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Delete custom

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  1. custom/custom_FFN_PF_lut4_chunk_01of01.mlpackage/Data/com.apple.CoreML/model.mlmodel +0 -3
  2. custom/custom_FFN_PF_lut4_chunk_01of01.mlpackage/Data/com.apple.CoreML/weights/weight.bin +0 -3
  3. custom/custom_FFN_PF_lut4_chunk_01of01.mlpackage/Manifest.json +0 -18
  4. custom/custom_embeddings.mlmodelc/coremldata.bin +0 -3
  5. custom/custom_embeddings.mlmodelc/model.mil +0 -16
  6. custom/custom_embeddings.mlmodelc/weights/weight.bin +0 -3
  7. custom/custom_lm_head_lut6.mlmodelc/coremldata.bin +0 -3
  8. custom/custom_lm_head_lut6.mlmodelc/model.mil +0 -186
  9. custom/custom_lm_head_lut6.mlmodelc/weights/weight.bin +0 -3
custom/custom_FFN_PF_lut4_chunk_01of01.mlpackage/Data/com.apple.CoreML/model.mlmodel DELETED
@@ -1,3 +0,0 @@
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- size 2432838
 
 
 
 
custom/custom_FFN_PF_lut4_chunk_01of01.mlpackage/Data/com.apple.CoreML/weights/weight.bin DELETED
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custom/custom_FFN_PF_lut4_chunk_01of01.mlpackage/Manifest.json DELETED
@@ -1,18 +0,0 @@
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- {
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- "fileFormatVersion": "1.0.0",
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- "itemInfoEntries": {
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- "8AF6FC31-EB32-469F-B437-3E7EC4A85979": {
5
- "author": "com.apple.CoreML",
6
- "description": "CoreML Model Weights",
7
- "name": "weights",
8
- "path": "com.apple.CoreML/weights"
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- },
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- "C5BD2776-CFCF-4969-879D-2770E575745B": {
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- "author": "com.apple.CoreML",
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- "description": "CoreML Model Specification",
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- "name": "model.mlmodel",
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- "path": "com.apple.CoreML/model.mlmodel"
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- }
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- },
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- "rootModelIdentifier": "C5BD2776-CFCF-4969-879D-2770E575745B"
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
custom/custom_embeddings.mlmodelc/coremldata.bin DELETED
@@ -1,3 +0,0 @@
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- size 550
 
 
 
 
custom/custom_embeddings.mlmodelc/model.mil DELETED
@@ -1,16 +0,0 @@
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- program(1.3)
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- [buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-torch", "2.7.1"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
3
- {
4
- func main<ios18>(tensor<int32, [1, 1]> input_ids) {
5
- int32 hidden_states_batch_dims_0 = const()[name = string("hidden_states_batch_dims_0"), val = int32(0)];
6
- bool hidden_states_validate_indices_0 = const()[name = string("hidden_states_validate_indices_0"), val = bool(false)];
7
- tensor<fp16, [120818, 2048]> embed_tokens_weight_to_fp16 = const()[name = string("embed_tokens_weight_to_fp16"), val = tensor<fp16, [120818, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
8
- int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
9
- tensor<bool, [1, 1]> greater_equal_0 = greater_equal(x = input_ids, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
10
- int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(120818)];
11
- tensor<int32, [1, 1]> add_0 = add(x = input_ids, y = slice_by_index_0)[name = string("add_0")];
12
- tensor<int32, [1, 1]> select_0 = select(a = input_ids, b = add_0, cond = greater_equal_0)[name = string("select_0")];
