate(chunk, temperature=0.85)
|
||||
dt = time.time() - t0
|
||||
|
||||
# 保存中间文件
|
||||
interim_path = os.path.join(model_dir, f'mega_interim_{batch_num}.json')
|
||||
with open(interim_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(results, f, ensure_ascii=False, indent=2)
|
||||
|
||||
# 追加到训练集
|
||||
for tgt in ['xiaoli_train_v2.json', 'xiaoli_merged_data_v2.json']:
|
||||
tgt_path = os.path.join(model_dir, tgt)
|
||||
if not os.path.exists(tgt_path):
|
||||
continue
|
||||
with open(tgt_path, 'r', encoding='utf-8') as f:
|
||||
cur = json.load(f)
|
||||
existing = set((item.get("question",""), item.get("answer","")) for item in cur)
|
||||
truly_new = [item for item in results if (item["question"], item["answer"]) not in existing]
|
||||
if truly_new:
|
||||
cur.extend(truly_new)
|
||||
with open(tgt_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(cur, f, ensure_ascii=False, indent=2)
|
||||
if tgt == 'xiaoli_train_v2.json':
|
||||
print(f" ✅ 追加训练集: +{len(truly_new)}条, 现有{len(cur)}条")
|
||||
|
||||
print(f"批{batch_num}完成: +{len(results)}条 ({dt:.0f}s)")
|
||||
@@ -0,0 +1,73 @@
|
||||
#!/usr/bin/env python3
|
||||
"""高温度多样化生成 — 每个问题跑 N 次，获得多种回答"""
|
||||
import sys, os, json, random, time
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', buffering=1)
|
||||
from api_correction import batch_generate_astron as batch_generate
|
||||
|
||||
MODEL_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
|
||||
# 载入全部问题
|
||||
with open(os.path.join(MODEL_DIR, 'mega_questions.json')) as f:
|
||||
mega = json.load(f)
|
||||
total_q = len(mega)
|
||||
print(f"mega_questions 共 {total_q} 条")
|
||||
|
||||
# 温度配置：逐渐升高，产出多样化
|
||||
TEMP_CONFIG = [0.9, 1.1, 1.3, 1.5] # 跑4轮
|
||||
all_new_results = []
|
||||
|
||||
for round_idx, temp in enumerate(TEMP_CONFIG):
|
||||
print(f"\n{'='*60}")
|
||||
print(f"第{round_idx+1}轮: 温度={temp}")
|
||||
print(f"{'='*60}")
|
||||
|
||||
questions = [item['question'] for item in mega]
|
||||
|
||||
t0 = time.time()
|
||||
results = batch_generate(questions, temperature=temp)
|
||||
elapsed = time.time() - t0
|
||||
|
||||
# 标注来源
|
||||
for r in results:
|
||||
r['temperature'] = temp
|
||||
r['round'] = round_idx + 1
|
||||
|
||||
all_new_results.extend(results)
|
||||
print(f"轮{round_idx+1} 完成: {len(results)}条 ({elapsed:.0f}s)")
|
||||
print(f"累计: {len(all_new_results)}条")
|
||||
|
||||
# 去重（问题+回答完全重复才去）
|
||||
print(f"\n总体去重中...")
|
||||
seen = set()
|
||||
unique_results = []
|
||||
for r in all_new_results:
|
||||
key = (r['question'][:100], r['answer'][:100])
|
||||
if key not in seen:
|
||||
seen.add(key)
|
||||
unique_results.append(r)
|
||||
|
||||
print(f"去重前: {len(all_new_results)}条 → 去重后: {len(unique_results)}条")
|
||||
|
||||
# 追加到训练集
|
||||
for tgt in ['xiaoli_train_v2.json', 'xiaoli_merged_data_v2.json']:
|
||||
tp = os.path.join(MODEL_DIR, tgt)
|
||||
with open(tp, encoding='utf-8') as f:
|
||||
cur = json.load(f)
|
||||
|
||||
ex = set((it.get("question","")[:100], it.get("answer","")[:100]) for it in cur)
|
||||
to_add = []
|
||||
for r in unique_results:
|
||||
key = (r['question'][:100], r['answer'][:100])
|
||||
if key not in ex:
|
||||
ex.add(key)
|
||||
to_add.append({"question": r['question'], "answer": r['answer']})
|
||||
|
||||
cur.extend(to_add)
|
||||
with open(tp, 'w', encoding='utf-8') as f:
|
||||
json.dump(cur, f, ensure_ascii=False, indent=2)
|
||||
if 'train' in tgt:
|
||||
print(f"✅ 训练集 +{len(to_add)}条 → {len(cur)}条")
|
||||
|
||||
print(f"\n🎉 全部完成!")
|
||||
print(f"总数: {len(cur)}条")
|
||||
+82
-17
@@ -35,6 +35,7 @@ class CharTokenizer:
|
||||
self.char_to_id = self.special_tokens.copy()
|
||||
self.id_to_char = {v: k for k, v in self.char_to_id.items()}
|
||||
self.next_id = len(self.special_tokens)
|
||||
self._sorted_vocab_cache = None # encode缓存用
|
||||
|
||||
def build_vocab(self, texts: List[str], min_freq: int = 1) -> Dict[str, int]:
|
||||
"""从文本构建词汇表"""
|
||||
@@ -53,6 +54,13 @@ class CharTokenizer:
|
||||
self.id_to_char[self.next_id] = char
|
||||
self.next_id += 1
|
||||
|
||||
# 清除encode缓存（vocab已变化）
|
||||
self._sorted_vocab_cache = None
|
||||
# 清除多字符token缓存
|
||||
for attr in ['_multi_trie', '_multi_keys', '_single_map']:
|
||||
if hasattr(self, attr):
|
||||
delattr(self, attr)
|
||||
|
||||
return self.char_to_id
|
||||
|
||||
def _split_chars(self, text: str) -> List[str]:
|
||||
@@ -71,18 +79,38 @@ class CharTokenizer:
|
||||
return result
|
||||
|
||||
def encode(self, text: str) -> List[int]:
|
||||
"""将文本编码为ID序列"""
|
||||
"""将文本编码为ID序列 - 高效版"""
|
||||
if not text:
|
||||
return [self.special_tokens['<UNK>']]
|
||||
|
||||
