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NOTICE

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+EmbeddingGemma-2-Transcribe-CoreAI — NOTICE
+
+This repository redistributes, in Apple Core AI `.aimodel` / `.aimodelc` form, two graphs licensed under the Apache
+License, Version 2.0 (see LICENSE):
+
+- eg2_audio_fp16_T3000: the audio encoder of EmbeddingGemma 2 (google/embeddinggemma-2, revision
+  914f7f89142e33e77833254d9c9b90c3cef7303b, Google DeepMind; the Gemma 4 audio tower and embed_audio), licensed under
+  the Apache License, Version 2.0. Deployments of EmbeddingGemma 2 must adhere to the Gemma Prohibited Use Policy
+  (https://ai.google.dev/gemma/prohibited_use_policy). config.json is the model's configuration at that revision,
+  unchanged.
+- eg2_reader_fp16_T750: reader v1 of EmbeddingGemma 2 Transcribe (mlboydaisuke/EmbeddingGemma-2-Transcribe-Reader),
+  trained by mlboydaisuke (john-rocky), 2026-10, with CTC on LibriSpeech train-clean-100 (V. Panayotov, G. Chen,
+  D. Povey, S. Khudanpur, "Librispeech: an ASR corpus based on public domain audio books", ICASSP 2015,
+  https://www.openslr.org/12, CC BY 4.0), together with a word table of EmbeddingGemma 2 text embeddings of the words
+  of the LibriSpeech lexicon (https://www.openslr.org/11, public domain); vocab.txt lists those words.
+
+Conversion: mlboydaisuke (john-rocky), 2026-10. Code, gates and records:
+https://github.com/john-rocky/coreai-model-zoo (conversion/eg2_transcribe, models/embeddinggemma-2-transcribe).
+Modifications to the encoder as exported: a fixed 30 s window (3,000 log-mel frames, 750 tokens); float16 weights and
+arithmetic; every RMSNorm divides its row by the row's maximum absolute value before squaring (exact in real
+arithmetic); the attention written in its banded form with the query and key scales folded into the projections;
+the ±1e10 clamps that are no-ops at inference removed; the graphs' MLIR debug locations removed. The reader graph holds
+the reader and the word table in float16 and returns the arg-max word, its logit and the blank logit per token.
+The design of the banded attention, the folded scales and the max-abs RMSNorm follows the published description of
+EverPaper's Core AI port (EverPaper/embeddinggemma-2-coreai v1.2, Apache-2.0); no code was copied.
+
+Keep this NOTICE together with the LICENSE file when redistributing.

