v4: two-lane decoder (two pieces per infer call, grouped-query attention)
Browse files- README.md +23 -13
- SHA256SUMS +3 -3
- r2t2_FFN_PF_lut8_chunk_01of02.mlmodelc/analytics/coremldata.bin +1 -1
- r2t2_FFN_PF_lut8_chunk_01of02.mlmodelc/coremldata.bin +2 -2
- r2t2_FFN_PF_lut8_chunk_01of02.mlmodelc/metadata.json +90 -64
- r2t2_FFN_PF_lut8_chunk_01of02.mlmodelc/model.mil +0 -0
- r2t2_FFN_PF_lut8_chunk_01of02.mlmodelc/weights/weight.bin +2 -2
- r2t2_FFN_PF_lut8_chunk_02of02.mlmodelc/analytics/coremldata.bin +1 -1
- r2t2_FFN_PF_lut8_chunk_02of02.mlmodelc/coremldata.bin +2 -2
- r2t2_FFN_PF_lut8_chunk_02of02.mlmodelc/metadata.json +89 -63
- r2t2_FFN_PF_lut8_chunk_02of02.mlmodelc/model.mil +0 -0
- r2t2_FFN_PF_lut8_chunk_02of02.mlmodelc/weights/weight.bin +2 -2
- r2t2_lm_head_lut8.mlmodelc/analytics/coremldata.bin +1 -1
- r2t2_lm_head_lut8.mlmodelc/coremldata.bin +2 -2
- r2t2_lm_head_lut8.mlmodelc/metadata.json +70 -70
- r2t2_lm_head_lut8.mlmodelc/model.mil +0 -0
README.md
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@@ -28,9 +28,11 @@ Neural Engine holds the weights dequantised to fp16, about 4.5 GB, outside the p
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Converted with the pipeline in the VoiceInk fork's `tools/r2t2-coreml`: the encoder through
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coremltools 9 with a fixed 800-frame window (the fused `gelu` replaced by a tanh formulation, whose
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constant absolute error otherwise swamps this encoder's small activations), the decoder through
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[ANEMLL](https://github.com/Anemll/Anemll) as two stateful chunks
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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:
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@@ -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` (
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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` (
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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` | | |
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@@ -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
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cheap is keeping the prompt prefix and every completed encoder window in the decoder's KV state,
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prefilling only the new rows plus the previous pass's tokens as a draft, confirming the draft with
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one `verify` call and decoding only from the first divergence. Passes take 150–250 ms on an M5 Pro
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-
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For another runtime, the interface:
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-
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`
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output `output_hidden_states [1, B, 2048]`.
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`
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- Prompt: the Qwen3-ASR chat template, with the encoder rows in place of the `<|audio_pad|>`
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embeddings and, to force a language, the assistant turn opened with `language <Name><asr_text>`.
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The model's `|` marks where it stops trusting its own output.
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Converted with the pipeline in the VoiceInk fork's `tools/r2t2-coreml`: the encoder through
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coremltools 9 with a fixed 800-frame window (the fused `gelu` replaced by a tanh formulation, whose
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constant absolute error otherwise swamps this encoder's small activations), the decoder through
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+
[ANEMLL](https://github.com/Anemll/Anemll) as two stateful chunks whose KV state holds two
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sequences side by side ("lanes"), with a 128-row `prefill` of one lane and an `infer` that
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decodes one token of each lane in a single pass over the weights, and a head with a two-row
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`infer` and a `verify` function that returns the argmax of 128 rows in one call. Requires macOS 15
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or later (Core ML stateful models) on Apple silicon.
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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:
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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 of one lane) and `infer` (one row per lane) | 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` (two rows → `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` | | |
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on every pass, so the live text converges on the same result as an offline decode; what makes that
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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
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+
one `verify` call and decoding only from the first divergence. Passes take 150–250 ms on an M5 Pro.
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+
Offline, two 30 s pieces decode together, one token of each per `infer` call: a 120 s recording
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takes about 6 s (8.7 ms per token; 17 ms for a single piece).
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For another runtime, the interface:
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- Both chunks share one `MLState` of shape `(56, 8, 2048, 128)` fp16: lane *l* owns cache positions
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`l·1024 ..< (l+1)·1024`, written by absolute position, so a caller can rewind and overwrite. The
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causal mask is 0 to attend, −10 000 otherwise; output `output_hidden_states [1, B, 2048]`.
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- `prefill`: `hidden_states [1, 128, 2048]` fp16, `position_ids [128]` int32 (the lane's own
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positions), `causal_mask [1, 1, 128, 1024]`, `current_pos [1]` int32 (absolute: `l·1024 + p`),
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`lane_weights [2, 1, 1]` fp16 one-hot selecting the lane.
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- `infer`: `hidden_states [1, 2, 2048]` (one row per lane), `position_ids [2]`,
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`causal_mask [1, 1, 4, 1024]` (each lane's row twice: the two query heads of a K/V head share a
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matmul row axis), `current_pos [1]` and `current_pos_b [1]` (lane 0 and lane 1, absolute). An
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idle lane takes a zero row, a fully masked row and a scratch position its next real row overwrites.