chars = self._split_chars(text)
|
||||
ids = []
|
||||
for char in chars:
|
||||
if char in self.char_to_id:
|
||||
ids.append(self.char_to_id[char])
|
||||
else:
|
||||
# 未知字符用 UNK token
|
||||
ids.append(self.special_tokens['<UNK>'])
|
||||
i = 0
|
||||
n = len(text)
|
||||
|
||||
# 构建多字符token的前缀映射（仅一次）
|
||||
if not hasattr(self, '_multi_trie'):
|
||||
self._multi_trie = {}
|
||||
for token in self.char_to_id:
|
||||
if len(token) > 1:
|
||||
self._multi_trie[token] = self.char_to_id[token]
|
||||
# 按长度降序排序，优先匹配长token
|
||||
self._multi_keys = sorted(self._multi_trie.keys(), key=len, reverse=True)
|
||||
self._single_map = {c: tid for c, tid in self.char_to_id.items() if len(c) == 1}
|
||||
|
||||
while i < n:
|
||||
matched = False
|
||||
# 优先匹配多字符token（特殊标记）
|
||||
for token in self._multi_keys:
|
||||
if text.startswith(token, i):
|
||||
ids.append(self._multi_trie[token])
|
||||
i += len(token)
|
||||
matched = True
|
||||
break
|
||||
if not matched:
|
||||
# 单字符直接用dict查
|
||||
c = text[i]
|
||||
ids.append(self._single_map.get(c, self.special_tokens['<UNK>']))
|
||||
i += 1
|
||||
|
||||
return ids
|
||||
|
||||
@@ -92,8 +120,8 @@ class CharTokenizer:
|
||||
for tid in ids:
|
||||
if tid in self.id_to_char:
|
||||
char = self.id_to_char[tid]
|
||||
if char not in self.special_tokens:
|
||||
chars.append(char)
|
||||
# 保留特殊token以便后续处理，但这里先不过滤
|
||||
chars.append(char)
|
||||
return ''.join(chars)
|
||||
|
||||
|
||||
@@ -512,7 +540,7 @@ class LLMModel(nn.Module):
|
||||
class ConversationDataset(Dataset):
|
||||
"""对话数据集"""
|
||||
|
||||
def __init__(self, conversations: List[Tuple[str, str]], vocab: dict, max_length: int = 128):
|
||||
def __init__(self, conversations: List[Tuple[str, str]], vocab: dict, max_length: int = 256):
|
||||
self.conversations = conversations
|
||||
self.vocab = vocab
|
||||
self.max_length = max_length
|
||||
@@ -582,7 +610,7 @@ class LLMTrainer:
|
||||
self.model.train()
|
||||
total_loss = 0
|
||||
accumulation_steps = config.get('gradient_accumulation_steps', 1) if config else 1
|
||||
log_interval = max(1, len(dataloader) // 10) # 每10%打印一次
|
||||
log_interval = max(1, len(dataloader) // 100) # 每1%打印一次
|
||||
|
||||
for batch_idx, batch in enumerate(dataloader):
|
||||
input_ids = batch['input_ids'].to(self.device, non_blocking=True)
|
||||
@@ -637,6 +665,27 @@ class LLMTrainer:
|
||||
self.scheduler.step()
|
||||
return total_loss / len(dataloader)
|
||||
|
||||
def evaluate(self, dataloader: DataLoader, config: dict = None):
|
||||
"""在验证集上计算loss（无梯度，不更新权重）"""
|
||||
self.model.eval()
|
||||
total_loss = 0
|
||||
accumulation_steps = config.get('gradient_accumulation_steps', 1) if config else 1
|
||||
|
||||
with torch.no_grad():
|
||||
for batch_idx, batch in enumerate(dataloader):
|
||||
input_ids = batch['input_ids'].to(self.device, non_blocking=True)
|
||||
target_ids = batch['target_ids'].to(self.device, non_blocking=True)
|
||||
|
||||
mask = self.model.generate_mask(input_ids.size(1)).to(self.device)
|
||||
outputs = self.model(input_ids, mask)
|
||||
outputs = outputs.reshape(-1, outputs.size(-1))
|
||||
tgt = target_ids.reshape(-1)
|
||||
|
||||
loss = self.criterion(outputs, tgt)
|
||||
total_loss += loss.item()
|
||||
|
||||
return total_loss / len(dataloader)
|
||||
|
||||
def save_model(self, filepath: str, vocab: dict, epoch: int = None):
|
||||
save_dict = {
|
||||
'model_state_dict': self.model.state_dict(),
|
||||
@@ -677,7 +726,7 @@ class LLMTrainer:
|
||||
class LLMChat:
|
||||
"""LLM对话"""
|
||||
|
||||
def __init__(self, model: nn.Module, vocab: dict, device: str = 'cpu', max_length: int = 128,
|
||||
def __init__(self, model: nn.Module, vocab: dict, device: str = 'cpu', max_length: int = 256,
|
||||
show_thinking: bool = True):
|
||||
self.model = model.to(device)
|
||||
self.model.eval()
|
||||
@@ -692,14 +741,30 @@ class LLMChat:
|
||||
# 同步全局tokenizer的vocab（关键！）
|
||||
tokenizer_instance.char_to_id = vocab
|
||||
tokenizer_instance.id_to_char = {v: k for k, v in vocab.items()}
|
||||
# 清除encode缓存，避免用旧的cache导致错位
|
||||
tokenizer_instance._sorted_vocab_cache = None
|
||||
for attr in ['_multi_trie', '_multi_keys', '_single_map']:
|
||||
if hasattr(tokenizer_instance, attr):
|
||||
delattr(tokenizer_instance, attr)
|
||||
|
||||
def generate_response(self, question: str, max_length: int = 100, temperature: float = 0.8,
|
||||
include_thinking: bool = False, use_beam_search: bool = False):
|
||||
"""生成回答"""
|
||||
include_thinking: bool = False, use_beam_search: bool = False,
|
||||
history: list = None):
|
||||
"""生成回答
|
||||
history: 可选，历史对话列表 [(user_q, assistant_a), ...]