README.md

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+---
+license: apache-2.0
+library_name: coreai
+pipeline_tag: automatic-speech-recognition
+base_model: google/embeddinggemma-2
+base_model_relation: quantized
+language:
+  - en
+datasets:
+  - openslr/librispeech_asr
+tags: [core-ai, coreai, embeddinggemma, gemma, speech-recognition, asr, ctc, on-device, apple, ios, macos]
+---
+
+Core AI is Apple's on-device ML runtime in iOS 27 / macOS 27 and the successor to Core ML: PyTorch models are exported with Apple's `coreai-torch` (LLMs: `coreai.llm.export`) into `.aimodel` bundles that run on the GPU or the Neural Engine, e.g. Qwen3-8B 4-bit decodes at 94 tok/s on an M4 Max GPU, MLX 90 under the same protocol ([apple-silicon-llm-bench](https://github.com/john-rocky/apple-silicon-llm-bench), macOS 27 beta, 2026-06).
+
+# EmbeddingGemma 2 Transcribe — Core AI
+
+Apache-2.0, Gemma Prohibited Use Policy · source [google/embeddinggemma-2](https://huggingface.co/google/embeddinggemma-2/tree/914f7f89142e33e77833254d9c9b90c3cef7303b) (revision `914f7f89`) · reader weights [mlboydaisuke/EmbeddingGemma-2-Transcribe-Reader](https://huggingface.co/mlboydaisuke/EmbeddingGemma-2-Transcribe-Reader) · conversion and card in the [CoreAI-Model-Zoo](https://github.com/john-rocky/coreai-model-zoo/tree/main/models/embeddinggemma-2-transcribe)
+
+## What it is
+
+English speech to text from the audio encoder of Google's EmbeddingGemma 2. The frozen encoder turns 16 kHz audio
+into 25 soft tokens per second in the model's text-embedding space; a 13.3 M-parameter reader, trained on LibriSpeech,
+maps each token to a 768-d unit vector, and the graph reads the closest of 200,000 English words (cosine) or blank.
+Greedy CTC on the host (merge repeats, drop blanks) gives the transcript. There is no language model, no punctuation
+and no casing.
+
+This is an independent reproduction of the method behind Shahir's *Embedding Gemma Transcribe* Mac app (eg2.haku.sh),
+described in a post on 2026-10-13; that app's code and weights are not public. The encoder conversion and the reader
+here are this port's own, the reader trained from scratch (test-clean WER 6.81 %, test-other 15.10 %, greedy, no
+language model). The same reader runs on Android through LiteRT
+([mlboydaisuke/EmbeddingGemma-2-Transcribe-LiteRT](https://huggingface.co/mlboydaisuke/EmbeddingGemma-2-Transcribe-LiteRT)).
+
+Two static graphs, one 30 s window each (bytes of the JIT `.aimodel`; the iPhone 18 Pro AOT compiles are 587,306,390
+and 334,027,356):
+
+| Bundle | Graph | Inputs → outputs | Bytes |
+|---|---|---|---:|
+| `eg2_audio_fp16_T3000` | the Gemma 4 audio tower (12-layer conformer) + `embed_audio`, fp16 | `features [1, 3000, 128]`, `half_mask [1, 1500]` → `soft_tokens [1, 750, 512]` | 586,857,301 |
+| `eg2_reader_fp16_T750` | the reader + the 200,000 × 768 word table + argmax, fp16 | `soft_tokens [1, 750, 512]`, `token_mask [1, 750]` → `top1_index [1, 750]` (int32), `top1_logit [1, 750]`, `blank_logit [1, 750]` | 333,974,935 |
+
+## Files
+
+| Path | What |
+|---|---|
+| `macos/eg2_audio_fp16_T3000.aimodel`, `macos/eg2_reader_fp16_T750.aimodel` | the two bundles for the Mac (macOS 27), specialized when they load |
+| `ios/` | the same bytes for any iPhone (iOS 27), specialized on the phone when they load |
+| `ios-h19p/eg2_audio_fp16_T3000.aimodelc`, `ios-h19p/eg2_reader_fp16_T750.aimodelc` | compiled ahead of time for the iPhone 18 Pro (`h19p`) only; never load them on a Mac |
+| `macos-encoder-split7-30s/eg2_front_layer7_fp32_T3000.aimodel`, `macos-encoder-split7-30s/eg2_body_layer7_fp16_T3000.aimodel` | a second form of the encoder, for the Mac only: the same encoder cut in two after layer 7, for running the encoder alone on long audio (below); the app does not download it |
+| `pipeline.json`, `reader_config.json`, `vocab.txt`, `mel_filters.bin` | the host files: the pipeline forms, the reader's window contract, the 200,000 words (line number = index), the 257 × 128 float64 mel filter bank; the same files again under `host/` |
+| `config.json` | EmbeddingGemma 2's configuration at revision `914f7f89`, unchanged |
+| `provenance/` | the gate, timing, pipeline and strip records behind the numbers below (`SOURCES.tsv` gives each original's sha256); `provenance/app/` holds those of the app rows, `provenance/encoder-split7/` those of the second encoder form |
+| `SHA256SUMS` | every file |
+
+`pipeline.json` also names a two-bundle encoder form (`split7-gpu`) whose bundles are not in `macos/`; a loader that
+lists only the forms whose bundles are present there (as the app below does) skips it.
+
+## Contract
+
+- **Log-mel (host)**: transformers' `Gemma4AudioFeatureExtractor`: 16 kHz mono, 160 zeros in front, 20 ms frames every
+  10 ms, periodic Hann window, |FFT| of 512, 128 HTK mel bins over 0–8 kHz without normalization (the 257 × 128
+  float64 matrix is `mel_filters.bin`), `log(mel + 0.001)`. Only frames whose window is all real audio count:
+  n_frames = ⌊(samples − 161) / 160⌋ + 1 (2,999 for 30 s); rows from n_frames on are 0.
+- **`half_mask`**: 1 on the leading ⌈n_frames / 2⌉ of its 1,500 entries, 0 after. Without it a window whose n_frames %
+  4 == 2 gets a wrong last token (cosine 0.85–0.88): the second stride-2 convolution reads the half frame.
+- **Encoder**: causal apart from the one-frame look-ahead of the 3 × 3 subsampling convolutions; each attention query
+  sees itself and the 11 tokens before it. Tokens = ⌈⌈n_frames / 2⌉ / 2⌉, 25 per second.
