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Qwen3 Embedding 0.6B Onnx Uint8
这是一个基于ONNX的量化模型，是Qwen/Qwen3-Embedding-0.6B的uint8量化版本，在保持检索性能的同时减少了模型大小。
下载量 112
发布时间 : 6/8/2025
模型简介
该模型是一个文本嵌入模型，用于生成文本的向量表示，适用于信息检索、语义搜索等任务。
模型特点
高效量化
采用uint8量化技术，显著减少模型大小，同时保持检索性能。
高性能
与完整f32模型相比，检索性能差异仅约1%。
兼容性
与qdrant fastembed兼容，便于在相关环境中部署使用。
优化量化策略
通过排除484个敏感节点不进行量化，在模型大小和准确率之间取得良好平衡。
模型能力
文本向量化
语义搜索
信息检索
使用案例
信息检索
文档搜索
将文档转换为向量表示，实现基于语义的文档搜索。
推荐系统
内容推荐
通过内容向量相似度实现个性化推荐。
🚀 Qwen3-Embedding-0.6B-onnx-uint8
这是一个基于ONNX的量化模型，是 Qwen/Qwen3-Embedding-0.6B 的uint8量化版本。该模型在保持一定检索性能的同时，减少了模型大小，并且与qdrant fastembed兼容。
🚀 快速开始
本模型已经过动态量化为uint8，并进一步修改以输出一个uint8的1024维张量。使用时请注意以下细节：
- 执行模型时不进行池化和归一化操作。
- 注意以下代码中的示例查询格式。
✨ 主要特性
模型优化
- 提升了模型质量，但模型大小从571MiB增加到624MiB。
- 与完整的f32模型相比，检索性能差异仅约1%。
- 与onnx-community uint8模型（f32输出）相比，检索准确率提高了约6%。
- 与该模型的上一版本相比，检索准确率提高了约3.5%。
- 在我的硬件（Ryzen CPU）上，推理速度与上一版本相同。
兼容性
本模型与qdrant fastembed兼容，可在相关环境中使用。
🔧 技术细节
量化方法
为了辅助量化，创建了一个小型的ONNX模型检测框架。具体步骤如下：
- 生成校准数据：生成校准数据，并创建一个带检测功能的ONNX模型，记录推理过程中模型中每个张量的值范围。
- 节点筛选：测试不同的节点排除标准，最终选择了一个在模型大小和准确率之间取得良好平衡的方案，排除了484个最敏感的节点不进行量化。
- 再次校准：生成100万个标记的校准数据，并记录推理过程中看到的float32输出范围，范围为 -0.3009805381298065 到 0.3952634334564209。
- 量化转换：使用该范围进行从float32到uint8的非对称线性量化。
排除的节点
以下是排除量化的节点列表：
排除的节点列表
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