针对小模型蒸馏优化的合成数据生成
构建移动 NPU 部署的 0.5B-1B 模型时，合成数据质量的重要性成指数级增长。以下是如何生成、过滤和验证专为小模型蒸馏设计的合成训练数据。
构建移动 NPU 部署的 0.5B 参数模型与构建云推理的 70B 模型根本不同。模型小 140 倍。对噪声或不对齐训练数据的容忍度接近零。
标准方法——大教师模型生成示例，用于训练小学生模型——在学生为 7B-13B 时可以接受。但在学生为 0.5B-1B 时就崩溃了，因为教师生成的文本复杂度超出了学生能再现的水平。
关键过滤策略
长度分布匹配：匹配生产输入/输出分布。 复杂度评分：在学生模型上运行困惑度，丢弃高于阈值的示例。 领域相关性评分：嵌入相似度过滤。 格式一致性强制：零容忍格式变异。
数据
| 指标 | 朴素方法 | 优化方法 |
|---|---|---|
| 生成合成示例 | 100,000 | 100,000 |
| 最终训练集 | 80,000 | 20,000 |
| 端侧准确率 | 61-68% | 84-91% |
优化方法使用 75% 更少的训练示例，达到高 20-30 个百分点的准确率。质量优于数量不是空话——在低于 1B 的规模下它是全部策略。
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