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1 Parent(s): c54339a

v4: two-lane decoder (two pieces per infer call, grouped-query attention)

Browse files
README.md CHANGED
@@ -28,9 +28,11 @@ Neural Engine holds the weights dequantised to fp16, about 4.5 GB, outside the p
28
  Converted with the pipeline in the VoiceInk fork's `tools/r2t2-coreml`: the encoder through
29
  coremltools 9 with a fixed 800-frame window (the fused `gelu` replaced by a tanh formulation, whose
30
  constant absolute error otherwise swamps this encoder's small activations), the decoder through
31
- [ANEMLL](https://github.com/Anemll/Anemll) as two stateful chunks with a 128-row `prefill` and a
32
- 1-row `infer` function, and a head with a `verify` function that returns the argmax of 128 rows
33
- in one call. Requires macOS 15 or later (Core ML stateful models) on Apple silicon.
 
 
34
 
35
  Word error rate, whole utterances, 100-utterance subsets: LibriSpeech test-clean 2.40 %, FLEURS
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  pt_br 3.50 %. Against the unconverted bfloat16 weights on MLX, paired on 300 utterances of each:
@@ -41,9 +43,9 @@ pt_br 3.50 %. Against the unconverted bfloat16 weights on MLX, paired on 300 utt
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  | file | | size |
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  |---|---|---|
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  | `R2T2AudioEncoder.mlmodelc` | encoder, fp16, `[1, 128, 800]` mel window + key mask → `[1, 104, 2048]` | 607 MB |
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- | `r2t2_FFN_PF_lut8_chunk_01of02.mlmodelc` | decoder layers 0–13, functions `prefill` (128 rows) and `infer` (1 row) | 692 MB |
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  | `r2t2_FFN_PF_lut8_chunk_02of02.mlmodelc` | decoder layers 14–27 and the final norm, same functions | 692 MB |
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- | `r2t2_lm_head_lut8.mlmodelc` | head, 16-way split: `infer` (one row → `logits1…16`), `verify` (128 rows → argmaxes) | 306 MB |
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  | `embed_tokens.f16.bin` | token embeddings, 151 936 × 2048 fp16, row-major, no header | 622 MB |
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  | `tokenizer.json`, `tokenizer_config.json` | the original tokenizer | 11 MB |
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  | `MODEL_LICENSE`, `LICENSE-Qwen3-ASR.txt`, `NOTICE`, `SHA256SUMS` | | |
@@ -55,17 +57,25 @@ after the licence is accepted. The runtime re-decodes the whole current piece of
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  on every pass, so the live text converges on the same result as an offline decode; what makes that
56
  cheap is keeping the prompt prefix and every completed encoder window in the decoder's KV state,
57
  prefilling only the new rows plus the previous pass's tokens as a draft, confirming the draft with
58
- one `verify` call and decoding only from the first divergence. Passes take 150–250 ms on an M5 Pro;
59
- a 30 s piece decodes offline in about 2.5 s.
 
60
 
61
  For another runtime, the interface:
62
 
63
- - Decoder inputs `hidden_states [1, B, 2048]` fp16, `position_ids [B]` int32,
64
- `causal_mask [1, 1, B, 1024]` fp16 (0 to attend, −10 000 otherwise), `current_pos [1]` int32;
65
- output `output_hidden_states [1, B, 2048]`. Both chunks share one `MLState` of shape
66
- `(56, 8, 1024, 128)` fp16, written by absolute position, so a caller can rewind and overwrite.
67
- - Head `verify` output per row and slice: `argmax_val`, `argmax_hi`, `argmax_lo` (`[128, 16]` fp16),
68
- the slice's best logit and its index as `hi × 64 + lo`.
 
 
 
 
 
 
 
69
  - Prompt: the Qwen3-ASR chat template, with the encoder rows in place of the `<|audio_pad|>`
70
  embeddings and, to force a language, the assistant turn opened with `language <Name><asr_text>`.
71
  The model's `|` marks where it stops trusting its own output.
 