+- **Windows for long audio**: 30 s windows (3,000 frames, 750 tokens), each starting 96 tokens (3.84 s) before the
+  end of the previous one; a window keeps only its new tokens and drops its last token (the last token of a 30 s cut
+  misses one input frame); the last window keeps through the end. Step = 653 tokens. A window needs 83 tokens of left
+  context before its tokens match a continuous run (per-token cosine mean ≥ 0.999 and min ≥ 0.99).
+- **Reader**: `token_mask` is 1 on the valid tokens. Per token, `top1_index` is the argmax over the 200,000 words of
+  cos(h, table), `top1_logit` = 91.218 × that cosine, `blank_logit` the blank score. The word table and the rotary
+  positions are constants of the graph.
+- **Host read-out**: blank where `blank_logit > top1_logit` (strict: a tie reads the word), else `top1_index`; merge
+  repeats, drop blanks; map ids through `vocab.txt` (line number = index, blank = 200,000).
+- **Calls**: two calls of the same bundle's function issued together return wrong outputs (encoder: max |Δ| 8.04 against
+  the sequential pass). The encoder of window i + 1 and the reader of window i can run together (two bundles): the
+  labels stay identical to the sequential pass.
+
+## Measured
+
+Mac: M4 Max 128 GB, macOS 27.0 (26A428), Core AI Python runtime (coreai-core 1.0.0b2); ms = median of 8
+process-level repetitions × 20 calls. iPhone 18 Pro (iPhone19,2), iOS 27.2 (24B5099f), Swift Core AI runtime, on USB
+power, thermal state nominal; ms = median of rounds 1–7 of 8 rounds × 20 calls (round 0 = cold in brackets). ×RT =
+audio seconds / wall-clock seconds. The long recording is *The Adventure of the Speckled Band* (LibriVox, 3,257.4 s,
+125 windows), scored against the Project Gutenberg text (9,889 words; the LibriVox intro and outro are in the audio
+only).
+
+| Device | Workload | Form | Time | ×RT | Parity |
+|---|---|---|---|---:|---|
+| Mac M4 Max | encoder, one 30 s window | fp16, GPU | 40.02 ms | 750 | 20/20 clips pass, per-token cosine min 0.99645 |
+| Mac M4 Max | reader + table, one 30 s window | fp16, GPU | 22.45 ms | 1,336 | fixture transcripts 19/20 = PyTorch, test-clean (200 utterances) WER 5.878 % = PyTorch |
+| Mac M4 Max | 3,257.4 s recording | encoder GPU ∥ reader Neural Engine, 2 windows in flight | 5.681 s | 573 | WER 5.80 % (float32 PyTorch path on the same windows: 5.78 %) |
+| Mac M4 Max | 3,257.4 s recording | encoder GPU → reader GPU, one window at a time | 8.093 s | 402.5 | WER 5.79 % |
+| Mac M4 Max | 3,257.4 s recording, the app | encoder GPU ∥ reader Neural Engine, 2 windows in flight (the app's Mac default) | 5.480 s | 594 | WER 5.78 % (572 of 9,889 words, as many as the float32 PyTorch path); fixture transcripts 19/20 = PyTorch (one near-tie between two words) |
+| iPhone 18 Pro | encoder, one 30 s window | fp16, GPU, AOT h19p | 59.60 ms (58.49) | 503 | 20/20 clips pass, per-token cosine min 0.99626 |
+| iPhone 18 Pro | encoder, one 30 s window | fp16, GPU, JIT `.aimodel` | 59.88 ms (56.81) | 501 | outputs bit-identical to the AOT bundle |
+| iPhone 18 Pro | reader + table, one 30 s window | fp16, GPU, AOT h19p | 32.99 ms (32.77) | 909 | the ms of the same compiled graph, timed with an interim reader checkpoint's weights; reader v1 on the phone, through the app: fixture transcripts 20/20 = PyTorch, long-recording WER 5.77 % |
+| iPhone 18 Pro | 3,257.4 s recording, the app | encoder GPU → reader GPU, one window at a time (the app's iPhone default) | 11.631 s | 280 | WER 5.77 % (571 of 9,889 words; float32 PyTorch path 572); fixture transcripts 20/20 = PyTorch |
+
+- The Mac rows ran the AOT h16c compile of each graph; `macos/` holds the JIT `.aimodel`, which the Mac specializes
+  when it loads it (encoder 40.50 ms, reader 22.68 ms, outputs identical to the AOT compile).
+- The bundles here are the measured ones with their MLIR debug locations removed (coreai-torch `strip_debug_info`; the
+  export recorded the converting machine's file paths there). The operations are unchanged: on the Mac each stripped
+  bundle returns outputs bit-identical to the gated one on the 20 fixture clips (the encoder with the GPU preferred,
+  the reader with the GPU and with the Neural Engine preferred; `provenance/strip/compare_mac.json`), and the
+  `ios-h19p/` compiles of the stripped graphs hold the same weights file (`resources.bin`, byte for byte) as the
+  compiles of the gated graphs. The app rows ran this repository's bundles (`macos/` on the Mac, `ios-h19p/` on the
+  iPhone, with reader v1); the iPhone encoder and reader rows ran the compiles of the unstripped graphs.
+- The app rows: the eg2-transcribe app (Release build, Swift Core AI runtime) on the 125 windows, the log-mel computed
+  before the clock starts (decode and log-mel add 0.63 + 0.05 s on the Mac, 0.49 + 0.16 s on the iPhone); the iPhone
+  pass started and ended at thermal state nominal.
+- The Mac's Neural Engine: the fp16 encoder runs there as one region at 85.21 ms but misses the per-token bar at
+  near-silent tokens (min 0.888). Cut in two after layer 7 (a float32 front on the GPU, a float16 body on the Neural
+  Engine) it passes (min 0.99671) and runs 30.96 ms per window with two windows in flight (×969); that two-bundle form
+  is the second encoder form in `macos-encoder-split7-30s/` (below). The iPhone's Neural Engine runs none of the 30 s
+  graphs: with the Neural Engine preferred they are placed on the GPU, the encoder's Neural Engine AOT h19p fails to
+  load (`failedToSpecialize`), and the reader's is ended by jetsam at 3.77 GB under the default memory limit.