|
||||
"""
|
||||
self.model.eval()
|
||||
|
||||
# 使用与训练数据一致的格式: <SOS>问题<SEP><START>
|
||||
input_text = f"<SOS>{question}<SEP><START>"
|
||||
# 构建输入（支持多轮上下文）
|
||||
if history:
|
||||
parts = []
|
||||
for hq, ha in history:
|
||||
parts.append(hq)
|
||||
parts.append(ha)
|
||||
parts.append(question)
|
||||
input_text = f"<SOS>{'<SEP>'.join(parts)}<SEP><START>"
|
||||
else:
|
||||
input_text = f"<SOS>{question}<SEP><START>"
|
||||
|
||||
input_ids = tokenizer_instance.encode(input_text)
|
||||
|
||||
|
||||
@@ -0,0 +1,144 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
高温多样化生成 — 把训练集全部问题用 Astron 高温度重答一次
|
||||
温度=1.2，追求多样化回答，丰富训练数据
|
||||
"""
|
||||
import sys, os, json, time
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', buffering=1)
|
||||
|
||||
from api_correction import batch_generate
|
||||
|
||||
MODEL_DIR = os.path.dirname(os.path.abspath(__file__))
|
||||
TEMPERATURE = 1.0 # 高温度(上限1.0)，产出多样化回答
|
||||
BATCH_SIZE = 500 # 每批 500 条，分批保存防丢
|
||||
MAX_WORKERS = 5 # Astron 5并发
|
||||
|
||||
def extract_questions(data):
|
||||
"""从训练数据中提取所有问题"""
|
||||
questions = []
|
||||
for item in data:
|
||||
if isinstance(item, dict):
|
||||
q = item.get('question', '') or item.get('input', '')
|
||||
if q:
|
||||
questions.append(q)
|
||||
return questions
|
||||
|
||||
def main():
|
||||
# 1. 加载训练数据
|
||||
train_path = os.path.join(MODEL_DIR, 'xiaoli_train_v2.json')
|
||||
print(f"加载训练数据: {train_path}")
|
||||
with open(train_path, encoding='utf-8') as f:
|
||||
train_data = json.load(f)
|
||||
|
||||
questions = extract_questions(train_data)
|
||||
total = len(questions)
|
||||
print(f"提取到 {total} 条问题，温度={TEMPERATURE}，{MAX_WORKERS}并发")
|
||||
|
||||
# 2. 创建输出目录
|
||||
out_dir = os.path.join(MODEL_DIR, 'high_temp_outputs')
|
||||
os.makedirs(out_dir, exist_ok=True)
|
||||
|
||||
# 3. 分批处理
|
||||
all_results = []
|
||||
existing = set() # 去重用
|
||||
|
||||
# 检查是否有已完成的中间结果
|
||||
interim_files = sorted([f for f in os.listdir(out_dir) if f.startswith('batch_') and f.endswith('.json')])
|
||||
if interim_files:
|
||||
for fname in interim_files:
|
||||
with open(os.path.join(out_dir, fname), encoding='utf-8') as f:
|
||||
batch_data = json.load(f)
|
||||
for r in batch_data:
|
||||
key = (r['question'][:100], r['answer'][:100])
|
||||
if key not in existing:
|
||||
existing.add(key)
|
||||
all_results.append(r)
|
||||
print(f"发现已有中间结果: {len(all_results)} 条（来自 {len(interim_files)} 个文件）")
|
||||
# 跳过已处理的 question
|
||||
processed_questions = set(r['question'] for r in all_results)
|
||||
remaining = [q for q in questions if q not in processed_questions]
|
||||
print(f"已处理 {len(processed_questions)} 条，剩余 {len(remaining)} 条")
|
||||
questions = remaining
|
||||
|
||||
total_batches = (len(questions) + BATCH_SIZE - 1) // BATCH_SIZE
|
||||
t_start = time.time()
|
||||
|
||||
for batch_idx in range(total_batches):
|
||||
batch_qs = questions[batch_idx * BATCH_SIZE : (batch_idx + 1) * BATCH_SIZE]
|
||||
print(f"\n{'='*60}")
|
||||
print(f"批次 {batch_idx + 1}/{total_batches} ({len(batch_qs)}条, 温度{TEMPERATURE})")
|
||||
print(f"{'='*60}")
|
||||
|
||||
t0 = time.time()
|
||||
results = batch_generate(batch_qs, temperature=TEMPERATURE, max_workers=MAX_WORKERS)
|
||||
elapsed = time.time() - t0
|
||||
|
||||
# 标注入参温度
|
||||
for r in results:
|
||||
r['temperature'] = TEMPERATURE
|
||||
|
||||
# 去重后加入总集
|
||||
new_in_batch = 0
|
||||
for r in results:
|
||||
key = (r['question'][:100], r['answer'][:100])
|
||||
if key not in existing:
|
||||
existing.add(key)
|
||||
all_results.append(r)
|
||||
new_in_batch += 1
|
||||
|
||||
# 立即保存本批结果
|
||||
batch_file = os.path.join(out_dir, f'batch_{batch_idx + 1:04d}.json')
|
||||
with open(batch_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(results, f, ensure_ascii=False, indent=2)
|
||||
|
||||
# 追加保存总进度
|
||||
progress_file = os.path.join(out_dir, 'all_progress.json')
|
||||
with open(progress_file, 'w', encoding='utf-8') as f:
|
||||
json.dump(all_results, f, ensure_ascii=False, indent=2)
|
||||
|
||||
total_elapsed = time.time() - t_start
|
||||
rate = len(all_results) / max(total_elapsed, 1) * 60
|
||||
eta_remaining = (total - len(all_results)) / max(rate, 0.1) / 60 if rate > 0 else 0
|
||||
print(f" 本批新增 {new_in_batch} 条 (去重后) | 累计 {len(all_results)} 条 | "
|
||||
f"{rate:.0f}条/分 | 已用 {total_elapsed/60:.1f}分 | 预计剩余 {eta_remaining:.1f}分")
|
||||
|
||||
# 4. 去重统计
|
||||
print(f"\n{'='*60}")
|
||||
print(f"全部生成完成!")
|
||||
print(f"生成结果: {len(all_results)} 条 (去重后)")
|
||||
print(f"用时: {(time.time() - t_start)/60:.1f} 分钟")
|
||||
|
||||
# 5. 追加到训练集
|
||||
for tgt in ['xiaoli_train_v2.json', 'xiaoli_merged_data_v2.json']:
|
||||
tp = os.path.join(MODEL_DIR, tgt)
|
||||
print(f"\n追加到 {tgt}...")
