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Qwen3-Embedding-0.6B-onnx-uint8

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qwen3
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  1. README.md +535 -8
  2. dynamic_uint8.onnx +2 -2
README.md CHANGED
@@ -1,3 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
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  # Qwen3-Embedding-0.6B-onnx-uint8
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  This is an onnx version of https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
@@ -9,6 +21,508 @@ This model is compatible with qdrant fastembed, please note these details:
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  - Execute model without pooling and without normalization
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  - Pay attention to the example query format in the code below
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  # Benchmarks
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  I used beir-qdrant with the scifact dataset.
@@ -19,13 +533,8 @@ I welcome any additional benchmarks by the community, please feel free to share
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  If someone wants to sponsor me with an NVIDIA GPU I can have a much faster turnaround time with my model experiments and explore some different quantization strategies.
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- edit: I've done pretty extensive testing, including comparing benchmarks to:
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- https://huggingface.co/onnx-community/Qwen3-Embedding-0.6B-ONNX/blob/main/onnx/model_uint8.onnx
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-
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- and haven't been able to surpass this initial model.
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-
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- onnx f32 model with f32 output:
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  ```
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  ndcg: {'NDCG@1': 0.57, 'NDCG@3': 0.65655, 'NDCG@5': 0.68177, 'NDCG@10': 0.69999, 'NDCG@100': 0.72749, 'NDCG@1000': 0.73301}
@@ -33,7 +542,7 @@ recall: {'Recall@1': 0.53828, 'Recall@3': 0.71517, 'Recall@5': 0.77883, 'Recall@
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  precision: {'P@1': 0.57, 'P@3': 0.26111, 'P@5': 0.17467, 'P@10': 0.09467, 'P@100': 0.01083, 'P@1000': 0.00113}
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  ```
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- onnx dynamic uint8 model with f32 output:
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  ```
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  ndcg: {'NDCG@1': 0.52333, 'NDCG@3': 0.58087, 'NDCG@5': 0.59811, 'NDCG@10': 0.6249, 'NDCG@100': 0.66025, 'NDCG@1000': 0.67023}
@@ -41,7 +550,9 @@ recall: {'Recall@1': 0.4965, 'Recall@3': 0.62211, 'Recall@5': 0.66622, 'Recall@1
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  precision: {'P@1': 0.52333, 'P@3': 0.22889, 'P@5': 0.15, 'P@10': 0.085, 'P@100': 0.0103, 'P@1000': 0.00111}
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  ```
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- onnx dynamic uint8 model with uint8 output (this model):
 
 
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  ```
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  ndcg: {'NDCG@1': 0.52667, 'NDCG@3': 0.58478, 'NDCG@5': 0.60006, 'NDCG@10': 0.62646, 'NDCG@100': 0.66175, 'NDCG@1000': 0.67171}
@@ -49,6 +560,22 @@ recall: {'Recall@1': 0.49983, 'Recall@3': 0.62711, 'Recall@5': 0.66706, 'Recall@
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  precision: {'P@1': 0.52667, 'P@3': 0.23111, 'P@5': 0.15, 'P@10': 0.085, 'P@100': 0.0103, 'P@1000': 0.00111}
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  ```
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  # Example inference/benchmark code and how to use the model with Fastembed
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  After installing beir-qdrant make sure to upgrade fastembed.
 
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+ # Update
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+
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+ I've improved the quality of the model, but size increased from 571MiB to 624MiB.
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+
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+ There's now only a ~1% difference in retrieval performance between this model and the full f32 model.
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+
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+ This model is ~6% more accurate at retrieval than the onnx-community uint8 model with f32 output.
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+
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+ This model is somewhere around 3.5% more accurate at retrieval than the previous version of this model.
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+
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+ Inference speed was the same on my hardware vs. previous model (Ryzen CPU).
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+
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  # Qwen3-Embedding-0.6B-onnx-uint8
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15
  This is an onnx version of https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
 