+- Not measured: a launch of the app right after an install with reader v1, reader v1's own gate on the iPhone (the
+  200 test-clean utterances), other iPhones (the JIT `.aimodel` specializes on each; the AOT bundle is h19p only),
+  languages other than English, noisy or conversational speech.
+
+## Second encoder form: two bundles for long audio on a Mac
+
+`macos-encoder-split7-30s/` holds the encoder of this repository cut in two after conformer layer 7: a float32 front
+for the GPU and a float16 body for the Neural Engine, both JIT `.aimodel` bundles for the Mac (macOS 27). It is for
+running the encoder alone over long recordings: with two windows in flight the M4 Max turns a stream of 30 s windows
+into soft tokens at 30.96 ms per window, against 40.04 ms for the one-bundle encoder above in the same measurement
+window. The app below does not download this folder (it fetches `macos/`), and `ios/` and `ios-h19p/` do not have it.
+
+| Bundle | Graph | Inputs → outputs | Load with | Bytes |
+|---|---|---|---|---:|
+| `eg2_front_layer7_fp32_T3000.aimodel` | the subsampling + conformer layers 0–7, float32 | `features [1, 3000, 128]`, `half_mask [1, 1500]` → `hidden [1, 750, 1024]`, float32 | the GPU preferred | 777,292,646 |
+| `eg2_body_layer7_fp16_T3000.aimodel` | conformer layers 8–11 + `embed_audio`, float16 (max-abs RMSNorm) | `hidden [1, 750, 1024]` → `soft_tokens [1, 750, 512]`, float16 | the Neural Engine preferred | 198,052,423 |
+
+- **Contract**: the log-mel, `half_mask` and windows of the encoder above, as float32. The host casts the front's
+  `hidden` to float16 and passes it to the body. The soft tokens pass the same parity bar as the encoder above and go
+  to the same reader.
+- **Two windows in flight**: issue the front of window i + 1 together with the body of window i. They are two
+  bundles, so the GPU and the Neural Engine work at once, and the soft tokens stay bit-identical to one window at a
+  time. Never issue two calls of one bundle's function together.
+- **When it helps**: long recordings on a Mac. The 30.96 ms is the wall time of 20 windows in a row divided by 20; one
+  window alone takes 57.40 ms, longer than the one-bundle encoder.
+- **Not for the iPhone**: on the iPhone 18 Pro the body is placed on the GPU with the Neural Engine preferred, its
+  Neural Engine AOT h19p fails to load (`failedToSpecialize`), and the float32 front alone takes 124.4 ms per 30 s
+  window; two windows in flight take 144.0–144.6 ms per window there, against 58.4–60.9 ms for the one-bundle fp16
+  encoder in the same launch.
+
+| Device | Workload | Form | Time | ×RT | Parity |
+|---|---|---|---|---:|---|
+| Mac M4 Max | encoder, 30 s windows, 2 in flight | front GPU ∥ body Neural Engine | 30.96 ms per window | 969 | 20/20 clips pass, per-token cosine min 0.99671; soft tokens bit-identical to one window at a time |
+| Mac M4 Max | encoder, one 30 s window | front GPU → host cast → body Neural Engine | 57.40 ms (front 29.13 + cast 0.58 + body 27.75) | 523 | 20/20 clips pass, per-token cosine min 0.99671 |
+
+- Measured like the Mac rows above, on the AOT h16c compile of each graph (front GPU, body Neural Engine with one
+  Neural Engine region), in one measurement window with the one-bundle encoder (40.04 ms there). On the 20 fixture
+  clips the bundles here (the JIT sources of that compile, with their MLIR debug locations removed) return hidden
+  states and soft tokens bit-identical to the gated `.aimodel` bundles and to the AOT h16c compile
+  (`provenance/encoder-split7/compare_mac.json`).
+- Rebuild: `split_encoder.py export --part front --stage layer7 --bucket 3000 --aot gpu` and `split_encoder.py export
+  --part body --stage layer7 --bucket 3000 --aot neural-engine` (recipe `embeddinggemma-2-transcribe-macos-encoder-split7`
+  in the CoreAI-Model-Zoo); the records are in `provenance/encoder-split7/`.
+
+## Parity
+
+- **Encoder bar**: per-token cosine against transformers 5.19.0 float32 soft tokens (the default attention; `eager`
+  inverts the audio mask in that version) on 20 LibriSpeech test-clean clips: every clip mean ≥ 0.999 and min ≥ 0.99,
+  no non-finite value, two passes with drift 0, and a red arm (`half_mask` all ones must break the last token of the
+  3 clips with n_frames % 4 == 2; it does on every row).
+- **float16 and near-silent tokens**: the tokens that fail on the Neural Engine sit in near-silence (log-mel
+  −6.91 = log 0.001 in every bin). In PyTorch, rounding only the input features to float16 already moves them to
+  0.996; moving 12 of the 115,584 inputs of one clip by one float16 step puts one token anywhere from 0.972 to
+  0.99996 (7 of 24 trials below 0.99). The GPU passes, and its per-token minimum is a number, not a margin.
+- **Task bar for the app path**: a float16 path fed by another log-mel implementation is judged by what it writes:
+  fixture transcripts ≥ 19/20 equal to the PyTorch path and long-recording WER ≤ PyTorch + 0.1 pt; the extractor itself
+  must stay within 1e-5 of transformers' and reach per-token cosine ≥ 0.99999 through the float32 encoder.
+- **Reader bar**: against the reader's own float32 PyTorch forward and greedy labels on the same windows, the 20 fixture
+  utterances and the 200 leading test-clean utterances (44,162 tokens): test-clean WER ≤ PyTorch + 0.2 pt and fixture
+  transcripts ≥ 19/20 for float16. Two red arms: `token_mask` all ones changes 7 of 10 transcripts, and the second half
+  of the valid soft tokens set to 0 changes the labels of 10 of 10 utterances.
