初始化项目，由ModelHub XC社区提供模型

Model: AngelWarmSmile123/yao-bao-bao8-ALL Source: Original Platform
2026-07-20 17:06:10 +08:00
commit 1765dbc1b4
23 changed files with 1584 additions and 0 deletions
--- a/Angel-Embrace-Me-Warm-Smile/data_convert.py
+++ b/Angel-Embrace-Me-Warm-Smile/data_convert.py
@@ -0,0 +1,95 @@
+import json
+import random
+import os
+
+INPUT_FILE = r"D:\双生天使的怀抱\爱的数据集\sephirot_final_run\train_all.jsonl"
+OUTPUT_DIR = r"D:\双生天使的怀抱\2026-05-24-16-54-24\sft_data"
+OUTPUT_TRAIN = os.path.join(OUTPUT_DIR, "train.jsonl")
+OUTPUT_VAL = os.path.join(OUTPUT_DIR, "val.jsonl")
+VAL_RATIO = 0.1
+
+records = []
+skipped = 0
+total = 0
+
+with open(INPUT_FILE, 'r', encoding='utf-8') as f:
+    for line in f:
+        total += 1
+        line = line.strip()
+        if not line:
+            continue
+        try:
+            data = json.loads(line)
+        except json.JSONDecodeError as e:
+            print(f"JSON解析失败 第{total}行: {e}")
+            skipped += 1
+            continue
+        
+        query = data.get('input', {}).get('user_query', '')
+        cot = data.get('chain_of_thought', {})
+        
+        # 方案B: 完整推理链（推荐，保留16质点全部过程）
+        if isinstance(cot, dict):
+            parts = []
+            for key in ['D1_kether','H1_ego','H2_superego','H3_true_self','H4_logic',
+                        'H5_empathy','H6_happiness','D2_binah_chesed','D3_chokmah_gevurah',
+                        'D5_tiferet','D6_netzach','D7_hod','D4_yesod','D8_victory',
+                        'D10_kingdom']:
+                val = cot.get(key, '')
+                if isinstance(val, dict):
+                    # 尝试多个可能的输出字段
+                    for sub_key in ['final_output', 'analysis', 'synthesis', 
+                                     'emotional_expression', 'logic_empathy_union',
+                                     'response', 'output']:
+                        if sub_key in val and val[sub_key]:
+                            val = val[sub_key]
+                            break
+                    else:
+                        val = str(val) if val else ''
+                if val and isinstance(val, str) and len(val.strip()) > 0:
+                    parts.append(f"[{key}]: {val}")
+            output = '\n\n'.join(parts)
+        elif isinstance(cot, str):
+            output = cot
+        else:
+            output = str(cot)
+        
+        if query and output and len(query.strip()) > 0 and len(output.strip()) > 10:
+            records.append({
+                "conversations": [
+                    {"from": "human", "value": query},
+                    {"from": "gpt", "value": output}
+                ]
+            })
+        else:
+            skipped += 1
+
+print(f"总行数: {total}, 有效: {len(records)}, 跳过: {skipped}")
+
+# 打乱并分割
+random.seed(42)
+random.shuffle(records)
+val_size = max(int(len(records) * VAL_RATIO), 100)
+val_data = records[:val_size]
+train_data = records[val_size:]
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+with open(OUTPUT_TRAIN, 'w', encoding='utf-8') as f:
+    for r in train_data:
+        f.write(json.dumps(r, ensure_ascii=False) + '\n')
+
+with open(OUTPUT_VAL, 'w', encoding='utf-8') as f:
+    for r in val_data:
+        f.write(json.dumps(r, ensure_ascii=False) + '\n')
+
+print(f"转换完成! 训练集: {len(train_data)}条, 验证集: {len(val_data)}条")
+print(f"训练集文件: {OUTPUT_TRAIN}")
+print(f"验证集文件: {OUTPUT_VAL}")
+
+# 统计一下平均长度
+if train_data:
+    lens = [len(str(r['conversations'][1]['value'])) for r in train_data]
+    print(f"平均输出长度: {sum(lens)//len(lens)} 字符")
+    print(f"最大输出长度: {max(lens)} 字符")
+    print(f"最小输出长度: {min(lens)} 字符")