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Qwen3 Embedding 0.6B Onnx Uint8
這是一個基於ONNX的量化模型，是Qwen/Qwen3-Embedding-0.6B的uint8量化版本，在保持檢索性能的同時減少了模型大小。
下載量 112
發布時間 : 6/8/2025
模型概述
該模型是一個文本嵌入模型，用於生成文本的向量表示，適用於信息檢索、語義搜索等任務。
模型特點
高效量化
採用uint8量化技術，顯著減少模型大小，同時保持檢索性能。
高性能
與完整f32模型相比，檢索性能差異僅約1%。
兼容性
與qdrant fastembed兼容，便於在相關環境中部署使用。
優化量化策略
通過排除484個敏感節點不進行量化，在模型大小和準確率之間取得良好平衡。
模型能力
文本向量化
語義搜索
信息檢索
使用案例
信息檢索
文檔搜索
將文檔轉換為向量表示，實現基於語義的文檔搜索。
推薦系統
內容推薦
通過內容向量相似度實現個性化推薦。
🚀 Qwen3-Embedding-0.6B-onnx-uint8
這是一個基於ONNX的量化模型，是 Qwen/Qwen3-Embedding-0.6B 的uint8量化版本。該模型在保持一定檢索性能的同時，減少了模型大小，並且與qdrant fastembed兼容。
🚀 快速開始
本模型已經過動態量化為uint8，並進一步修改以輸出一個uint8的1024維張量。使用時請注意以下細節：
- 執行模型時不進行池化和歸一化操作。
- 注意以下代碼中的示例查詢格式。
✨ 主要特性
模型優化
- 提升了模型質量，但模型大小從571MiB增加到624MiB。
- 與完整的f32模型相比，檢索性能差異僅約1%。
- 與onnx-community uint8模型（f32輸出）相比，檢索準確率提高了約6%。
- 與該模型的上一版本相比，檢索準確率提高了約3.5%。
- 在我的硬件（Ryzen CPU）上，推理速度與上一版本相同。
兼容性
本模型與qdrant fastembed兼容，可在相關環境中使用。
🔧 技術細節
量化方法
為了輔助量化，創建了一個小型的ONNX模型檢測框架。具體步驟如下：
- 生成校準數據：生成校準數據，並創建一個帶檢測功能的ONNX模型，記錄推理過程中模型中每個張量的值範圍。
- 節點篩選：測試不同的節點排除標準，最終選擇了一個在模型大小和準確率之間取得良好平衡的方案，排除了484個最敏感的節點不進行量化。
- 再次校準：生成100萬個標記的校準數據，並記錄推理過程中看到的float32輸出範圍，範圍為 -0.3009805381298065 到 0.3952634334564209。
- 量化轉換：使用該範圍進行從float32到uint8的非對稱線性量化。
排除的節點
以下是排除量化的節點列表：
排除的節點列表
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