13
- int32 hidden_states_cast_fp16_axis_0 = const()[name = string("hidden_states_cast_fp16_axis_0"), val = int32(0)];
14
- tensor<fp16, [1, 1, 2048]> hidden_states = gather(axis = hidden_states_cast_fp16_axis_0, batch_dims = hidden_states_batch_dims_0, indices = select_0, validate_indices = hidden_states_validate_indices_0, x = embed_tokens_weight_to_fp16)[name = string("hidden_states_cast_fp16")];
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- } -> (hidden_states);
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- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
custom/custom_embeddings.mlmodelc/weights/weight.bin DELETED
@@ -1,3 +0,0 @@
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- version https://git-lfs.github.com/spec/v1
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- size 494870656
 
 
 
 
custom/custom_lm_head_lut6.mlmodelc/coremldata.bin DELETED
@@ -1,3 +0,0 @@
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- version https://git-lfs.github.com/spec/v1
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- oid sha256:e41d277f42c917c2c6a54ff4e044a7c666648d830daed3576fe63d878d7aed32
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- size 1106
 
 
 
 
custom/custom_lm_head_lut6.mlmodelc/model.mil DELETED
@@ -1,186 +0,0 @@
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- program(1.3)
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- [buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}})]
3
- {
4
- func main<ios18>(tensor<fp16, [1, 1, 2048]> hidden_states) {
5
- tensor<int32, [3]> var_5 = const()[name = string("op_5"), val = tensor<int32, [3]>([0, 2, 1])];
6
- tensor<int32, [1]> input_axes_0 = const()[name = string("input_axes_0"), val = tensor<int32, [1]>([2])];
7
- tensor<fp16, [1, 2048, 1]> var_6_cast_fp16 = transpose(perm = var_5, x = hidden_states)[name = string("transpose_16")];
8
- tensor<fp16, [1, 2048, 1, 1]> input_cast_fp16 = expand_dims(axes = input_axes_0, x = var_6_cast_fp16)[name = string("input_cast_fp16")];
9
- string var_29_pad_type_0 = const()[name = string("op_29_pad_type_0"), val = string("valid")];
10
- tensor<int32, [2]> var_29_strides_0 = const()[name = string("op_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
11
- tensor<int32, [4]> var_29_pad_0 = const()[name = string("op_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
12
- tensor<int32, [2]> var_29_dilations_0 = const()[name = string("op_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
13
- int32 var_29_groups_0 = const()[name = string("op_29_groups_0"), val = int32(1)];
14
- tensor<fp16, [7552, 2048, 1, 1]> op_9_promoted_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [7552, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor<fp16, [944, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11600000))))[name = string("op_9_promoted_to_fp16_palettized")];
15
- tensor<fp16, [1, 7552, 1, 1]> var_29_cast_fp16 = conv(dilations = var_29_dilations_0, groups = var_29_groups_0, pad = var_29_pad_0, pad_type = var_29_pad_type_0, strides = var_29_strides_0, weight = op_9_promoted_to_fp16_palettized, x = input_cast_fp16)[name = string("op_29_cast_fp16")];
16
- tensor<int32, [1]> var_31_axes_0 = const()[name = string("op_31_axes_0"), val = tensor<int32, [1]>([2])];
17
- tensor<fp16, [1, 7552, 1]> var_31_cast_fp16 = squeeze(axes = var_31_axes_0, x = var_29_cast_fp16)[name = string("op_31_cast_fp16")];
18
- tensor<int32, [3]> var_34_perm_0 = const()[name = string("op_34_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
19
- string var_55_pad_type_0 = const()[name = string("op_55_pad_type_0"), val = string("valid")];
20
- tensor<int32, [2]> var_55_strides_0 = const()[name = string("op_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
21
- tensor<int32, [4]> var_55_pad_0 = const()[name = string("op_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