|
||||
with open(tp, encoding='utf-8') as f:
|
||||
cur = json.load(f)
|
||||
|
||||
# 去重
|
||||
ex = set((it.get('question', '')[:100], it.get('answer', '')[:100]) for it in cur)
|
||||
to_add = []
|
||||
for r in all_results:
|
||||
key = (r['question'][:100], r['answer'][:100])
|
||||
if key not in ex:
|
||||
ex.add(key)
|
||||
to_add.append({'question': r['question'], 'answer': r['answer']})
|
||||
|
||||
cur.extend(to_add)
|
||||
with open(tp, 'w', encoding='utf-8') as f:
|
||||
json.dump(cur, f, ensure_ascii=False, indent=2)
|
||||
|
||||
if 'train' in tgt:
|
||||
print(f" ✅ 训练集 +{len(to_add)} 条 → {len(cur)} 条")
|
||||
else:
|
||||
print(f" ✅ 合并集 +{len(to_add)} 条 → {len(cur)} 条")
|
||||
|
||||
print(f"\n{'='*60}")
|
||||
print(f"全部完成! 新增 {len(all_results)} 条多样化回答")
|
||||
print(f"{'='*60}")
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
+495
@@ -0,0 +1,495 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
小狸完整数据集训练 - 支持断点续训 + batch=4防OOM
|
||||
"""
|
||||
import sys, os
|
||||
sys.path.insert(0, '/workspace/xiaoli-llm-chat')
|
||||
sys.stdout = os.fdopen(sys.stdout.fileno(), 'w', buffering=1)
|
||||
sys.stderr = os.fdopen(sys.stderr.fileno(), 'w', buffering=1)
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import json
|
||||
import time
|
||||
import glob
|
||||
import random
|
||||
from torch.utils.data import Dataset, DataLoader
|
||||
|
||||
from llm_chat_improved import (
|
||||
CharTokenizer, build_vocabulary, LLMModel, LLMChat,
|
||||
ConversationDataset, LLMTrainer, tokenizer_instance
|
||||
)
|
||||
from api_correction import batch_convert, batch_generate_multi_temp
|
||||
|
||||
def dict_to_tuple(data_list):
|
||||
result = []
|
||||
for item in data_list:
|
||||
if isinstance(item, dict):
|
||||
q = item.get("question", "") or item.get("input", "") or item.get("prompt", "") or item.get("instruction", "")
|
||||
a = item.get("answer", "") or item.get("output", "") or item.get("response", "")
|
||||
if q and a:
|
||||
result.append((q, a))
|
||||
elif isinstance(item, (list, tuple)) and len(item) >= 2:
|
||||
result.append((str(item[0]), str(item[1])))
|
||||
return result
|
||||
|
||||
def load_multiturn_data(path):
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
data = json.load(f)
|
||||
single_turn = []
|
||||
for item in data:
|
||||
if isinstance(item, dict) and "conversations" in item:
|
||||
convs = item["conversations"]
|
||||
for i in range(0, len(convs) - 1, 2):
|
||||
if i + 1 < len(convs):
|
||||
c1, c2 = convs[i], convs[i+1]
|
||||
if isinstance(c1, dict):
|
||||
h = c1.get("value", "") or c1.get("content", "")
|
||||
a = c2.get("value", "") or c2.get("content", "")
|
||||
elif isinstance(c1, list) and len(c1) >= 2:
|
||||
h, a = str(c1[1]), str(c2[1])
|
||||
else:
|
||||
continue
|
||||
if h and a:
|
||||
single_turn.append((h, a))
|
||||
return single_turn
|
||||
|
||||
def find_latest_checkpoint(model_dir):
|
||||
"""查找最新的checkpoint，返回 (路径, epoch数) 或 (None, 0)"""
|
||||
checkpoints = glob.glob(os.path.join(model_dir, 'llm_model_v2_resume_epoch_*.pth'))
|
||||
if not checkpoints:
|
||||
checkpoints = glob.glob(os.path.join(model_dir, 'llm_model_v2_epoch_*.pth'))
|
||||
if not checkpoints:
|
||||
return None, 0
|
||||
best_path = None
|
||||
best_epoch = 0
|
||||
for path in checkpoints:
|
||||
import re
|
||||
nums = [int(s) for s in re.findall(r'\d+', os.path.basename(path).replace('.pth',''))]
|
||||
if nums:
|
||||
epoch = max(nums)
|
||||
if epoch > best_epoch:
|
||||
best_epoch = epoch
|
||||
best_path = path
|
||||
return best_path, best_epoch
|
||||
|
||||
def migrate_weights(old_sd, new_sd_init, old_d_model, new_d_model,
|
||||
old_n_layers, new_n_layers, old_d_ff, new_d_ff,
|
||||
old_vocab_size, new_vocab_size):
|
||||
"""权重扩展迁移：将旧模型权重扩展到新架构
|
||||
支持 d_model / n_layers / d_ff / vocab_size 全部变化
|
||||
"""
|
||||
import torch
|
||||
new_sd = {}
|
||||
old_keys = list(old_sd.keys())
|
||||
new_keys = list(new_sd_init.keys())
|
||||
|
||||
for key in new_keys:
|
||||
if 'freq' in key:
|
||||
# RoPE频率表，新模型自动生成，跳过
|
||||
new_sd[key] = new_sd_init[key].clone()
|
||||
continue
|
||||
|
||||
# 解析层索引（如 transformer_blocks.3.attention.wq.weight）
|
||||
old_key = key
|
||||
new_w = new_sd_init[key].clone()
|
||||
old_w_shape = None
|
||||
|
||||
if key in old_sd:
|
||||
old_w = old_sd[key]
|
||||
old_w_shape = old_w.shape
|
||||
new_shape = new_w.shape
|
||||
|
||||
# 1. 严格匹配 = 直接复制
|
||||
if old_w.shape == new_shape:
|
||||
new_w = old_w.clone()
|
||||
# 2. 二维权重（线性层 + embedding）
|
||||
elif old_w.dim() == 2:
|
||||
o0, o1 = old_w.shape
|
||||
n0, n1 = new_shape
|
||||
# embedding.weight 和 fc_out.weight: 词汇表维度变化
|
||||
if 'embedding' in key and old_vocab_size != new_vocab_size:
|
||||
# 词汇表扩展：复制旧词嵌入，新词随机
|
||||
min_v = min(old_vocab_size, new_vocab_size)
|
||||
min_d = min(o1, n1)
|
||||
new_w[:min_v, :min_d] = old_w[:min_v, :min_d]
|
||||
print(f" 迁移 {key}: ({o0},{o1})→({n0},{n1}) vocab扩展")
|
||||
elif 'fc_out' in key and old_vocab_size != new_vocab_size:
|
||||
min_vocab = min(old_vocab_size, new_vocab_size)
|
||||
min_feat = min(o1, n1)
|
||||
new_w[:min_vocab, :min_feat] = old_w[:min_vocab, :min_feat]
|
||||
print(f" 迁移 {key}: ({o0},{o1})→({n0},{n1}) vocab扩展")
|
||||
elif o0 <= n0 and o1 <= n1:
|
||||
# 权重扩展（old 是 new 的子集，放在左上角）
|
||||
new_w[:o0, :o1] = old_w
|
||||
if o0 < n0 or o1 < n1:
|
||||