28
  Converted with the pipeline in the VoiceInk fork's `tools/r2t2-coreml`: the encoder through
29
  coremltools 9 with a fixed 800-frame window (the fused `gelu` replaced by a tanh formulation, whose
30
  constant absolute error otherwise swamps this encoder's small activations), the decoder through
31
+ [ANEMLL](https://github.com/Anemll/Anemll) as two stateful chunks whose KV state holds two
32
+ sequences side by side ("lanes"), with a 128-row `prefill` of one lane and an `infer` that
33
+ decodes one token of each lane in a single pass over the weights, and a head with a two-row
34
+ `infer` and a `verify` function that returns the argmax of 128 rows in one call. Requires macOS 15
35
+ or later (Core ML stateful models) on Apple silicon.
36
 
37
  Word error rate, whole utterances, 100-utterance subsets: LibriSpeech test-clean 2.40 %, FLEURS
38
  pt_br 3.50 %. Against the unconverted bfloat16 weights on MLX, paired on 300 utterances of each:
 
43
  | file | | size |
44
  |---|---|---|
45
  | `R2T2AudioEncoder.mlmodelc` | encoder, fp16, `[1, 128, 800]` mel window + key mask → `[1, 104, 2048]` | 607 MB |
46
+ | `r2t2_FFN_PF_lut8_chunk_01of02.mlmodelc` | decoder layers 0–13, functions `prefill` (128 rows of one lane) and `infer` (one row per lane) | 692 MB |
47
  | `r2t2_FFN_PF_lut8_chunk_02of02.mlmodelc` | decoder layers 14–27 and the final norm, same functions | 692 MB |
48
+ | `r2t2_lm_head_lut8.mlmodelc` | head, 16-way split: `infer` (two rows → `logits1…16`), `verify` (128 rows → argmaxes) | 306 MB |
49
  | `embed_tokens.f16.bin` | token embeddings, 151 936 × 2048 fp16, row-major, no header | 622 MB |
50
  | `tokenizer.json`, `tokenizer_config.json` | the original tokenizer | 11 MB |
51
  | `MODEL_LICENSE`, `LICENSE-Qwen3-ASR.txt`, `NOTICE`, `SHA256SUMS` | | |
 
57
  on every pass, so the live text converges on the same result as an offline decode; what makes that
58
  cheap is keeping the prompt prefix and every completed encoder window in the decoder's KV state,
59
  prefilling only the new rows plus the previous pass's tokens as a draft, confirming the draft with
60
+ one `verify` call and decoding only from the first divergence. Passes take 150–250 ms on an M5 Pro.
61
+ Offline, two 30 s pieces decode together, one token of each per `infer` call: a 120 s recording
62
+ takes about 6 s (8.7 ms per token; 17 ms for a single piece).
63
 
64
  For another runtime, the interface:
65
 
66
+ - Both chunks share one `MLState` of shape `(56, 8, 2048, 128)` fp16: lane *l* owns cache positions
67
+ `l·1024 ..< (l+1)·1024`, written by absolute position, so a caller can rewind and overwrite. The
68
+ causal mask is 0 to attend, −10 000 otherwise; output `output_hidden_states [1, B, 2048]`.
69
+ - `prefill`: `hidden_states [1, 128, 2048]` fp16, `position_ids [128]` int32 (the lane's own
70
+ positions), `causal_mask [1, 1, 128, 1024]`, `current_pos [1]` int32 (absolute: `l·1024 + p`),
71
+ `lane_weights [2, 1, 1]` fp16 one-hot selecting the lane.
72
+ - `infer`: `hidden_states [1, 2, 2048]` (one row per lane), `position_ids [2]`,
73
+ `causal_mask [1, 1, 4, 1024]` (each lane's row twice: the two query heads of a K/V head share a
74
+ matmul row axis), `current_pos [1]` and `current_pos_b [1]` (lane 0 and lane 1, absolute). An
75
+ idle lane takes a zero row, a fully masked row and a scratch position its next real row overwrites.
76
+ - Head `infer` takes `hidden_states [1, 2, 2048]` and returns `logits1…16` for both rows; `verify`
77
+ takes 128 rows and returns per row and slice `argmax_val`, `argmax_hi`, `argmax_lo` (`[128, 16]`
78
+ fp16), the slice's best logit and its index as `hi × 64 + lo`.
79
  - Prompt: the Qwen3-ASR chat template, with the encoder rows in place of the `<|audio_pad|>`
80
  embeddings and, to force a language, the assistant turn opened with `language <Name><asr_text>`.
81
  The model's `|` marks where it stops trusting its own output.
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- "formattedType" : "MultiArray (Float16 1 × 1 × 9496)",
328
  "shortDescription" : "",
329
- "shape" : "[1, 1, 9496]",
330
  "name" : "logits11",
331
  "type" : "MultiArray"
332
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@@ -334,9 +334,9 @@
334
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335
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336
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337
- "formattedType" : "MultiArray (Float16 1 × 1 × 9496)",
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  "name" : "logits12",
341
  "type" : "MultiArray"
342
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@@ -344,9 +344,9 @@
344
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345
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346
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- "formattedType" : "MultiArray (Float16 1 × 1 × 9496)",
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349
- "shape" : "[1, 1, 9496]",
350
  "name" : "logits13",
351
  "type" : "MultiArray"
352
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@@ -354,9 +354,9 @@
354
  "hasShapeFlexibility" : "0",
355
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356
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357
- "formattedType" : "MultiArray (Float16 1 × 1 × 9496)",
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  "shortDescription" : "",
359
- "shape" : "[1, 1, 9496]",
360
  "name" : "logits14",
361
  "type" : "MultiArray"
362
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@@ -364,9 +364,9 @@
364
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365
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366
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- "formattedType" : "MultiArray (Float16 1 × 1 × 9496)",
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  "shortDescription" : "",
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- "shape" : "[1, 1, 9496]",
370
  "name" : "logits15",
371
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372
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@@ -374,9 +374,9 @@
374
  "hasShapeFlexibility" : "0",
375
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376
  "dataType" : "Float16",
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- "formattedType" : "MultiArray (Float16 1 × 1 × 9496)",
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  "shortDescription" : "",
379
- "shape" : "[1, 1, 9496]",
380
  "name" : "logits16",
381
  "type" : "MultiArray"
382
  }
 