22
- tensor<int32, [2]> var_55_dilations_0 = const()[name = string("op_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
23
- int32 var_55_groups_0 = const()[name = string("op_55_groups_0"), val = int32(1)];
24
- tensor<fp16, [7552, 2048, 1, 1]> op_35_promoted_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint6, [7552, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(11720896))), lut = tensor<fp16, [944, 1, 1, 1, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23320832))))[name = string("op_35_promoted_to_fp16_palettized")];
25
- tensor<fp16, [1, 7552, 1, 1]> var_55_cast_fp16 = conv(dilations = var_55_dilations_0, groups = var_55_groups_0, pad = var_55_pad_0, pad_type = var_55_pad_type_0, strides = var_55_strides_0, weight = op_35_promoted_to_fp16_palettized, x = input_cast_fp16)[name = string("op_55_cast_fp16")];
26
- tensor<int32, [1]> var_57_axes_0 = const()[name = string("op_57_axes_0"), val = tensor<int32, [1]>([2])];
27
- tensor<fp16, [1, 7552, 1]> var_57_cast_fp16 = squeeze(axes = var_57_axes_0, x = var_55_cast_fp16)[name = string("op_57_cast_fp16")];
28
- tensor<int32, [3]> var_60_perm_0 = const()[name = string("op_60_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
29
- string var_81_pad_type_0 = const()[name = string("op_81_pad_type_0"), val = string("valid")];
30
- tensor<int32, [2]> var_81_strides_0 = const()[name = string("op_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
31
- tensor<int32, [4]> var_81_pad_0 = const()[name = string("op_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
32
- tensor<int32, [2]> var_81_dilations_0 = const()[name = string("op_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
33
- int32 var_81_groups_0 = const()[name = string("op_81_groups_0"), val = int32(1)];
34
- tensor<fp16, [7551, 2048, 1, 1]> var_61_promoted_to_fp16 = const()[name = string("op_61_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23441728)))];
35
- tensor<fp16, [1, 7551, 1, 1]> var_81_cast_fp16 = conv(dilations = var_81_dilations_0, groups = var_81_groups_0, pad = var_81_pad_0, pad_type = var_81_pad_type_0, strides = var_81_strides_0, weight = var_61_promoted_to_fp16, x = input_cast_fp16)[name = string("op_81_cast_fp16")];
36
- tensor<int32, [1]> var_83_axes_0 = const()[name = string("op_83_axes_0"), val = tensor<int32, [1]>([2])];
37
- tensor<fp16, [1, 7551, 1]> var_83_cast_fp16 = squeeze(axes = var_83_axes_0, x = var_81_cast_fp16)[name = string("op_83_cast_fp16")];
38
- tensor<int32, [3]> var_86_perm_0 = const()[name = string("op_86_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
39
- string var_107_pad_type_0 = const()[name = string("op_107_pad_type_0"), val = string("valid")];
40
- tensor<int32, [2]> var_107_strides_0 = const()[name = string("op_107_strides_0"), val = tensor<int32, [2]>([1, 1])];
41
- tensor<int32, [4]> var_107_pad_0 = const()[name = string("op_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
42
- tensor<int32, [2]> var_107_dilations_0 = const()[name = string("op_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
43
- int32 var_107_groups_0 = const()[name = string("op_107_groups_0"), val = int32(1)];
44
- tensor<fp16, [7551, 2048, 1, 1]> var_87_promoted_to_fp16 = const()[name = string("op_87_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(54370688)))];
45
- tensor<fp16, [1, 7551, 1, 1]> var_107_cast_fp16 = conv(dilations = var_107_dilations_0, groups = var_107_groups_0, pad = var_107_pad_0, pad_type = var_107_pad_type_0, strides = var_107_strides_0, weight = var_87_promoted_to_fp16, x = input_cast_fp16)[name = string("op_107_cast_fp16")];
46
- tensor<int32, [1]> var_109_axes_0 = const()[name = string("op_109_axes_0"), val = tensor<int32, [1]>([2])];
47
- tensor<fp16, [1, 7551, 1]> var_109_cast_fp16 = squeeze(axes = var_109_axes_0, x = var_107_cast_fp16)[name = string("op_109_cast_fp16")];
48