print(f" 扩展 {key}: ({o0},{o1})→({n0},{n1})")
|
||||
elif n0 <= o0 and n1 <= o1:
|
||||
# 权重缩小（new 是 old 的子集，取左上角）
|
||||
new_w = old_w[:n0, :n1].clone()
|
||||
print(f" 缩小 {key}: ({o0},{o1})→({n0},{n1})")
|
||||
else:
|
||||
# 特殊形状 → 尽量复制重合部分
|
||||
min0 = min(o0, n0)
|
||||
min1 = min(o1, n1)
|
||||
new_w[:min0, :min1] = old_w[:min0, :min1]
|
||||
print(f" 部分迁移 {key}: ({o0},{o1})→({n0},{n1})")
|
||||
# 3. 一维权重（LayerNorm, bias等）
|
||||
elif old_w.dim() == 1:
|
||||
o0 = old_w.shape[0]
|
||||
n0 = new_w.shape[0]
|
||||
min_d = min(o0, n0)
|
||||
new_w[:min_d] = old_w[:min_d]
|
||||
if o0 != n0:
|
||||
print(f" 迁移 {key}: ({o0})→({n0}) norm扩展")
|
||||
else:
|
||||
new_w = old_w.clone()
|
||||
else:
|
||||
# 旧模型中没有这个key（可能是不存在的层）
|
||||
# 检查是否是额外的新层
|
||||
# 如 transformer_blocks.6.xxx → 旧模型只有0-5
|
||||
new_sd[key] = new_w # 保持随机初始化
|
||||
continue
|
||||
|
||||
new_sd[key] = new_w
|
||||
|
||||
return new_sd
|
||||
|
||||
|
||||
def main():
|
||||
print("=" * 60)
|
||||
print("小狸训练 - 从头训练版 v2 (13.2M架构)")
|
||||
print("=" * 60)
|
||||
|
||||
device = 'cpu'
|
||||
model_dir = '/workspace/xiaoli-llm-chat'
|
||||
|
||||
# 加载数据 - 从合并文件加载
|
||||
merged_path = os.path.join(model_dir, 'xiaoli_merged_data_v2.json')
|
||||
if not os.path.exists(merged_path):
|
||||
print("❌ 未找到合并数据文件 xiaoli_merged_data.json")
|
||||
print("请先运行合并脚本")
|
||||
return
|
||||
|
||||
with open(merged_path, 'r', encoding='utf-8') as f:
|
||||
data = json.load(f)
|
||||
|
||||
unique = dict_to_tuple(data)
|
||||
print(f"加载合并数据: {len(unique)}条")
|
||||
|
||||
# ====== 划分训练/验证集（保存到独立文件，避免问题重复问题 ======
|
||||
val_split = 0.1
|
||||
train_path = os.path.join(model_dir, 'xiaoli_train_v2.json')
|
||||
val_path = os.path.join(model_dir, 'xiaoli_valid_v2.json')
|
||||
|
||||
if os.path.exists(train_path) and os.path.exists(val_path):
|
||||
# 从独立文件加载（精确的position-based划分）
|
||||
with open(train_path, 'r', encoding='utf-8') as f:
|
||||
train_set = dict_to_tuple(json.load(f))
|
||||
with open(val_path, 'r', encoding='utf-8') as f:
|
||||
val_set = dict_to_tuple(json.load(f))
|
||||
# 检查是否有新数据（来自外部脚本）
|
||||
train_qs = set(q for q, _ in train_set)
|
||||
val_qs = set(q for q, _ in val_set)
|
||||
new_entries = [(q, a) for q, a in unique if q not in train_qs and q not in val_qs]
|
||||
if new_entries:
|
||||
train_set.extend(new_entries)
|
||||
with open(train_path, 'w', encoding='utf-8') as f:
|
||||
json.dump([{"question": q, "answer": a} for q, a in train_set], f, ensure_ascii=False, indent=2)
|
||||
print(f" 新增外部数据: {len(new_entries)}条 → 加入训练集")
|
||||
print(f" 训练集: {len(train_set)}条 | 验证集: {len(val_set)}条 (来自文件)")
|
||||
else:
|
||||
# 首次划分：随机打乱后按位置切分
|
||||
shuffled = unique.copy()
|
||||
random.seed(42)
|
||||
random.shuffle(shuffled)
|
||||
split_idx = int(len(shuffled) * val_split)
|
||||
val_set = shuffled[:split_idx]
|
||||
train_set = shuffled[split_idx:]
|
||||
# 保存两份独立文件
|
||||
with open(train_path, 'w', encoding='utf-8') as f:
|
||||
json.dump([{"question": q, "answer": a} for q, a in train_set], f, ensure_ascii=False, indent=2)
|
||||
with open(val_path, 'w', encoding='utf-8') as f:
|
||||
json.dump([{"question": q, "answer": a} for q, a in val_set], f, ensure_ascii=False, indent=2)
|
||||
print(f" 首次划分: 训练集{len(train_set)}条 + 验证集{len(val_set)}条 (已保存)")
|
||||
|
||||
print(f" 训练集: {len(train_set)}条 | 验证集: {len(val_set)}条")
|
||||
|
||||
# ====== 加载固定vocab ======
|
||||
vocab_path = os.path.join(model_dir, 'vocab_v2.json')
|
||||
if not os.path.exists(vocab_path):
|
||||
print("❌ 未找到 vocab_v2.json，请先运行固定vocab生成脚本")
|
||||
return
|
||||
with open(vocab_path, 'r', encoding='utf-8') as f:
|
||||
vocab = json.load(f)
|
||||
print(f"词汇表: {len(vocab)} (固定, 来自 vocab_v2.json)")
|
||||
# 同步tokenizer（新字符映射为UNK）
|
||||
tokenizer_instance.char_to_id = vocab.copy()
|
||||
tokenizer_instance.id_to_char = {v: k for k, v in vocab.items()}
|
||||
tokenizer_instance._sorted_vocab_cache = None
|
||||
for attr in ['_multi_trie', '_multi_keys', '_single_map']:
|
||||
if hasattr(tokenizer_instance, attr):
|
||||
delattr(tokenizer_instance, attr)
|
||||
|
||||
# ====== 加载蒸馏问题(v3 + 真题) ======
|
||||
distill_q_path = os.path.join(model_dir, 'exam_distill_questions.json')
|
||||
if not os.path.exists(distill_q_path):
|
||||
distill_q_path = os.path.join(model_dir, 'distill_questions_v3.json')
|
||||
if not os.path.exists(distill_q_path):
|
||||
distill_q_path = os.path.join(model_dir, 'distill_questions_v2.json')
|
||||
if os.path.exists(distill_q_path):
|
||||
with open(distill_q_path, 'r', encoding='utf-8') as f:
|
||||
distill_questions = json.load(f)
|
||||
print(f"蒸馏问题: {len(distill_questions)}道")
|
||||
else:
|
||||
distill_questions = []
|
||||
print("蒸馏问题: 无")
|
||||
|
||||
config = {
|
||||
'batch_size': 4,
|
||||
'gradient_accumulation_steps': 2,
|
||||
'epochs': 200,
|
||||
'd_model': 320, 'n_heads': 8, 'n_layers': 6,
|
||||
'd_ff': 1536, 'max_length': 256, 'dropout': 0.1,
|
||||
'use_amp': False,
|
||||
}
|
||||
|
||||
model = LLMModel(
|
||||
vocab_size=len(vocab), d_model=config['d_model'],
|
||||
n_heads=config['n_heads'], n_layers=config['n_layers'],
|
||||
d_ff=config['d_ff'], max_len=config['max_length'],
|
||||
dropout=config['dropout'],
|
||||
use_memory=False, use_thinking=False, use_moe=False,
|
||||
num_experts=1, top_k=1,
|
||||
)
|
||||
total_params = sum(p.numel() for p in model.parameters())
|
||||