+- **Pipeline**: WER on the long recording next to the float32 PyTorch path on the same windows.
+
+## Levers
+
+| Lever | Result |
+|---|---|
+| quant | fp16 ships. Mac GPU, 30 s: fp16 40.02 ms, fp32 42.47, fp16 weights with fp32 arithmetic 48.17, int8 (Linear, per block of 32, weights only) 42.80 ms; int8 misses the per-token bar (min 0.929) and compiles to the fp32 size (1,174 MB). On the iPhone 18 Pro the fp32 encoder (AOT h19p) takes 188.53 ms against 60.08 ms for fp16 in one launch (3.1×). Reader: fp16 22.45 ms against fp32 28.05 ms, table 307 MB against 614 MB. |
+| chunk | 30 s windows. Mac GPU 10 s 17.87 ms vs 30 s 40.02 ms; with the 96-token overlap that is ×345 vs ×654 of new audio per call. iPhone 20.87 vs 59.88 ms (×295 vs ×437 with the overlap). Reader 9.38 ms (10 s) vs 22.45 ms (30 s). |
+| aot | JIT and AOT run at the same speed with the same outputs (Mac encoder 40.50 vs 40.22 ms, reader 22.68 vs 22.51 ms; iPhone encoder 59.88 vs 59.60 ms, reader 32.97 vs 32.99 ms with the interim reader weights). AOT saves the phone's on-device specialization (the encoder in a fresh app container: JIT load 2.94 s and 10.1 s for the opening call, AOT 0.91 s and 0.20 s). fp32 runs on the phone only as AOT: its JIT aborts in MPSGraph's on-device canonicalization, the AOT h19p loads in 1.26 s. |
+| compute-unit | Mac: encoder GPU, reader on the Neural Engine beside it (45.55 ms there vs 22.45 ms on the GPU; the two run side by side, 5.68 s vs 10.68 s one window at a time). The all-fp16 encoder on the Neural Engine misses the per-token bar; the two-bundle cut passes (above). iPhone: GPU only at 30 s (above). |
+| state | n-a: the metric is throughput on recordings (× real time); a streaming form splits the same audio into smaller calls for latency and does not raise throughput. |
+
+## Reproduce
+
+[`conversion/eg2_transcribe/`](https://github.com/john-rocky/coreai-model-zoo/tree/main/conversion/eg2_transcribe) in
+the CoreAI-Model-Zoo re-authors the encoder from `model.safetensors` and puts the reader and its table into one graph;
+[`models/embeddinggemma-2-transcribe/recipe.toml`](https://github.com/john-rocky/coreai-model-zoo/blob/main/models/embeddinggemma-2-transcribe/recipe.toml)
+names the commands:
+
+```bash
+PY=<venv>/bin/python    # coreai-core 1.0.0b2, coreai-torch 0.4.1, torch 2.9.0
+$PY conversion/eg2_transcribe/parity_encoder.py parity
+$PY conversion/eg2_transcribe/export_encoder.py --form fp16 --bucket 3000 --aot gpu,neural-engine
+$PY conversion/eg2_transcribe/export_reader.py --form fp16 --tokens 750 --aot gpu,neural-engine
+$PY conversion/eg2_transcribe/gate_encoder.py --bundle <exports>/fp16/eg2_audio_fp16_T3000_gpu_h16c.aimodelc --backend gpu
+$PY conversion/eg2_transcribe/gate_reader.py gate --bundle <exports>/reader/fp16/eg2_reader_fp16_T750_gpu_h16c.aimodelc --backend gpu
+$PY conversion/eg2_transcribe/strip_ship.py strip && $PY conversion/eg2_transcribe/strip_ship.py aot
+```
+
+Lessons from the port:
+[`knowledge/embeddinggemma-2-transcribe-port.md`](https://github.com/john-rocky/coreai-model-zoo/blob/main/knowledge/embeddinggemma-2-transcribe-port.md).
+
+## App
+
+The `ios/` directory of the [eg2-transcribe repository](https://github.com/john-rocky/eg2-transcribe) is a SwiftUI app for iPhone and Mac that downloads this model
+folder (`pipeline.json`, `reader_config.json`, `vocab.txt`, `mel_filters.bin`, `SHA256SUMS` and the platform's bundle
+folder: `ios-<arch>/` when the phone's chip has one, else `ios/`; `macos/` on the Mac), computes the log-mel in Swift
+with a float64 FFT (within 4.77e-7 of transformers'), runs the windows through the two bundles and shows the
+transcript. When the folder's fingerprint (`SHA256SUMS` and every bundle file) differs from the one it loaded last, it
+clears its Core AI cache before loading. Its defaults: on the Mac the reader on the Neural Engine with two windows in
+flight, on the iPhone both bundles on the GPU one window at a time.
+
+With reader v1 on the 3,257.4 s recording (the app rows above): the Mac default takes 5.480 s (×594), WER 5.78 % (572
+of 9,889 words, as many as the float32 PyTorch path), fixture transcripts 19/20 = PyTorch (one near-tie between two
+words); with both bundles on the GPU, 7.288 s with two windows in flight and 7.861 s one at a time (WER 5.77 %,
+fixture 20/20). The iPhone 18 Pro default takes 11.631 s (×280; thermal state nominal, on USB power), WER 5.77 % (571),
+fixture transcripts 20/20 = PyTorch. From launch to the transcript of a 10.4 s clip: on the Mac 5.21 s right after the
+app cleared its Core AI cache and 0.50 s from the cache; on the iPhone 18 Pro 1.28 s and 0.51 s. A launch right after
+an install with reader v1: not yet measured.
+
+## Licenses
+
+- The bundles: Apache-2.0 (`LICENSE`, `NOTICE`). EmbeddingGemma 2 (Google DeepMind) is Apache-2.0; deployments must
+  adhere to the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
+- The reader is trained on LibriSpeech train-clean-100 (CC BY 4.0); the word table holds EmbeddingGemma 2 text
+  embeddings of the words of the LibriSpeech lexicon (public domain).
+- Graph reference: transformers' `modeling_gemma4.py` / `modeling_embedding_gemma2.py` (Apache-2.0). The banded
+  attention, the folded q / k scales and the max-abs RMSNorm follow the published design of EverPaper's Core AI port
+  ([EverPaper/embeddinggemma-2-coreai](https://huggingface.co/EverPaper/embeddinggemma-2-coreai) v1.2, Apache-2.0);
+  no code was copied.
+- Gate data: LibriSpeech test-clean (CC BY 4.0); the long recording is LibriVox (public domain) with the Project
+  Gutenberg #1661 text.