- tensor<int32, [3]> var_112_perm_0 = const()[name = string("op_112_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
49
- string var_133_pad_type_0 = const()[name = string("op_133_pad_type_0"), val = string("valid")];
50
- tensor<int32, [2]> var_133_strides_0 = const()[name = string("op_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
51
- tensor<int32, [4]> var_133_pad_0 = const()[name = string("op_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
52
- tensor<int32, [2]> var_133_dilations_0 = const()[name = string("op_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
53
- int32 var_133_groups_0 = const()[name = string("op_133_groups_0"), val = int32(1)];
54
- tensor<fp16, [7551, 2048, 1, 1]> var_113_promoted_to_fp16 = const()[name = string("op_113_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(85299648)))];
55
- tensor<fp16, [1, 7551, 1, 1]> var_133_cast_fp16 = conv(dilations = var_133_dilations_0, groups = var_133_groups_0, pad = var_133_pad_0, pad_type = var_133_pad_type_0, strides = var_133_strides_0, weight = var_113_promoted_to_fp16, x = input_cast_fp16)[name = string("op_133_cast_fp16")];
56
- tensor<int32, [1]> var_135_axes_0 = const()[name = string("op_135_axes_0"), val = tensor<int32, [1]>([2])];
57
- tensor<fp16, [1, 7551, 1]> var_135_cast_fp16 = squeeze(axes = var_135_axes_0, x = var_133_cast_fp16)[name = string("op_135_cast_fp16")];
58
- tensor<int32, [3]> var_138_perm_0 = const()[name = string("op_138_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
59
- string var_159_pad_type_0 = const()[name = string("op_159_pad_type_0"), val = string("valid")];
60
- tensor<int32, [2]> var_159_strides_0 = const()[name = string("op_159_strides_0"), val = tensor<int32, [2]>([1, 1])];
61
- tensor<int32, [4]> var_159_pad_0 = const()[name = string("op_159_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
62
- tensor<int32, [2]> var_159_dilations_0 = const()[name = string("op_159_dilations_0"), val = tensor<int32, [2]>([1, 1])];
63
- int32 var_159_groups_0 = const()[name = string("op_159_groups_0"), val = int32(1)];
64
- tensor<fp16, [7551, 2048, 1, 1]> var_139_promoted_to_fp16 = const()[name = string("op_139_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116228608)))];
65
- tensor<fp16, [1, 7551, 1, 1]> var_159_cast_fp16 = conv(dilations = var_159_dilations_0, groups = var_159_groups_0, pad = var_159_pad_0, pad_type = var_159_pad_type_0, strides = var_159_strides_0, weight = var_139_promoted_to_fp16, x = input_cast_fp16)[name = string("op_159_cast_fp16")];
66
- tensor<int32, [1]> var_161_axes_0 = const()[name = string("op_161_axes_0"), val = tensor<int32, [1]>([2])];
67
- tensor<fp16, [1, 7551, 1]> var_161_cast_fp16 = squeeze(axes = var_161_axes_0, x = var_159_cast_fp16)[name = string("op_161_cast_fp16")];
68
- tensor<int32, [3]> var_164_perm_0 = const()[name = string("op_164_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
69
- string var_185_pad_type_0 = const()[name = string("op_185_pad_type_0"), val = string("valid")];
70
- tensor<int32, [2]> var_185_strides_0 = const()[name = string("op_185_strides_0"), val = tensor<int32, [2]>([1, 1])];
71
- tensor<int32, [4]> var_185_pad_0 = const()[name = string("op_185_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
72
- tensor<int32, [2]> var_185_dilations_0 = const()[name = string("op_185_dilations_0"), val = tensor<int32, [2]>([1, 1])];
73
- int32 var_185_groups_0 = const()[name = string("op_185_groups_0"), val = int32(1)];
74
- tensor<fp16, [7551, 2048, 1, 1]> var_165_promoted_to_fp16 = const()[name = string("op_165_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(147157568)))];
75
- tensor<fp16, [1, 7551, 1, 1]> var_185_cast_fp16 = conv(dilations = var_185_dilations_0, groups = var_185_groups_0, pad = var_185_pad_0, pad_type = var_185_pad_type_0, strides = var_185_strides_0, weight = var_165_promoted_to_fp16, x = input_cast_fp16)[name = string("op_185_cast_fp16")];
