Update the model, which is now trained on 7,320,000 examples

onnx/decoder_model_merged.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cf616c0fd4c0e34d17de39dfddcdaa8b421e3da57ddb5674d56b0695df6095e2
+oid sha256:3f0b01da52fc4be5708e15f2f3acf85dda8acf47ebe129e6e70f613b2811ecee
 size 1000986613

onnx/decoder_model_merged_quantized.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:01c949896bba19f1d237ebfa13fa16084e230c4060c8242a410a35c5827a8a86
-size 257278179
+oid sha256:03cfaa0d5353ca7bf4ee10a36942db8aa18846872ebf82ada3212825dfa48ef0
+size 257278180

quantize_config.json

@@ -4,34 +4,34 @@
     "per_model_config": {
         "model": {
             "op_types": [
-                "ConstantOfShape",
-                "MatMul",
-                "Mul",
                 "Expand",
-                "Equal",
-                "Transpose",
-                "Gather",
-                "Sub",
                 "Concat",
-                "Slice",
-                "Squeeze",
-                "Range",
-                "Sigmoid",
-                "Unsqueeze",
-                "Constant",
+                "Reshape",
                 "Where",
+                "MatMul",
+                "Constant",
+                "ConstantOfShape",
                 "ReduceMean",
-                "Sqrt",
+                "Equal",
+                "Range",
+                "Sub",
+                "Trilu",
+                "Gather",
                 "Div",
                 "Shape",
-                "Pow",
-                "Add",
-                "Trilu",
+                "Sqrt",
                 "Less",
-                "Softmax",
                 "Cast",
-                "Reshape",
-                "Neg"
+                "Slice",
+                "Mul",
+                "Neg",
+                "Transpose",
+                "Softmax",
+                "Unsqueeze",
+                "Add",
+                "Pow",
+                "Squeeze",
+                "Sigmoid"
             ],
             "weight_type": "QInt8"
         }