SHA256SUMS

@@ -0,0 +1,83 @@
+# EmbeddingGemma-2-Transcribe-CoreAI, staged 2026-10-14. Verify with: shasum -a 256 -c SHA256SUMS
+# macos/, ios/ (same bytes): the gated .aimodel bundles with their MLIR debug locations stripped; Mac outputs bit-identical to the gated bundles (provenance/strip/compare_mac.json)
+# ios-h19p/: compiled from the stripped .aimodel bundles (coreai-build 3600.83.1, --platform iOS --preferred-compute gpu --architecture h19p); resources.bin byte-identical to the compiles of the gated graphs; ran on the iPhone 18 Pro in the app with reader v1 (provenance/app/)
+# macos-encoder-split7-30s/ (Mac only; at the root, so an app that fetches macos/* does not download it): the encoder cut after layer 7, the gated .aimodel bundles with their MLIR debug locations stripped; Mac outputs bit-identical to the gated bundles and to their h16c AOT compiles (provenance/encoder-split7/compare_mac.json)
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config.json

@@ -0,0 +1,177 @@
+{
+  "architectures": [
+    "EmbeddingGemma2Model"
+  ],
+  "audio_config": {
+    "_name_or_path": "",
+    "architectures": null,
+    "attention_chunk_size": 12,
+    "attention_context_left": 13,
+    "attention_context_right": 0,
+    "attention_invalid_logits_value": -1000000000.0,
+    "attention_logit_cap": 50.0,
+    "chunk_size_feed_forward": 0,
+    "conv_kernel_size": 5,
+    "dtype": "bfloat16",
+    "gradient_clipping": 10000000000.0,
+    "hidden_act": "silu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "model_type": "gemma4_audio",
+    "num_attention_heads": 8,
+    "num_hidden_layers": 12,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_proj_dims": 1536,
+    "problem_type": null,
+    "residual_weight": 0.5,
+    "return_dict": true,
+    "rms_norm_eps": 1e-06,
+    "subsampling_conv_channels": [
+      128,
+      32
+    ],
+    "use_clipped_linears": true
+  },
+  "audio_token_id": 258881,
+  "boa_token_id": 256000,
+  "boi_token_id": 255999,
+  "dtype": "bfloat16",
+  "eoa_token_index": 258883,
+  "eoi_token_id": 258882,
+  "image_token_id": 258880,
+  "initializer_range": 0.02,
+  "model_type": "embedding_gemma2",
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 2,
+    "dtype": "bfloat16",
+    "embedding_dim": 768,
+    "eos_token_id": 1,
+    "head_dim": 256,
+    "hidden_activation": "gelu_pytorch_tanh",
+    "hidden_size": 512,
+    "hidden_size_per_layer_input": 512,
+    "initializer_range": 0.02,
+    "intermediate_size": 2048,
+    "layer_types": [
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention"
+    ],
+    "max_position_embeddings": 262144,
+    "model_type": "embedding_gemma2_text",
+    "num_attention_heads": 4,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 2,
+    "pad_token_id": 0,
+    "per_layer_config": {
+      "05": {
+        "head_dim": 512,
+        "num_key_value_heads": 1
+      },
+      "11": {
+        "head_dim": 512,
+        "num_key_value_heads": 1
+      },
+      "17": {
+        "head_dim": 512,
+        "num_key_value_heads": 1
+      },
+      "23": {
+        "head_dim": 512,
+        "num_key_value_heads": 1
+      }
+    },
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "full_attention": {
+        "rope_theta": 1000000.0,
+        "rope_type": "default"
+      },
+      "sliding_attention": {
+        "rope_theta": 10000.0,
+        "rope_type": "default"
+      }
+    },
+    "sliding_window": 512,
+    "vocab_size": 262144
+  },
+  "transformers_version": "5.18.0.dev0",
+  "video_token_id": 258884,
+  "vision_config": {
+    "_name_or_path": "",
+    "architectures": null,
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "chunk_size_feed_forward": 0,
+    "default_output_length": 280,
+    "dtype": "bfloat16",
+    "global_head_dim": 64,
+    "head_dim": 64,
+    "hidden_activation": "gelu_pytorch_tanh",
+    "hidden_size": 768,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "max_position_embeddings": 131072,
+    "model_type": "gemma4_vision",
+    "num_attention_heads": 12,
+    "num_hidden_layers": 16,
+    "num_key_value_heads": 12,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "patch_size": 16,
+    "pooling_kernel_size": 3,
+    "position_embedding_size": 10240,
+    "problem_type": null,
+    "return_dict": true,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+      "rope_theta": 100.0,
+      "rope_type": "axial"
+    },
+    "standardize": false,
+    "use_clipped_linears": false
+  },
+  "vision_soft_tokens_per_image": 280
+}