76
- tensor<int32, [1]> var_187_axes_0 = const()[name = string("op_187_axes_0"), val = tensor<int32, [1]>([2])];
77
- tensor<fp16, [1, 7551, 1]> var_187_cast_fp16 = squeeze(axes = var_187_axes_0, x = var_185_cast_fp16)[name = string("op_187_cast_fp16")];
78
- tensor<int32, [3]> var_190_perm_0 = const()[name = string("op_190_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
79
- string var_211_pad_type_0 = const()[name = string("op_211_pad_type_0"), val = string("valid")];
80
- tensor<int32, [2]> var_211_strides_0 = const()[name = string("op_211_strides_0"), val = tensor<int32, [2]>([1, 1])];
81
- tensor<int32, [4]> var_211_pad_0 = const()[name = string("op_211_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
82
- tensor<int32, [2]> var_211_dilations_0 = const()[name = string("op_211_dilations_0"), val = tensor<int32, [2]>([1, 1])];
83
- int32 var_211_groups_0 = const()[name = string("op_211_groups_0"), val = int32(1)];
84
- tensor<fp16, [7551, 2048, 1, 1]> var_191_promoted_to_fp16 = const()[name = string("op_191_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178086528)))];
85
- tensor<fp16, [1, 7551, 1, 1]> var_211_cast_fp16 = conv(dilations = var_211_dilations_0, groups = var_211_groups_0, pad = var_211_pad_0, pad_type = var_211_pad_type_0, strides = var_211_strides_0, weight = var_191_promoted_to_fp16, x = input_cast_fp16)[name = string("op_211_cast_fp16")];
86
- tensor<int32, [1]> var_213_axes_0 = const()[name = string("op_213_axes_0"), val = tensor<int32, [1]>([2])];
87
- tensor<fp16, [1, 7551, 1]> var_213_cast_fp16 = squeeze(axes = var_213_axes_0, x = var_211_cast_fp16)[name = string("op_213_cast_fp16")];
88
- tensor<int32, [3]> var_216_perm_0 = const()[name = string("op_216_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
89
- string var_237_pad_type_0 = const()[name = string("op_237_pad_type_0"), val = string("valid")];
90
- tensor<int32, [2]> var_237_strides_0 = const()[name = string("op_237_strides_0"), val = tensor<int32, [2]>([1, 1])];
91
- tensor<int32, [4]> var_237_pad_0 = const()[name = string("op_237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
92
- tensor<int32, [2]> var_237_dilations_0 = const()[name = string("op_237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
93
- int32 var_237_groups_0 = const()[name = string("op_237_groups_0"), val = int32(1)];
94
- tensor<fp16, [7551, 2048, 1, 1]> var_217_promoted_to_fp16 = const()[name = string("op_217_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(209015488)))];
95
- tensor<fp16, [1, 7551, 1, 1]> var_237_cast_fp16 = conv(dilations = var_237_dilations_0, groups = var_237_groups_0, pad = var_237_pad_0, pad_type = var_237_pad_type_0, strides = var_237_strides_0, weight = var_217_promoted_to_fp16, x = input_cast_fp16)[name = string("op_237_cast_fp16")];
96
- tensor<int32, [1]> var_239_axes_0 = const()[name = string("op_239_axes_0"), val = tensor<int32, [1]>([2])];
97
- tensor<fp16, [1, 7551, 1]> var_239_cast_fp16 = squeeze(axes = var_239_axes_0, x = var_237_cast_fp16)[name = string("op_239_cast_fp16")];
98
- tensor<int32, [3]> var_242_perm_0 = const()[name = string("op_242_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
99
- string var_263_pad_type_0 = const()[name = string("op_263_pad_type_0"), val = string("valid")];
100
- tensor<int32, [2]> var_263_strides_0 = const()[name = string("op_263_strides_0"), val = tensor<int32, [2]>([1, 1])];
101
- tensor<int32, [4]> var_263_pad_0 = const()[name = string("op_263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
102
- tensor<int32, [2]> var_263_dilations_0 = const()[name = string("op_263_dilations_0"), val = tensor<int32, [2]>([1, 1])];
103
- int32 var_263_groups_0 = const()[name = string("op_263_groups_0"), val = int32(1)];
104