3
  "metadataOutputVersion" : "3.0",
4
  "userDefinedMetadata" : {
5
  "com.github.apple.coremltools.version" : "9.0",
6
+ "com.github.apple.coremltools.source_dialect" : "TorchScript",
7
  "com.github.apple.coremltools.source" : "torch==2.8.0",
8
  "com.anemll.context_length" : "1024",
 
9
  "com.anemll.lm_head_chunk_sizes" : "9496,9496,9496,9496,9496,9496,9496,9496,9496,9496,9496,9496,9496,9496,9496,9496",
10
  "com.anemll.vocab_size" : "151936",
11
+ "com.anemll.batch_size" : "2",
12
  "com.anemll.info" : "Converted with Anemll v0.1.1",
13
  "com.anemll.argmax_in_model" : "true",
14
  "com.anemll.lut_bits" : "8"
 
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  "isOptional" : "0",
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  "shortDescription" : "",
31
+ "shape" : "[1, 2, 2048]",
32
  "name" : "hidden_states",
33
  "type" : "MultiArray"
34
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39
  "isOptional" : "0",
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41
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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43
+ "shape" : "[1, 2, 9496]",
44
  "name" : "logits1",
45
  "type" : "MultiArray"
46
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48
  "hasShapeFlexibility" : "0",
49
  "isOptional" : "0",
50
  "dataType" : "Float16",
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+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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  "shortDescription" : "",
53
+ "shape" : "[1, 2, 9496]",
54
  "name" : "logits2",
55
  "type" : "MultiArray"
56
  },
 
58
  "hasShapeFlexibility" : "0",
59
  "isOptional" : "0",
60
  "dataType" : "Float16",
61
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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  "shortDescription" : "",
63
+ "shape" : "[1, 2, 9496]",
64
  "name" : "logits3",
65
  "type" : "MultiArray"
66
  },
 
68
  "hasShapeFlexibility" : "0",
69
  "isOptional" : "0",
70
  "dataType" : "Float16",
71
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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  "shortDescription" : "",
73
+ "shape" : "[1, 2, 9496]",
74
  "name" : "logits4",
75
  "type" : "MultiArray"
76
  },
 
78
  "hasShapeFlexibility" : "0",
79
  "isOptional" : "0",
80
  "dataType" : "Float16",
81
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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  "shortDescription" : "",
83
+ "shape" : "[1, 2, 9496]",
84
  "name" : "logits5",
85
  "type" : "MultiArray"
86
  },
 
88
  "hasShapeFlexibility" : "0",
89
  "isOptional" : "0",
90
  "dataType" : "Float16",
91
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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  "shortDescription" : "",
93
+ "shape" : "[1, 2, 9496]",
94
  "name" : "logits6",
95
  "type" : "MultiArray"
96
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98
  "hasShapeFlexibility" : "0",
99
  "isOptional" : "0",
100
  "dataType" : "Float16",
101
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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  "shortDescription" : "",
103
+ "shape" : "[1, 2, 9496]",
104
  "name" : "logits7",
105
  "type" : "MultiArray"
106
  },
 