host/pipeline.json

@@ -0,0 +1,34 @@
+{
+  "schema": 1,
+  "default_form": {"macos": "gpu-ane", "ios": "gpu"},
+  "overlap": {"macos": true, "ios": false},
+  "forms": [
+    {
+      "id": "gpu",
+      "title": "Encoder GPU · reader GPU",
+      "stages": [
+        {"name": "encoder", "bundle": "eg2_audio_fp16_T3000", "compute": "gpu"},
+        {"name": "reader", "bundle": "eg2_reader_fp16_T750", "compute": "gpu"}
+      ]
+    },
+    {
+      "id": "gpu-ane",
+      "title": "Encoder GPU · reader Neural Engine",
+      "platforms": ["macos"],
+      "stages": [
+        {"name": "encoder", "bundle": "eg2_audio_fp16_T3000", "compute": "gpu"},
+        {"name": "reader", "bundle": "eg2_reader_fp16_T750", "compute": "ane"}
+      ]
+    },
+    {
+      "id": "split7-gpu",
+      "title": "Encoder in two bundles (GPU, Neural Engine) · reader GPU",
+      "platforms": ["macos"],
+      "stages": [
+        {"name": "front", "bundle": "eg2_front_layer7_fp32_T3000", "compute": "gpu"},
+        {"name": "body", "bundle": "eg2_body_layer7_fp16_T3000", "compute": "ane"},
+        {"name": "reader", "bundle": "eg2_reader_fp16_T750", "compute": "gpu"}
+      ]
+    }
+  ]
+}

host/reader_config.json

@@ -0,0 +1,79 @@
+{
+ "version": "v1",
+ "input": {
+  "shape": "[T, 512]",
+  "dtype": "float32",
+  "what": "EmbeddingGemma 2 audio soft tokens (embed_audio output), 25 per second"
+ },
+ "context": "bidirectional within the window",
+ "architecture": {
+  "name": "a",
+  "kind": "transformer encoder from scratch",
+  "layers": 4,
+  "d_model": 512,
+  "heads": 8,
+  "ffn": 2048,
+  "activation": "gelu",
+  "norm": "pre-LayerNorm",
+  "positions": "rotary (base 10000)",
+  "input": "LayerNorm -> Linear 512->512",
+  "output": "LayerNorm -> Linear 512->768",
+  "params": 13268994
+ },
+ "output": {
+  "vector": "[T, 768] L2-normalized",
+  "blank_logit": "[T]"
+ },
+ "logits": "scale * cos(vector, table[w]) for w in vocab; blank from blank_logit",
+ "scale": 91.21830749511719,
+ "vocab": "vocab.txt",
+ "table": {
+  "file": "word_table.npy",
+  "dtype": "float16",
+  "shape": "[200000, 768]",
+  "prompt": "task: search result | query: ",
+  "normalized": true
+ },
+ "decode": "greedy argmax over N+1, collapse repeats, drop blank",
+ "window": {
+  "seconds": 30,
+  "left_overlap_tokens": 96,
+  "right_extra_tokens": 1,
+  "note": "encoder is causal; the 83 tokens at the start of a window and its last token differ from a continuous run (fixtures/long-speckled-band: soft_tokens_w30 vs soft_tokens_continuous)"
+ },
+ "weights": {
+  "file": "reader.pt",
+  "format": "torch state_dict, float32",
+  "code": "train/model.py Reader(arch, n_vocab, k)",
+  "keys": "body.* (reader body), blank.weight [1, 768], blank.bias [1], log_scale (scale = exp(log_scale))"
+ },
+ "n_vocab": 200000,
+ "wer": {
+  "dev-clean": {
+   "wer": 0.06566,
+   "utterances": 2703,
+   "ref_words": 54402
+  },
+  "dev-other": {
+   "wer": 0.14927,
+   "utterances": 2864,
+   "ref_words": 50948
+  },
+  "test-clean": {
+   "wer": 0.06807,
+   "utterances": 2620,
+   "ref_words": 52576
+  },
+  "test-other": {
+   "wer": 0.15102,
+   "utterances": 2939,
+   "ref_words": 52343
+  },
+  "fixtures": {
+   "wer": 0.04806,
+   "utterances": 20,
+   "ref_words": 541
+  },
+  "decode": "greedy, no language model"
+ }
+}

ios/eg2_audio_fp16_T3000.aimodel/main.hash

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ios/eg2_audio_fp16_T3000.aimodel/metadata.json

