Delete nvidia_parakeet-ja_483MB/MultimodalLogits.mlmodelc

nvidia_parakeet-ja/MultimodalLogits.mlmodelc/analytics/coremldata.bin

@@ -1,3 +1,3 @@
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-oid sha256:221a0b445bb9285847cf226f9f4b92f7937df26420258c097f92c76a30a9e6dd
+oid sha256:219c0e0c3a4cd73e660421d17e617af17184f2e319e03d6d6701bf1c50fc1e29
 size 243

nvidia_parakeet-ja/MultimodalLogits.mlmodelc/coremldata.bin

@@ -1,3 +1,3 @@
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-oid sha256:9915e1c06ebb6405326788975f23c59c527ca0b35d2615d51c38139058d409ca
-size 458
+oid sha256:7443a41887a91d5104f9c9f44774b7d5498b451eeac2724a8fae271498c4cd9e
+size 431

nvidia_parakeet-ja/MultimodalLogits.mlmodelc/metadata.json

@@ -3,16 +3,6 @@
     "metadataOutputVersion" : "3.0",
     "storagePrecision" : "Float16",
     "outputSchema" : [
-      {
-        "hasShapeFlexibility" : "0",
-        "isOptional" : "0",
-        "dataType" : "Float16",
-        "formattedType" : "MultiArray (Float16 1 × 3078)",
-        "shortDescription" : "",
-        "shape" : "[1, 3078]",
-        "name" : "raw_logits",
-        "type" : "MultiArray"
-      },
       {
         "hasShapeFlexibility" : "0",
         "isOptional" : "0",
@@ -29,13 +19,11 @@
     ],
     "specificationVersion" : 8,
     "mlProgramOperationTypeHistogram" : {
-      "Ios16.softmax" : 1,
-      "Ios17.log" : 1,
-      "Ios17.linear" : 1,
       "Ios17.add" : 1,
-      "Ios16.relu" : 1
+      "Ios16.relu" : 1,
+      "Ios17.linear" : 1
     },
-    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "computePrecision" : "Float16",
     "isUpdatable" : "0",
     "stateSchema" : [
 

nvidia_parakeet-ja/MultimodalLogits.mlmodelc/model.mil

@@ -3,13 +3,9 @@ program(1.0)
 {
     func main<ios17>(tensor<fp16, [1, 640]> decoder_output_projected, tensor<fp16, [1, 640]> encoder_output_projected) {
             tensor<fp16, [1, 640]> input_1_cast_fp16 = add(x = decoder_output_projected, y = encoder_output_projected)[name = tensor<string, []>("input_1_cast_fp16")];
-            tensor<fp16, [1, 640]> input_3_cast_fp16 = relu(x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<fp16, [1, 640]> input_cast_fp16 = relu(x = input_1_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
             tensor<fp16, [3078, 640]> joint_net_1_weight_to_fp16 = const()[name = tensor<string, []>("joint_net_1_weight_to_fp16"), val = tensor<fp16, [3078, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
             tensor<fp16, [3078]> joint_net_1_bias_to_fp16 = const()[name = tensor<string, []>("joint_net_1_bias_to_fp16"), val = tensor<fp16, [3078]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3939968)))];
-            tensor<fp16, [1, 3078]> raw_logits = linear(bias = joint_net_1_bias_to_fp16, weight = joint_net_1_weight_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
-            tensor<int32, []> var_11 = const()[name = tensor<string, []>("op_11"), val = tensor<int32, []>(-1)];
-            tensor<fp16, [1, 3078]> var_13_softmax_cast_fp16 = softmax(axis = var_11, x = raw_logits)[name = tensor<string, []>("op_13_softmax_cast_fp16")];
-            tensor<fp32, []> var_13_epsilon_0 = const()[name = tensor<string, []>("op_13_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
-            tensor<fp16, [1, 3078]> logits = log(epsilon = var_13_epsilon_0, x = var_13_softmax_cast_fp16)[name = tensor<string, []>("op_13_cast_fp16")];
-        } -> (raw_logits, logits);
+            tensor<fp16, [1, 3078]> logits = linear(bias = joint_net_1_bias_to_fp16, weight = joint_net_1_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
+        } -> (logits);
 }