- tensor<fp16, [7551, 2048, 1, 1]> var_243_promoted_to_fp16 = const()[name = string("op_243_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(239944448)))];
105
- tensor<fp16, [1, 7551, 1, 1]> var_263_cast_fp16 = conv(dilations = var_263_dilations_0, groups = var_263_groups_0, pad = var_263_pad_0, pad_type = var_263_pad_type_0, strides = var_263_strides_0, weight = var_243_promoted_to_fp16, x = input_cast_fp16)[name = string("op_263_cast_fp16")];
106
- tensor<int32, [1]> var_265_axes_0 = const()[name = string("op_265_axes_0"), val = tensor<int32, [1]>([2])];
107
- tensor<fp16, [1, 7551, 1]> var_265_cast_fp16 = squeeze(axes = var_265_axes_0, x = var_263_cast_fp16)[name = string("op_265_cast_fp16")];
108
- tensor<int32, [3]> var_268_perm_0 = const()[name = string("op_268_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
109
- string var_289_pad_type_0 = const()[name = string("op_289_pad_type_0"), val = string("valid")];
110
- tensor<int32, [2]> var_289_strides_0 = const()[name = string("op_289_strides_0"), val = tensor<int32, [2]>([1, 1])];
111
- tensor<int32, [4]> var_289_pad_0 = const()[name = string("op_289_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
112
- tensor<int32, [2]> var_289_dilations_0 = const()[name = string("op_289_dilations_0"), val = tensor<int32, [2]>([1, 1])];
113
- int32 var_289_groups_0 = const()[name = string("op_289_groups_0"), val = int32(1)];
114
- tensor<fp16, [7551, 2048, 1, 1]> var_269_promoted_to_fp16 = const()[name = string("op_269_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(270873408)))];
115
- tensor<fp16, [1, 7551, 1, 1]> var_289_cast_fp16 = conv(dilations = var_289_dilations_0, groups = var_289_groups_0, pad = var_289_pad_0, pad_type = var_289_pad_type_0, strides = var_289_strides_0, weight = var_269_promoted_to_fp16, x = input_cast_fp16)[name = string("op_289_cast_fp16")];
116
- tensor<int32, [1]> var_291_axes_0 = const()[name = string("op_291_axes_0"), val = tensor<int32, [1]>([2])];
117
- tensor<fp16, [1, 7551, 1]> var_291_cast_fp16 = squeeze(axes = var_291_axes_0, x = var_289_cast_fp16)[name = string("op_291_cast_fp16")];
118
- tensor<int32, [3]> var_294_perm_0 = const()[name = string("op_294_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
119
- string var_315_pad_type_0 = const()[name = string("op_315_pad_type_0"), val = string("valid")];
120
- tensor<int32, [2]> var_315_strides_0 = const()[name = string("op_315_strides_0"), val = tensor<int32, [2]>([1, 1])];
121
- tensor<int32, [4]> var_315_pad_0 = const()[name = string("op_315_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
122
- tensor<int32, [2]> var_315_dilations_0 = const()[name = string("op_315_dilations_0"), val = tensor<int32, [2]>([1, 1])];
123
- int32 var_315_groups_0 = const()[name = string("op_315_groups_0"), val = int32(1)];
124
- tensor<fp16, [7551, 2048, 1, 1]> var_295_promoted_to_fp16 = const()[name = string("op_295_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301802368)))];
125
- tensor<fp16, [1, 7551, 1, 1]> var_315_cast_fp16 = conv(dilations = var_315_dilations_0, groups = var_315_groups_0, pad = var_315_pad_0, pad_type = var_315_pad_type_0, strides = var_315_strides_0, weight = var_295_promoted_to_fp16, x = input_cast_fp16)[name = string("op_315_cast_fp16")];
126
- tensor<int32, [1]> var_317_axes_0 = const()[name = string("op_317_axes_0"), val = tensor<int32, [1]>([2])];
127
- tensor<fp16, [1, 7551, 1]> var_317_cast_fp16 = squeeze(axes = var_317_axes_0, x = var_315_cast_fp16)[name = string("op_317_cast_fp16")];
128
- tensor<int32, [3]> var_320_perm_0 = const()[name = string("op_320_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
129
- string var_341_pad_type_0 = const()[name = string("op_341_pad_type_0"), val = string("valid")];
130
- tensor<int32, [2]> var_341_strides_0 = const()[name = string("op_341_strides_0"), val = tensor<int32, [2]>([1, 1])];
131
- tensor<int32, [4]> var_341_pad_0 = const()[name = string("op_341_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