print(f"参数量: {total_params:,} ({total_params/1e6:.2f}M)")
|
||||
|
||||
# 训练集/验证集 DataLoader
|
||||
train_dataset = ConversationDataset(train_set, vocab, config['max_length'])
|
||||
train_loader = DataLoader(train_dataset, batch_size=config['batch_size'],
|
||||
shuffle=True, num_workers=0)
|
||||
print(f"训练批次: {len(train_loader)}")
|
||||
|
||||
val_dataset = ConversationDataset(val_set, vocab, config['max_length'])
|
||||
val_loader = DataLoader(val_dataset, batch_size=config['batch_size'],
|
||||
shuffle=False, num_workers=0)
|
||||
print(f"验证批次: {len(val_loader)}")
|
||||
|
||||
# 检查断点
|
||||
ckpt_path, start_epoch = find_latest_checkpoint(model_dir)
|
||||
if ckpt_path and start_epoch > 0:
|
||||
print(f"\n发现断点: epoch {start_epoch} ({ckpt_path})")
|
||||
checkpoint = torch.load(ckpt_path, map_location=device)
|
||||
old_cfg = checkpoint.get('model_config', {})
|
||||
old_d_model = old_cfg.get('d_model', 256)
|
||||
old_n_layers = old_cfg.get('n_layers', 6)
|
||||
old_d_ff = old_cfg.get('d_ff', 1024)
|
||||
|
||||
# 检测架构变化，自动做权重扩展迁移
|
||||
new_sd_init = model.state_dict() # 随机初始化的新权重
|
||||
old_sd = checkpoint['model_state_dict']
|
||||
old_vocab_size = old_cfg.get('vocab_size', 0)
|
||||
new_vocab_size = len(vocab)
|
||||
|
||||
# 如果架构没变化，直接加载
|
||||
if (old_d_model == config['d_model'] and old_n_layers == config['n_layers']
|
||||
and old_d_ff == config['d_ff'] and old_vocab_size == new_vocab_size):
|
||||
model.load_state_dict(old_sd)
|
||||
print("✅ 架构一致，直接加载权重")
|
||||
else:
|
||||
print(f" 架构变化: d_model={old_d_model}→{config['d_model']}, "
|
||||
f"layers={old_n_layers}→{config['n_layers']}, "
|
||||
f"d_ff={old_d_ff}→{config['d_ff']}, "
|
||||
f"vocab={old_vocab_size}→{new_vocab_size}")
|
||||
print(" 执行权重扩展迁移（宽度扩展 + 深度复制）...")
|
||||
new_sd = migrate_weights(old_sd, new_sd_init, old_d_model, config['d_model'],
|
||||
old_n_layers, config['n_layers'],
|
||||
old_d_ff, config['d_ff'],
|
||||
old_vocab_size, new_vocab_size)
|
||||
model.load_state_dict(new_sd)
|
||||
print(" ✅ 权重扩展迁移完成！")
|
||||
|
||||
# 记录旧checkpoint已使用，从下一epoch开始
|
||||
start_epoch = start_epoch
|
||||
|
||||
# 尝试加载优化器（仅当架构vocab完全一致时）
|
||||
if (old_d_model == config['d_model'] and old_n_layers == config['n_layers']
|
||||
and old_d_ff == config['d_ff'] and old_vocab_size == new_vocab_size
|
||||
and 'optimizer_state_dict' in checkpoint):
|
||||
try:
|
||||
trainer = LLMTrainer(model, device, use_amp=config['use_amp'])
|
||||
# 前向一步恢复优化器（不做完整epoch，太慢）
|
||||
batch = next(iter(train_loader))
|
||||
input_ids = batch['input_ids'].to(device)
|
||||
target_ids = batch['target_ids'].to(device)
|
||||
mask = model.generate_mask(input_ids.size(1)).to(device)
|
||||
outputs = model(input_ids, mask)
|
||||
loss = trainer.criterion(outputs.reshape(-1, outputs.size(-1)), target_ids.reshape(-1))
|
||||
loss.backward()
|
||||
trainer.optimizer.step()
|
||||
trainer.optimizer.zero_grad()
|
||||
# 加载优化器状态
|
||||
trainer.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
|
||||
print("优化器状态已加载（1步热身）")
|
||||
except Exception as e:
|
||||
print(f"优化器加载失败（将使用新的）: {e}")
|
||||
|
||||
print(f"\n开始训练: epoch {start_epoch + 1} -> {config['epochs']}")
|
||||
print("=" * 60)
|
||||
start = time.time()
|
||||
|
||||
# 确保trainer已创建（可能因为架构变化没有在提前创建）
|
||||
if 'trainer' not in dir() or trainer is None:
|
||||
trainer = LLMTrainer(model, device, use_amp=config['use_amp'])
|
||||
|
||||
best_val_loss = float('inf')
|
||||
total_distill_added = 0
|
||||
for epoch in range(start_epoch + 1, config['epochs'] + 1):
|
||||
# ====== 每轮从train_path重建dataloader（蒸馏数据已追加至此）======
|
||||
with open(train_path, 'r', encoding='utf-8') as f:
|
||||
epoch_train = dict_to_tuple(json.load(f))
|
||||
print(f"\n 训练数据: {len(epoch_train)}条 (含{total_distill_added}条蒸馏新增)")
|
||||
|
||||
train_dataset = ConversationDataset(epoch_train, vocab, config['max_length'])
|
||||
train_loader = DataLoader(train_dataset, batch_size=config['batch_size'],
|
||||
shuffle=True, num_workers=0)
|
||||
|
||||
# ====== 训练 ======
|
||||
t0 = time.time()
|
||||
avg_loss = trainer.train_epoch(train_loader, epoch, vocab, config)
|
||||
dt = time.time() - t0
|
||||
|
||||
# ====== 验证 ======
|
||||
t1 = time.time()
|
||||
val_loss = trainer.evaluate(val_loader, config)
|
||||
dt_val = time.time() - t1
|
||||
|
||||
# 打印train/val loss
|
||||
marker = ""
|
||||
if val_loss < best_val_loss:
|
||||
best_val_loss = val_loss
|
||||
marker = " ★"
|
||||
best_path = os.path.join(model_dir, 'xiaoli_v2_best.pth')
|
||||
trainer.save_model(best_path, vocab, epoch)
|
||||
print(f"Epoch {epoch}/{config['epochs']} | Train Loss: {avg_loss:.4f} | Val Loss: {val_loss:.4f}{marker} | {dt:.0f}s+{dt_val:.0f}s")
|
||||
|
||||
# 每轮保存断点
|
||||
resume_path = os.path.join(model_dir, f'llm_model_v2_resume_epoch_{epoch}.pth')
|
||||
trainer.save_model(resume_path, vocab, epoch)
|
||||
if epoch % 20 == 0:
|
||||
save_path = os.path.join(model_dir, f'llm_model_v2_epoch_{epoch}.pth')
|
||||
trainer.save_model(save_path, vocab, epoch)
|
||||
print(f" 已保存正式checkpoint: epoch {epoch}")
|
||||
resume_files = sorted(glob.glob(os.path.join(model_dir, 'llm_model_v2_resume_*.pth')))
|
||||
for old in resume_files[:-5]:
|
||||
try: os.remove(old)
|
||||
except: pass
|
||||
|
||||
# ====== 每轮末尾：在线蒸馏 ======
|
||||
if distill_questions:
|
||||
distill_t0 = time.time()
|
||||
try:
|
||||
print(f" 📡 在线蒸馏 ({len(distill_questions)}题 × 多种方式)...")