nvidia_parakeet-ja_483MB/MultimodalLogits.mlmodelc/analytics/coremldata.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:221a0b445bb9285847cf226f9f4b92f7937df26420258c097f92c76a30a9e6dd
+oid sha256:219c0e0c3a4cd73e660421d17e617af17184f2e319e03d6d6701bf1c50fc1e29
 size 243

nvidia_parakeet-ja_483MB/MultimodalLogits.mlmodelc/coremldata.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9915e1c06ebb6405326788975f23c59c527ca0b35d2615d51c38139058d409ca
-size 458
+oid sha256:7443a41887a91d5104f9c9f44774b7d5498b451eeac2724a8fae271498c4cd9e
+size 431

nvidia_parakeet-ja_483MB/MultimodalLogits.mlmodelc/metadata.json

@@ -3,16 +3,6 @@
     "metadataOutputVersion" : "3.0",
     "storagePrecision" : "Float16",
     "outputSchema" : [
-      {
-        "hasShapeFlexibility" : "0",
-        "isOptional" : "0",
-        "dataType" : "Float16",
-        "formattedType" : "MultiArray (Float16 1 × 3078)",
-        "shortDescription" : "",
-        "shape" : "[1, 3078]",
-        "name" : "raw_logits",
-        "type" : "MultiArray"
-      },
       {
         "hasShapeFlexibility" : "0",
         "isOptional" : "0",
@@ -29,13 +19,11 @@
     ],
     "specificationVersion" : 8,
     "mlProgramOperationTypeHistogram" : {
-      "Ios16.softmax" : 1,
-      "Ios17.log" : 1,
-      "Ios17.linear" : 1,
       "Ios17.add" : 1,
-      "Ios16.relu" : 1
+      "Ios16.relu" : 1,
+      "Ios17.linear" : 1
     },
-    "computePrecision" : "Mixed (Float16, Float32, Int32)",
+    "computePrecision" : "Float16",
     "isUpdatable" : "0",
     "stateSchema" : [
 

nvidia_parakeet-ja_483MB/MultimodalLogits.mlmodelc/model.mil

@@ -3,13 +3,9 @@ program(1.0)
 {
     func main<ios17>(tensor<fp16, [1, 640]> decoder_output_projected, tensor<fp16, [1, 640]> encoder_output_projected) {
             tensor<fp16, [1, 640]> input_1_cast_fp16 = add(x = decoder_output_projected, y = encoder_output_projected)[name = tensor<string, []>("input_1_cast_fp16")];
-            tensor<fp16, [1, 640]> input_3_cast_fp16 = relu(x = input_1_cast_fp16)[name = tensor<string, []>("input_3_cast_fp16")];
+            tensor<fp16, [1, 640]> input_cast_fp16 = relu(x = input_1_cast_fp16)[name = tensor<string, []>("input_cast_fp16")];
             tensor<fp16, [3078, 640]> joint_net_1_weight_to_fp16 = const()[name = tensor<string, []>("joint_net_1_weight_to_fp16"), val = tensor<fp16, [3078, 640]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
             tensor<fp16, [3078]> joint_net_1_bias_to_fp16 = const()[name = tensor<string, []>("joint_net_1_bias_to_fp16"), val = tensor<fp16, [3078]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3939968)))];
-            tensor<fp16, [1, 3078]> raw_logits = linear(bias = joint_net_1_bias_to_fp16, weight = joint_net_1_weight_to_fp16, x = input_3_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
-            tensor<int32, []> var_11 = const()[name = tensor<string, []>("op_11"), val = tensor<int32, []>(-1)];
-            tensor<fp16, [1, 3078]> var_13_softmax_cast_fp16 = softmax(axis = var_11, x = raw_logits)[name = tensor<string, []>("op_13_softmax_cast_fp16")];
-            tensor<fp32, []> var_13_epsilon_0 = const()[name = tensor<string, []>("op_13_epsilon_0"), val = tensor<fp32, []>(0x1p-149)];
-            tensor<fp16, [1, 3078]> logits = log(epsilon = var_13_epsilon_0, x = var_13_softmax_cast_fp16)[name = tensor<string, []>("op_13_cast_fp16")];
-        } -> (raw_logits, logits);
+            tensor<fp16, [1, 3078]> logits = linear(bias = joint_net_1_bias_to_fp16, weight = joint_net_1_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
+        } -> (logits);
 }