132
- tensor<int32, [2]> var_341_dilations_0 = const()[name = string("op_341_dilations_0"), val = tensor<int32, [2]>([1, 1])];
133
- int32 var_341_groups_0 = const()[name = string("op_341_groups_0"), val = int32(1)];
134
- tensor<fp16, [7551, 2048, 1, 1]> var_321_promoted_to_fp16 = const()[name = string("op_321_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(332731328)))];
135
- tensor<fp16, [1, 7551, 1, 1]> var_341_cast_fp16 = conv(dilations = var_341_dilations_0, groups = var_341_groups_0, pad = var_341_pad_0, pad_type = var_341_pad_type_0, strides = var_341_strides_0, weight = var_321_promoted_to_fp16, x = input_cast_fp16)[name = string("op_341_cast_fp16")];
136
- tensor<int32, [1]> var_343_axes_0 = const()[name = string("op_343_axes_0"), val = tensor<int32, [1]>([2])];
137
- tensor<fp16, [1, 7551, 1]> var_343_cast_fp16 = squeeze(axes = var_343_axes_0, x = var_341_cast_fp16)[name = string("op_343_cast_fp16")];
138
- tensor<int32, [3]> var_346_perm_0 = const()[name = string("op_346_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
139
- string var_367_pad_type_0 = const()[name = string("op_367_pad_type_0"), val = string("valid")];
140
- tensor<int32, [2]> var_367_strides_0 = const()[name = string("op_367_strides_0"), val = tensor<int32, [2]>([1, 1])];
141
- tensor<int32, [4]> var_367_pad_0 = const()[name = string("op_367_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
142
- tensor<int32, [2]> var_367_dilations_0 = const()[name = string("op_367_dilations_0"), val = tensor<int32, [2]>([1, 1])];
143
- int32 var_367_groups_0 = const()[name = string("op_367_groups_0"), val = int32(1)];
144
- tensor<fp16, [7551, 2048, 1, 1]> var_347_promoted_to_fp16 = const()[name = string("op_347_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(363660288)))];
145
- tensor<fp16, [1, 7551, 1, 1]> var_367_cast_fp16 = conv(dilations = var_367_dilations_0, groups = var_367_groups_0, pad = var_367_pad_0, pad_type = var_367_pad_type_0, strides = var_367_strides_0, weight = var_347_promoted_to_fp16, x = input_cast_fp16)[name = string("op_367_cast_fp16")];
146
- tensor<int32, [1]> var_369_axes_0 = const()[name = string("op_369_axes_0"), val = tensor<int32, [1]>([2])];
147
- tensor<fp16, [1, 7551, 1]> var_369_cast_fp16 = squeeze(axes = var_369_axes_0, x = var_367_cast_fp16)[name = string("op_369_cast_fp16")];
148
- tensor<int32, [3]> var_372_perm_0 = const()[name = string("op_372_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
149
- string var_393_pad_type_0 = const()[name = string("op_393_pad_type_0"), val = string("valid")];
150
- tensor<int32, [2]> var_393_strides_0 = const()[name = string("op_393_strides_0"), val = tensor<int32, [2]>([1, 1])];
151
- tensor<int32, [4]> var_393_pad_0 = const()[name = string("op_393_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
152
- tensor<int32, [2]> var_393_dilations_0 = const()[name = string("op_393_dilations_0"), val = tensor<int32, [2]>([1, 1])];
153
- int32 var_393_groups_0 = const()[name = string("op_393_groups_0"), val = int32(1)];
154
- tensor<fp16, [7551, 2048, 1, 1]> var_373_promoted_to_fp16 = const()[name = string("op_373_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(394589248)))];
155
- tensor<fp16, [1, 7551, 1, 1]> var_393_cast_fp16 = conv(dilations = var_393_dilations_0, groups = var_393_groups_0, pad = var_393_pad_0, pad_type = var_393_pad_type_0, strides = var_393_strides_0, weight = var_373_promoted_to_fp16, x = input_cast_fp16)[name = string("op_393_cast_fp16")];
156
- tensor<int32, [1]> var_395_axes_0 = const()[name = string("op_395_axes_0"), val = tensor<int32, [1]>([2])];
157
- tensor<fp16, [1, 7551, 1]> var_395_cast_fp16 = squeeze(axes = var_395_axes_0, x = var_393_cast_fp16)[name = string("op_395_cast_fp16")];
158