|
||||
chat = LLMChat(model, vocab, device, config['max_length'])
|
||||
all_new_pairs = []
|
||||
|
||||
# ====== Pass 1: 模型生成(temperature=0.15) → API转换 ======
|
||||
print(" [P1] 模型生成中...")
|
||||
model_answers = []
|
||||
for i, q in enumerate(distill_questions):
|
||||
ans = chat.generate_response(q, max_length=40, temperature=0.15)
|
||||
model_answers.append((q, ans))
|
||||
if (i + 1) % 100 == 0:
|
||||
print(f" P1: {i+1}/{len(distill_questions)}")
|
||||
p1_gen = time.time()
|
||||
print(f" [P1] 生成完毕 ({(p1_gen-distill_t0):.0f}s)，API转换中...")
|
||||
|
||||
def progress(done, total):
|
||||
if done % 50 == 0 or done == total:
|
||||
print(f" API: {done}/{total}", end="\r")
|
||||
|
||||
p1_results = batch_convert(model_answers, progress)
|
||||
print(f" API: {len(model_answers)}/{len(model_answers)}")
|
||||
all_new_pairs.extend(p1_results)
|
||||
print(f" [P1] +{len(p1_results)}条")
|
||||
|
||||
# ====== Pass 2: 模型高温生成(temperature=0.7) → 更多多样性 ======
|
||||
print(f" [P2] 模型高温生成中 (temp=0.7)...")
|
||||
model_answers2 = []
|
||||
for i, q in enumerate(distill_questions):
|
||||
ans = chat.generate_response(q, max_length=40, temperature=0.7)
|
||||
model_answers2.append((q, ans))
|
||||
if (i + 1) % 100 == 0:
|
||||
print(f" P2: {i+1}/{len(distill_questions)}")
|
||||
p2_gen = time.time()
|
||||
print(f" [P2] 生成完毕 ({(p2_gen-p1_gen):.0f}s)，API转换中...")
|
||||
|
||||
p2_results = batch_convert(model_answers2, progress)
|
||||
print(f" API: {len(model_answers2)}/{len(model_answers2)}")
|
||||
all_new_pairs.extend(p2_results)
|
||||
print(f" [P2] +{len(p2_results)}条")
|
||||
|
||||
# ====== Pass 3: API直接生成多样回答(temp=1.0) ======
|
||||
print(f" [P3] API直接生成 (temp=1.0, 全部直答)...")
|
||||
p3_t0 = time.time()
|
||||
p3_results = batch_generate_multi_temp(
|
||||
distill_questions,
|
||||
temperatures=[1.0],
|
||||
max_workers=8
|
||||
)
|
||||
all_new_pairs.extend(p3_results)
|
||||
print(f" [P3] +{len(p3_results)}条")
|
||||
|
||||
# ====== 合并去重后追加 ======
|
||||
if all_new_pairs:
|
||||
with open(train_path, 'r', encoding='utf-8') as f:
|
||||
cur_train = json.load(f)
|
||||
existing_pairs = set((item.get("question",""), item.get("answer","")) for item in cur_train)
|
||||
truly_new = [item for item in all_new_pairs
|
||||
if (item["question"], item["answer"]) not in existing_pairs]
|
||||
|
||||
if truly_new:
|
||||
cur_train.extend(truly_new)
|
||||
with open(train_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(cur_train, f, ensure_ascii=False, indent=2)
|
||||
# 同步到merged
|
||||
with open(merged_path, 'r', encoding='utf-8') as f:
|
||||
cur_merged = json.load(f)
|
||||
cur_merged.extend(truly_new)
|
||||
with open(merged_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(cur_merged, f, ensure_ascii=False, indent=2)
|
||||
total_distill_added += len(truly_new)
|
||||
distill_total = time.time() - distill_t0
|
||||
print(f" ✅ 蒸馏完成: +{len(truly_new)}条 (新增{len(set(q for q,a in [(i['question'],i['answer']) for i in truly_new]))}道独特问题) | 总耗时{distill_total:.0f}s")
|
||||
else:
|
||||
print(f" ℹ️ 蒸馏完成: 本轮无新内容（全部已存在）({time.time()-distill_t0:.0f}s)")
|
||||
else:
|
||||
print(f" ℹ️ 蒸馏完成: 无有效转换 ({time.time()-distill_t0:.0f}s)")
|
||||
except Exception as e:
|
||||
print(f" ⚠️ 蒸馏异常: {e}")
|
||||
|
||||
# 保存最终模型
|
||||
trainer.save_model(os.path.join(model_dir, 'xiaoli_v2_final.pth'), vocab, config['epochs'])
|
||||
|
||||
# 清理所有resume文件
|
||||
for f in glob.glob(os.path.join(model_dir, 'llm_model_v2_resume_*.pth')):
|
||||
try:
|
||||
os.remove(f)
|
||||
except:
|
||||
pass
|
||||
|
||||
total = time.time() - start
|
||||
print(f"\n训练完成！总耗时: {total/60:.1f}分钟")
|
||||
|
||||
# 测试
|
||||
print("\n测试:")
|
||||
chat = LLMChat(model, vocab, device, config['max_length'])
|
||||
for q in ["你好", "你是谁", "你叫什么名字", "你好可爱", "我喜欢你", "1+1等于几"]:
|
||||
r = chat.generate_response(q, max_length=60, temperature=0.8)
|
||||
print(f" Q: {q}")
|
||||
print(f" A: {r}\n")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:efabe0696ef9fcd64685f70a62bd55c97ca2b8efa69bca26df35177ac6774330
|
||||
size 41185
|
||||
+128
@@ -0,0 +1,128 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""小狸v1.2 Web聊天服务器 - 当前训练中的模型"""
|
||||
import sys, os, json, time
|
||||
sys.path.insert(0, '/workspace/xiaoli-llm-chat')
|
||||
import torch
|
||||
from flask import Flask, request, jsonify, send_from_directory
|
||||
from llm_chat_improved import LLMModel, LLMChat, build_vocabulary
|
||||
|
||||
print("=" * 50)
|
||||
print(" 小狸 v1.2 Web 服务加载中...")
