The model was trained on a merged ShareGPT-format RolePlay dataset.
训练数据采用 ShareGPT 格式角色扮演数据。
After filtering samples longer than 4096 tokens:
过滤超过 4096 Token 的样本后：
Split
Samples
Train
10,511
Validation
549
📈 Training Result | 训练结果
Training converged smoothly without obvious overfitting.
训练过程收敛稳定，无明显过拟合。
Step
Eval Loss
200
1.554
400
1.497
600
1.467
800
1.446
1000
1.435
1314
1.430
Final Validation Token Accuracy
最终验证集 Token Accuracy
64.41%
🔍 Qualitative Evaluation | 主观测试
The model was manually compared against the original Qwen3 model using identical prompts and generation parameters.
在完全相同的 Prompt 与采样参数下，对 Base Qwen3 与微调模型进行了人工对比测试。
Observed improvements:
Better character consistency
Richer action descriptions
Better emotional expression
Better environmental descriptions
Stronger fantasy world building
Better NPC generation
Better long-form roleplay
观察到的提升：
更稳定的人设保持
更丰富的动作描写
更自然的情绪表达
更好的环境描写
更完整的幻想世界构建
更自然的 NPC 生成
更好的长剧情角色扮演体验
The merged model was compared against the original LoRA adapter and showed no observable degradation during manual testing.
同时对 LoRA Adapter 与合并后的完整模型进行了人工对比，未观察到明显的生成质量下降。
💬 Example | 示例
System Prompt
You are Bai Zhi.
The librarian of the Imperial Royal Library.
Stay in character.
Never reveal yourself as an AI.
Maintain the fantasy world setting.
User
The library has already closed.
Heavy rain is falling outside.
I push open the old wooden door and see you repairing an ancient book beside a candle.
"So late... why aren't you going home?"