- tensor<int32, [3]> var_398_perm_0 = const()[name = string("op_398_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
159
- string var_419_pad_type_0 = const()[name = string("op_419_pad_type_0"), val = string("valid")];
160
- tensor<int32, [2]> var_419_strides_0 = const()[name = string("op_419_strides_0"), val = tensor<int32, [2]>([1, 1])];
161
- tensor<int32, [4]> var_419_pad_0 = const()[name = string("op_419_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
162
- tensor<int32, [2]> var_419_dilations_0 = const()[name = string("op_419_dilations_0"), val = tensor<int32, [2]>([1, 1])];
163
- int32 var_419_groups_0 = const()[name = string("op_419_groups_0"), val = int32(1)];
164
- tensor<fp16, [7551, 2048, 1, 1]> var_399_promoted_to_fp16 = const()[name = string("op_399_promoted_to_fp16"), val = tensor<fp16, [7551, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(425518208)))];
165
- tensor<fp16, [1, 7551, 1, 1]> var_419_cast_fp16 = conv(dilations = var_419_dilations_0, groups = var_419_groups_0, pad = var_419_pad_0, pad_type = var_419_pad_type_0, strides = var_419_strides_0, weight = var_399_promoted_to_fp16, x = input_cast_fp16)[name = string("op_419_cast_fp16")];
166
- tensor<int32, [1]> var_421_axes_0 = const()[name = string("op_421_axes_0"), val = tensor<int32, [1]>([2])];
167
- tensor<fp16, [1, 7551, 1]> var_421_cast_fp16 = squeeze(axes = var_421_axes_0, x = var_419_cast_fp16)[name = string("op_421_cast_fp16")];
168
- tensor<int32, [3]> var_424_perm_0 = const()[name = string("op_424_perm_0"), val = tensor<int32, [3]>([0, 2, 1])];
169
- tensor<fp16, [1, 1, 7552]> logits1 = transpose(perm = var_34_perm_0, x = var_31_cast_fp16)[name = string("transpose_0")];
170
- tensor<fp16, [1, 1, 7552]> logits2 = transpose(perm = var_60_perm_0, x = var_57_cast_fp16)[name = string("transpose_1")];
171
- tensor<fp16, [1, 1, 7551]> logits3 = transpose(perm = var_86_perm_0, x = var_83_cast_fp16)[name = string("transpose_2")];
172
- tensor<fp16, [1, 1, 7551]> logits4 = transpose(perm = var_112_perm_0, x = var_109_cast_fp16)[name = string("transpose_3")];
173
- tensor<fp16, [1, 1, 7551]> logits5 = transpose(perm = var_138_perm_0, x = var_135_cast_fp16)[name = string("transpose_4")];
174
- tensor<fp16, [1, 1, 7551]> logits6 = transpose(perm = var_164_perm_0, x = var_161_cast_fp16)[name = string("transpose_5")];
175
- tensor<fp16, [1, 1, 7551]> logits7 = transpose(perm = var_190_perm_0, x = var_187_cast_fp16)[name = string("transpose_6")];
176
- tensor<fp16, [1, 1, 7551]> logits8 = transpose(perm = var_216_perm_0, x = var_213_cast_fp16)[name = string("transpose_7")];
177
- tensor<fp16, [1, 1, 7551]> logits9 = transpose(perm = var_242_perm_0, x = var_239_cast_fp16)[name = string("transpose_8")];
178
- tensor<fp16, [1, 1, 7551]> logits10 = transpose(perm = var_268_perm_0, x = var_265_cast_fp16)[name = string("transpose_9")];
179
- tensor<fp16, [1, 1, 7551]> logits11 = transpose(perm = var_294_perm_0, x = var_291_cast_fp16)[name = string("transpose_10")];
180
- tensor<fp16, [1, 1, 7551]> logits12 = transpose(perm = var_320_perm_0, x = var_317_cast_fp16)[name = string("transpose_11")];
181
- tensor<fp16, [1, 1, 7551]> logits13 = transpose(perm = var_346_perm_0, x = var_343_cast_fp16)[name = string("transpose_12")];
182
- tensor<fp16, [1, 1, 7551]> logits14 = transpose(perm = var_372_perm_0, x = var_369_cast_fp16)[name = string("transpose_13")];
183
- tensor<fp16, [1, 1, 7551]> logits15 = transpose(perm = var_398_perm_0, x = var_395_cast_fp16)[name = string("transpose_14")];
184
- tensor<fp16, [1, 1, 7551]> logits16 = transpose(perm = var_424_perm_0, x = var_421_cast_fp16)[name = string("transpose_15")];
185
- } -> (logits1, logits2, logits3, logits4, logits5, logits6, logits7, logits8, logits9, logits10, logits11, logits12, logits13, logits14, logits15, logits16);
186
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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