|
||||
print("=" * 50)
|
||||
|
||||
device = 'cpu'
|
||||
model_dir = '/workspace/xiaoli-llm-chat'
|
||||
|
||||
# 构建当前vocab
|
||||
with open(f'{model_dir}/xiaoli_merged_data_v2.json', 'r', encoding='utf-8') as f:
|
||||
all_data = json.load(f)
|
||||
pairs = []
|
||||
for d in all_data:
|
||||
q = d.get('question','') or d.get('input','') or d.get('prompt','') or ''
|
||||
a = d.get('answer','') or d.get('output','') or d.get('response','') or ''
|
||||
if q and a:
|
||||
pairs.append((q, a))
|
||||
vocab = build_vocabulary(pairs)
|
||||
print(f"词汇表: {len(vocab)} | 数据: {len(all_data)}条")
|
||||
|
||||
# 加载模型 (epoch 50 = 干净epoch 41 + vocab扩展)
|
||||
ckpt_path = f'{model_dir}/llm_model_v2_resume_epoch_50.pth'
|
||||
print(f"加载: {ckpt_path}")
|
||||
ckpt = torch.load(ckpt_path, map_location=device)
|
||||
old_vocab_size = ckpt.get('model_config', {}).get('vocab_size', 0)
|
||||
cfg = ckpt['model_config']
|
||||
print(f"断点vocab: {old_vocab_size}")
|
||||
|
||||
model = LLMModel(
|
||||
vocab_size=len(vocab),
|
||||
d_model=cfg['d_model'], n_heads=cfg['n_heads'],
|
||||
n_layers=cfg['n_layers'], d_ff=cfg['d_ff'],
|
||||
max_len=cfg['max_len'], dropout=cfg.get('dropout', 0.1),
|
||||
use_memory=False, use_thinking=False, use_moe=False,
|
||||
num_experts=1, top_k=1,
|
||||
)
|
||||
|
||||
# 迁移权重
|
||||
old_sd = ckpt['model_state_dict']
|
||||
new_sd = model.state_dict()
|
||||
if old_vocab_size != len(vocab):
|
||||
for key in new_sd:
|
||||
if key in old_sd:
|
||||
w = old_sd[key]
|
||||
if w.shape == new_sd[key].shape:
|
||||
new_sd[key] = w.clone()
|
||||
elif len(w.shape) == 2 and w.shape[0] == old_vocab_size:
|
||||
new_sd[key][:old_vocab_size] = w
|
||||
elif 'freq' in key:
|
||||
pass
|
||||
else:
|
||||
try: new_sd[key] = w.clone()
|
||||
except: pass
|
||||
model.load_state_dict(new_sd)
|
||||
else:
|
||||
model.load_state_dict(old_sd)
|
||||
|
||||
model.to(device)
|
||||
model.eval()
|
||||
chat = LLMChat(model, vocab, device, max_length=cfg['max_len'])
|
||||
print(f"✅ 模型加载完成！ | voc={len(vocab)} | max_len={cfg['max_len']}")
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
@app.route('/')
|
||||
def index():
|
||||
return send_from_directory(model_dir, 'chat.html')
|
||||
|
||||
@app.route('/api/chat', methods=['POST'])
|
||||
def api_chat():
|
||||
data = request.get_json()
|
||||
question = data.get('question', '').strip()
|
||||
temp = float(data.get('temperature', 0.2))
|
||||
max_len = int(data.get('max_length', 100))
|
||||
history = data.get('history', None)
|
||||
|
||||
if not question:
|
||||
return jsonify({'error': '问题不能为空'})
|
||||
|
||||
# 如果有history，转换为对话格式
|
||||
history_pairs = None
|
||||
if history and isinstance(history, list) and len(history) > 0:
|
||||
history_pairs = [(h['q'], h['a']) for h in history]
|
||||
|
||||
try:
|
||||
t0 = time.time()
|
||||
answer = chat.generate_response(question, max_length=max_len, temperature=temp, history=history_pairs)
|
||||
elapsed = time.time() - t0
|
||||
return jsonify({
|
||||
'question': question,
|
||||
'answer': answer,
|
||||
'time': f'{elapsed:.1f}s',
|
||||
'temperature': temp,
|
||||
})
|
||||
except Exception as e:
|
||||
return jsonify({'error': str(e)})
|
||||
|
||||
@app.route('/api/info')
|
||||
def model_info():
|
||||
return jsonify({
|
||||
'name': '小狸 v1.2',
|
||||
'version': 'epoch 52 (架构扩展: 7.9M→21.3M, max_len=256)',
|
||||
'vocab_size': len(vocab),
|
||||
'max_length': cfg['max_len'],
|
||||
'd_model': cfg['d_model'],
|
||||
'layers': cfg['n_layers'],
|
||||
'heads': cfg['n_heads'],
|
||||
'd_ff': cfg['d_ff'],
|
||||
'data_size': f'{len(all_data):,}条',
|
||||
'training': '新架构训练中 epoch 1→200',
|
||||
'features': ['多轮对话(1,694条)', '长文本(758条)', '我不知道(1,140条)'],
|
||||
})
|
||||
|
||||
if __name__ == '__main__':
|
||||
print("\n" + "=" * 50)
|
||||
print(" 小狸 v1.2 聊天服务已启动！")
|
||||
print(" 访问地址: http://0.0.0.0:5000")
|
||||
print(" 建议温度: 0.1 ~ 0.2")
|
||||
print("=" * 50)
|
||||
app.run(host='0.0.0.0', port=5000, debug=False, threaded=True)
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b117303df258c2296699ad987c2e5f9ef141e9adef2b693723c2a41511a85d4d
|
||||
size 12616207
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:bfa8970a4a176f23451002081afec16c909b6c8889b42819d698bc2e44e06aeb
|
||||
size 12290087
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c445541e8aaefe41ef3223ec0f83988acf5e282a52f34d13f8560e551615a2d2
|
||||
size 327442
|
||||
Reference in new issue
Block a user