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- Head `infer` takes `hidden_states [1, 2, 2048]` and returns `logits1…16` for both rows; `verify`
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takes 128 rows and returns per row and slice `argmax_val`, `argmax_hi`, `argmax_lo` (`[128, 16]`
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fp16), the slice's best logit and its index as `hi × 64 + lo`.
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- Prompt: the Qwen3-ASR chat template, with the encoder rows in place of the `<|audio_pad|>`
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embeddings and, to force a language, the assistant turn opened with `language <Name><asr_text>`.
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The model's `|` marks where it stops trusting its own output.
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SHA256SUMS
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0499602714160467f2d68b910651d6216020689f1e016be87a2d0019ee3baeab tokenizer.json
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4942d005604266809309cabc9f4e9cb89ce855d59b14681fdc0e1cc62ea26c4c tokenizer_config.json
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efa87abb745fd48d9ee657d62d1a18899e4ee8eba75e47507dbb5991f3b7095a embed_tokens.f16.bin
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b34645f8671aec799b836fb4f32ff591d57d061c12ea2c24a3faf0a713b10695 R2T2AudioEncoder.mlmodelc (tree)
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0499602714160467f2d68b910651d6216020689f1e016be87a2d0019ee3baeab tokenizer.json
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4942d005604266809309cabc9f4e9cb89ce855d59b14681fdc0e1cc62ea26c4c tokenizer_config.json
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efa87abb745fd48d9ee657d62d1a18899e4ee8eba75e47507dbb5991f3b7095a embed_tokens.f16.bin
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a419770619dc00aaa8d79eb3c4835f162cc2ad275433ef7c56e6663b0dd60da1 r2t2_FFN_PF_lut8_chunk_01of02.mlmodelc (tree)
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9cdb6253b241f4caa3c61c720976ddbedcd92aa0dc9bb933d52c784dd392c5e1 r2t2_FFN_PF_lut8_chunk_02of02.mlmodelc (tree)
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5967ef8ee01652f5b11923afc806774a112cb2f20974d574317138615e0eab9a r2t2_lm_head_lut8.mlmodelc (tree)
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b34645f8671aec799b836fb4f32ff591d57d061c12ea2c24a3faf0a713b10695 R2T2AudioEncoder.mlmodelc (tree)
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r2t2_FFN_PF_lut8_chunk_01of02.mlmodelc/analytics/coremldata.bin
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size 243
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r2t2_FFN_PF_lut8_chunk_01of02.mlmodelc/coremldata.bin
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size 1043
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r2t2_FFN_PF_lut8_chunk_01of02.mlmodelc/metadata.json
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{
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"metadataOutputVersion" : "3.0",
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"userDefinedMetadata" : {
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"com.github.apple.coremltools.version" : "9.0",
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"com.anemll.chunk_no" : "1",
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"com.github.apple.coremltools.source_dialect" : "TorchScript",
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"com.anemll.context_length" : "1024",
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"com.github.apple.coremltools.
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"com.anemll.num_chunks" : "2",
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"com.anemll.batch_size" : "128",
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"com.anemll.info" : "Converted with Anemll v0.1.1",
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"com.anemll.
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},
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"availability" : {
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"macOS" : "15.0",
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"hasShapeFlexibility" : "0",
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"isOptional" : "0",
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"dataType" : "Float16",
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"formattedType" : "MultiArray (Float16 1 ×
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"shortDescription" : "",
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"shape" : "[1,
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"name" : "hidden_states",
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"type" : "MultiArray"
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},
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"hasShapeFlexibility" : "0",
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"isOptional" : "0",
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"dataType" : "Int32",
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"shortDescription" : "",
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"shape" : "[
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"name" : "position_ids",
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"type" : "MultiArray"
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},
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"hasShapeFlexibility" : "0",
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"isOptional" : "0",
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"dataType" : "Float16",
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"formattedType" : "MultiArray (Float16 1 × 1 ×
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"shortDescription" : "",
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"shape" : "[1, 1,
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"name" : "causal_mask",
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"type" : "MultiArray"
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},
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"shape" : "[1]",
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"name" : "current_pos",
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"type" : "MultiArray"
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}
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],
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"outputSchema" : [
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"hasShapeFlexibility" : "0",
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"isOptional" : "0",
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"dataType" : "Float16",
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"formattedType" : "MultiArray (Float16 1 ×
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"shortDescription" : "",
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"shape" : "[1,
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"name" : "output_hidden_states",
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"type" : "MultiArray"
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}
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"hasShapeFlexibility" : "0",
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"isOptional" : "0",
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"dataType" : "Float16",
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"formattedType" : "MultiArray (Float16 1 ×
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"shortDescription" : "",
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"shape" : "[1,
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"name" : "hidden_states",
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"type" : "MultiArray"
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},
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"hasShapeFlexibility" : "0",
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"isOptional" : "0",
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"dataType" : "Int32",
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"type" : "MultiArray"
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"hasShapeFlexibility" : "0",
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"isOptional" : "0",
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"dataType" : "Float16",
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"shortDescription" : "",
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"shape" : "[1, 1,
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"name" : "causal_mask",
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"type" : "MultiArray"
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},
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"shape" : "[1]",
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"name" : "current_pos",
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"type" : "MultiArray"
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}
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"computePrecision" : "Mixed (Float16, Int16, Int32, UInt16)",
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{
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"dataType" : "Float16",
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"isOptional" : "0",
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"formattedType" : "State (Float16 56 × 8 ×
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"shortDescription" : "",
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"shape" : "[56, 8,
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"name" : "model_model_kv_cache_0",
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"type" : "State"
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}
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"hasShapeFlexibility" : "0",
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"isOptional" : "0",
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"dataType" : "Float16",
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"shortDescription" : "",
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"name" : "output_hidden_states",
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"type" : "MultiArray"
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}
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],
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"name" : "infer",
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"mlProgramOperationTypeHistogram" : {
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"Ios18.expandDims" :
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"Ios18.