21
  - Execute model without pooling and without normalization
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  - Pay attention to the example query format in the code below
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+ # Quantization method
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+
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+ I created a little onnx model instrumentation framework to assist in quantization. I generated calibration data, created an instrumented onnx model, and recorded the range of values for every tensor in the model during inference. I tested different criteria for excluding nodes until I settled on what I felt was a good size/accuracy tradeoff. I ended up excluding 484 of the most sensitive nodes from quantization.
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+
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+ After that I generated 1 million tokens of calibration data and recorded the range of float32 outputs seen during inference.
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+
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+ The range I found: -0.3009805381298065 to 0.3952634334564209
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+
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+ I used that range for an assymmetric linear quantization from float32 -> uint8.
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+
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+ <details>
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+ <summary>Here are the nodes I excluded</summary>
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+
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+ ```python
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+ ["/0/auto_model/ConstantOfShape",
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+ "/0/auto_model/Constant_28",
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+ "/0/auto_model/layers.25/post_attention_layernorm/Pow",
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+ "/0/auto_model/layers.26/input_layernorm/Pow",
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+ "/0/auto_model/layers.25/input_layernorm/Pow",
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+ "/0/auto_model/layers.27/self_attn/q_norm/Pow",
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+ "/0/auto_model/layers.25/self_attn/Slice_4",
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+ "/0/auto_model/Unsqueeze_4",
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+ "/0/auto_model/layers.10/self_attn/Slice_4",
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+ "/0/auto_model/layers.23/self_attn/Slice_4",
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+ "/0/auto_model/layers.17/self_attn/Slice_4",
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+ "/0/auto_model/Where_1",
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+ "/0/auto_model/layers.27/self_attn/Slice_4",
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+ "/0/auto_model/layers.20/self_attn/Slice_4",
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+ "/0/auto_model/Add",
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+ "/0/auto_model/Mul",
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+ "/0/auto_model/layers.7/self_attn/Slice_4",
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+ "/0/auto_model/layers.13/self_attn/Slice_4",
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521
+ "/0/auto_model/ScatterND"]
522
+ ```
523
+
524
+ </details>
525
+
526
  # Benchmarks
527
 
528
  I used beir-qdrant with the scifact dataset.
 
533
 
534
  If someone wants to sponsor me with an NVIDIA GPU I can have a much faster turnaround time with my model experiments and explore some different quantization strategies.
535
 
 
536
 
537
+ onnx f32 model with f32 output (baseline):
 
 
 
 
538
 
539
  ```
540
  ndcg: {'NDCG@1': 0.57, 'NDCG@3': 0.65655, 'NDCG@5': 0.68177, 'NDCG@10': 0.69999, 'NDCG@100': 0.72749, 'NDCG@1000': 0.73301}
 
542
  precision: {'P@1': 0.57, 'P@3': 0.26111, 'P@5': 0.17467, 'P@10': 0.09467, 'P@100': 0.01083, 'P@1000': 0.00113}
543
  ```
544
 
545
+ onnx dynamic uint8 model with f32 output (previous model's parent):
546
 
547
  ```
548
  ndcg: {'NDCG@1': 0.52333, 'NDCG@3': 0.58087, 'NDCG@5': 0.59811, 'NDCG@10': 0.6249, 'NDCG@100': 0.66025, 'NDCG@1000': 0.67023}
 
550
  precision: {'P@1': 0.52333, 'P@3': 0.22889, 'P@5': 0.15, 'P@10': 0.085, 'P@100': 0.0103, 'P@1000': 0.00111}
551
  ```
552
 
553
+ onnx dynamic uint8 model with uint8 output (previous model):
554
+
555
+ Note: This benchmarking better than it's parent is actually bad. I used more calibration data in the current version to avoid a repeat.
556
 
557
  ```
558
  ndcg: {'NDCG@1': 0.52667, 'NDCG@3': 0.58478, 'NDCG@5': 0.60006, 'NDCG@10': 0.62646, 'NDCG@100': 0.66175, 'NDCG@1000': 0.67171}
 
560
  precision: {'P@1': 0.52667, 'P@3': 0.23111, 'P@5': 0.15, 'P@10': 0.085, 'P@100': 0.0103, 'P@1000': 0.00111}
561
  ```
562
 
563
+ onnx dynamic uint8 model with f32 output (this model's parent):
564
+
565
+ ```
566
+ ndcg: {'NDCG@1': 0.56, 'NDCG@3': 0.63242, 'NDCG@5': 0.66258, 'NDCG@10': 0.68893, 'NDCG@100': 0.71276, 'NDCG@1000': 0.72}
567
+ recall: {'Recall@1': 0.53094, 'Recall@3': 0.68117, 'Recall@5': 0.75417, 'Recall@10': 0.83256, 'Recall@100': 0.94, 'Recall@1000': 0.99667}
568
+ precision: {'P@1': 0.56, 'P@3': 0.24778, 'P@5': 0.16867, 'P@10': 0.094, 'P@100': 0.0107, 'P@1000': 0.00113}
569
+ ```
570
+
571
+ onnx dynamic uint8 model with uint8 output (this model):
572
+
573
+ ```
574
+ ndcg: {'NDCG@1': 0.56, 'NDCG@3': 0.63119, 'NDCG@5': 0.66314, 'NDCG@10': 0.68867, 'NDCG@100': 0.71236, 'NDCG@1000': 0.7201}
575
+ recall: {'Recall@1': 0.53094, 'Recall@3': 0.67783, 'Recall@5': 0.75583, 'Recall@10': 0.83089, 'Recall@100': 0.93667, 'Recall@1000': 0.99667}
576
+ precision: {'P@1': 0.56, 'P@3': 0.24667, 'P@5': 0.16867, 'P@10': 0.094, 'P@100': 0.01067, 'P@1000': 0.00113}
577
+ ```
578
+
579
  # Example inference/benchmark code and how to use the model with Fastembed
580
 
581
  After installing beir-qdrant make sure to upgrade fastembed.
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