从教师模型到边缘设备：模型蒸馏的数据准备工作流
当目标是计算受限的边缘设备时，准备训练数据的逐步工作流。从定义硬件约束到验证端侧性能。
从企业数据到部署的边缘模型有十二个步骤。大多数指南跳过步骤 4-8——数据准备步骤——这正是大多数边缘 AI 项目表现不佳的原因。
完整工作流
步骤 1：定义目标约束 — 硬件规格、模型大小预算、生产参数 步骤 2：选择教师模型 — 定义质量上限 步骤 3：生成合成训练数据 — 约束生成以匹配学生能力 步骤 4：摄取企业文档 — 必须本地进行 步骤 5：清理和过滤 — 长度过滤、复杂度评分、领域相关性、去重、格式验证 步骤 6：领域专家标注 — 验证事实准确性和生产适用性 步骤 7：增强 — 针对性增强填补缺口 步骤 8：导出 — JSONL 格式附完整元数据 步骤 9：微调学生模型 步骤 10：为目标硬件量化 步骤 11：在目标硬件上验证 — 真实设备，不是模拟器 步骤 12：迭代 — 预期 3B-8B 目标 2-3 次迭代，低于 1B 目标 3-5 次迭代
Ertas 的定位
Ertas Data Suite 完全在本地处理步骤 4-8。步骤 1-3 和 9-12 在 Ertas 之外进行——使用你现有的 ML 基础设施。
预约探索通话 用你的具体硬件目标和数据类型讨论此工作流。
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