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"Ios18.
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"Ios18.matmul" :
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"Identity" : 1,
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"Ios18.greaterEqual" : 2,
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"Select" : 2,
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"Ios18.readState" :
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"Tile" : 28,
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"Ios18.gather" : 2,
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"Ios18.add" :
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"Ios18.layerNorm" : 56,
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"Ios18.sliceUpdate" :
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"Ios18.writeState" :
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"Ios18.reshape" :
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"Ios18.constexprLutToDense" : 98,
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"Ios18.conv" : 98,
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"Ios18.concat" :
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"Ios18.transpose" : 84,
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"Ios18.cast" : 5,
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"Ios18.silu" : 14,
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"Ios18.
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}
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{
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"shape" : "[1]",
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"name" : "current_pos",
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"type" : "MultiArray"
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}
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"computePrecision" : "Mixed (Float16, Int16, Int32, UInt16)",
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{
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"dataType" : "Float16",
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"name" : "model_model_kv_cache_0",
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"type" : "State"
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}
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],
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"name" : "prefill",
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"mlProgramOperationTypeHistogram" : {
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|
| 367 |
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|
| 368 |
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|
| 369 |
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"shape" : "[1,
|
| 370 |
"name" : "logits15",
|
| 371 |
"type" : "MultiArray"
|
| 372 |
},
|
|
@@ -374,9 +374,9 @@
|
|
| 374 |
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|
| 375 |
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|
| 376 |
"dataType" : "Float16",
|
| 377 |
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|
| 378 |
"shortDescription" : "",
|
| 379 |
-
"shape" : "[1,
|
| 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 |
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|
| 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 |
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|
| 26 |
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| 31 |
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| 32 |
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|
| 33 |
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|
| 34 |
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|
| 38 |
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| 43 |
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| 44 |
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| 45 |
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| 46 |
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| 53 |
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| 54 |
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| 55 |
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| 56 |
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| 64 |
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| 65 |
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| 126 |
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| 135 |
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| 144 |
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| 145 |
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| 146 |
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| 148 |
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| 155 |
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| 156 |
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| 213 |
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|
| 214 |
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|
| 215 |
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|
| 224 |
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| 230 |
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| 231 |
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|
| 234 |
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| 240 |
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| 241 |
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| 242 |
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| 244 |
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| 249 |
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| 250 |
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| 251 |
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| 252 |
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| 254 |
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| 261 |
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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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| 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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| 288 |
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| 289 |
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| 290 |
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|
| 291 |
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|
| 292 |
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| 294 |
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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 |
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| 308 |
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| 309 |
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| 310 |
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| 311 |
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|
| 312 |
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|
| 314 |
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| 318 |
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| 319 |
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|
| 320 |
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|
| 321 |
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|
| 322 |
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|
| 324 |
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| 326 |
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| 328 |
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|
| 329 |
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| 330 |
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|
| 331 |
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|
| 332 |
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|
| 334 |
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| 335 |
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| 336 |
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| 338 |
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| 339 |
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| 340 |
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|
| 341 |
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|
| 342 |
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| 344 |
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| 348 |
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|
| 349 |
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|
| 350 |
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|
| 351 |
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| 352 |
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|
| 354 |
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| 355 |
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| 358 |
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| 359 |
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|
| 360 |
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|
| 361 |
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|
| 362 |
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|
| 364 |
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| 365 |
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| 366 |
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| 367 |
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| 368 |
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|
| 369 |
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| 370 |
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|
| 371 |
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|
| 372 |
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|
| 374 |
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| 375 |
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| 376 |
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| 377 |
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| 378 |
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|
| 379 |
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| 380 |
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|
| 381 |
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|
| 382 |
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|
r2t2_lm_head_lut8.mlmodelc/model.mil
CHANGED
|
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|
|
|