108
  "hasShapeFlexibility" : "0",
109
  "isOptional" : "0",
110
  "dataType" : "Float16",
111
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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  "shortDescription" : "",
113
+ "shape" : "[1, 2, 9496]",
114
  "name" : "logits8",
115
  "type" : "MultiArray"
116
  },
 
118
  "hasShapeFlexibility" : "0",
119
  "isOptional" : "0",
120
  "dataType" : "Float16",
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+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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123
+ "shape" : "[1, 2, 9496]",
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  "name" : "logits9",
125
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126
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128
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129
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130
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131
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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133
+ "shape" : "[1, 2, 9496]",
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135
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136
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139
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140
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141
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
142
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143
+ "shape" : "[1, 2, 9496]",
144
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145
  "type" : "MultiArray"
146
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148
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149
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150
  "dataType" : "Float16",
151
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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153
+ "shape" : "[1, 2, 9496]",
154
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155
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156
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158
  "hasShapeFlexibility" : "0",
159
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161
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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163
+ "shape" : "[1, 2, 9496]",
164
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165
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166
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168
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169
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171
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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173
+ "shape" : "[1, 2, 9496]",
174
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179
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181
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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183
+ "shape" : "[1, 2, 9496]",
184
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185
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186
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188
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189
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190
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191
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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193
+ "shape" : "[1, 2, 9496]",
194
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195
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196
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207
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208
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213
  "name" : "hidden_states",
214
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215
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224
  "hasShapeFlexibility" : "0",
225
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+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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231
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234
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235
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236
  "dataType" : "Float16",
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+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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239
+ "shape" : "[1, 2, 9496]",
240
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241
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242
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244
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245
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246
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247
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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249
+ "shape" : "[1, 2, 9496]",
250
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251
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252
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254
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255
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256
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257
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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259
+ "shape" : "[1, 2, 9496]",
260
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261
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262
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264
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265
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266
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267
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
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269
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270
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271
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272
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274
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275
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276
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277
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278
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279
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280
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281
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282
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284
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285
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286
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287
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
288
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289
+ "shape" : "[1, 2, 9496]",
290
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291
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292
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294
  "hasShapeFlexibility" : "0",
295
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296
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297
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
298
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299
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300
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301
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302
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304
  "hasShapeFlexibility" : "0",
305
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306
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307
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309
+ "shape" : "[1, 2, 9496]",
310
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311
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312
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314
  "hasShapeFlexibility" : "0",
315
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316
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317
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
318
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319
+ "shape" : "[1, 2, 9496]",
320
  "name" : "logits10",
321
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322
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324
  "hasShapeFlexibility" : "0",
325
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326
  "dataType" : "Float16",
327
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
328
  "shortDescription" : "",
329
+ "shape" : "[1, 2, 9496]",
330
  "name" : "logits11",
331
  "type" : "MultiArray"
332
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334
  "hasShapeFlexibility" : "0",
335
  "isOptional" : "0",
336
  "dataType" : "Float16",
337
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
338
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339
+ "shape" : "[1, 2, 9496]",
340
  "name" : "logits12",
341
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342
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344
  "hasShapeFlexibility" : "0",
345
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346
  "dataType" : "Float16",
347
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
348
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349
+ "shape" : "[1, 2, 9496]",
350
  "name" : "logits13",
351
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352
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354
  "hasShapeFlexibility" : "0",
355
  "isOptional" : "0",
356
  "dataType" : "Float16",
357
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
358
  "shortDescription" : "",
359
+ "shape" : "[1, 2, 9496]",
360
  "name" : "logits14",
361
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362
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364
  "hasShapeFlexibility" : "0",
365
  "isOptional" : "0",
366
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367
+ "formattedType" : "MultiArray (Float16 1 × 2 × 9496)",
368
  "shortDescription" : "",
369
+ "shape" : "[1, 2, 9496]",
370
  "name" : "logits15",
371
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372
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374
  "hasShapeFlexibility" : "0",
375
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376
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377
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378
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379
+ "shape" : "[1, 2, 9496]",
380
  "name" : "logits16",
381
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382
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r2t2_lm_head_lut8.mlmodelc/model.mil CHANGED
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