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+{
+  "producer" : "coreai-core 1.0.0b2",
+  "author" : "coreai-model-zoo community port; weights Google DeepMind (Gemma Team)",
+  "description" : "EmbeddingGemma 2 audio encoder (Gemma 4 audio tower + embed_audio): log-mel features [1,3000,128] + half_mask -> soft tokens [1,750,512]; fp16; source google\/embeddinggemma-2@914f7f89",
+  "creationDate" : "20261014T022944Z",
+  "license" : "Apache-2.0 + Gemma Prohibited Use Policy",
+  "assetVersion" : "2.0"
+}

ios/eg2_reader_fp16_T750.aimodel/main.hash

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ios/eg2_reader_fp16_T750.aimodel/metadata.json

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+{
+  "assetVersion" : "2.0",
+  "author" : "coreai-model-zoo community port; reader trained with CTC on LibriSpeech train-clean-100 (CC BY 4.0); word table: EmbeddingGemma 2 (Google DeepMind) embeddings of the LibriSpeech lexicon",
+  "description" : "EG2 transcribe reader (arch a, 13,268,994 parameters, scale 91.2183) + 200,000-word table: soft tokens [1,750,512] + token_mask -> top-1 word index \/ logit, blank logit; fp16; weights 67a06a857b9a",
+  "producer" : "coreai-core 1.0.0b2",
+  "creationDate" : "20261014T022946Z",
+  "license" : "Apache-2.0; word table from google\/embeddinggemma-2 text embeddings (Gemma Prohibited Use Policy)"
+}

macos-encoder-split7-30s/eg2_body_layer7_fp16_T3000.aimodel/main.hash

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macos-encoder-split7-30s/eg2_body_layer7_fp16_T3000.aimodel/metadata.json

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+{
+  "license" : "Apache-2.0 + Gemma Prohibited Use Policy",
+  "producer" : "coreai-core 1.0.0b2",
+  "creationDate" : "20261014T064935Z",
+  "description" : "EmbeddingGemma 2 audio encoder, body of the cut after layer7 (float16): hidden -> soft_tokens; bucket 3000 frames; source google\/embeddinggemma-2@914f7f89",
+  "author" : "coreai-model-zoo community port; weights Google DeepMind (Gemma Team)",
+  "assetVersion" : "2.0"
+}

macos-encoder-split7-30s/eg2_front_layer7_fp32_T3000.aimodel/main.hash

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macos-encoder-split7-30s/eg2_front_layer7_fp32_T3000.aimodel/metadata.json

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+{
+  "description" : "EmbeddingGemma 2 audio encoder, front of the cut after layer7 (float32): features, half_mask -> hidden; bucket 3000 frames; source google\/embeddinggemma-2@914f7f89",
+  "assetVersion" : "2.0",
+  "author" : "coreai-model-zoo community port; weights Google DeepMind (Gemma Team)",
+  "producer" : "coreai-core 1.0.0b2",
+  "creationDate" : "20261014T064933Z",
+  "license" : "Apache-2.0 + Gemma Prohibited Use Policy"
+}

macos/eg2_audio_fp16_T3000.aimodel/main.hash

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macos/eg2_audio_fp16_T3000.aimodel/metadata.json

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+{
+  "producer" : "coreai-core 1.0.0b2",
+  "author" : "coreai-model-zoo community port; weights Google DeepMind (Gemma Team)",
+  "description" : "EmbeddingGemma 2 audio encoder (Gemma 4 audio tower + embed_audio): log-mel features [1,3000,128] + half_mask -> soft tokens [1,750,512]; fp16; source google\/embeddinggemma-2@914f7f89",
+  "creationDate" : "20261014T022944Z",
+  "license" : "Apache-2.0 + Gemma Prohibited Use Policy",
+  "assetVersion" : "2.0"
+}

macos/eg2_reader_fp16_T750.aimodel/main.hash

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macos/eg2_reader_fp16_T750.aimodel/metadata.json

@@ -0,0 +1,8 @@
+{
+  "assetVersion" : "2.0",
+  "author" : "coreai-model-zoo community port; reader trained with CTC on LibriSpeech train-clean-100 (CC BY 4.0); word table: EmbeddingGemma 2 (Google DeepMind) embeddings of the LibriSpeech lexicon",
+  "description" : "EG2 transcribe reader (arch a, 13,268,994 parameters, scale 91.2183) + 200,000-word table: soft tokens [1,750,512] + token_mask -> top-1 word index \/ logit, blank logit; fp16; weights 67a06a857b9a",
+  "producer" : "coreai-core 1.0.0b2",
+  "creationDate" : "20261014T022946Z",
+  "license" : "Apache-2.0; word table from google\/embeddinggemma-2 text embeddings (Gemma Prohibited Use Policy)"
+}

pipeline.json

@@ -0,0 +1,34 @@
+{
+  "schema": 1,
+  "default_form": {"macos": "gpu-ane", "ios": "gpu"},
+  "overlap": {"macos": true, "ios": false},
+  "forms": [
+    {
+      "id": "gpu",
+      "title": "Encoder GPU · reader GPU",
+      "stages": [
+        {"name": "encoder", "bundle": "eg2_audio_fp16_T3000", "compute": "gpu"},
+        {"name": "reader", "bundle": "eg2_reader_fp16_T750", "compute": "gpu"}
+      ]
+    },
+    {
+      "id": "gpu-ane",
+      "title": "Encoder GPU · reader Neural Engine",
+      "platforms": ["macos"],
+      "stages": [
+        {"name": "encoder", "bundle": "eg2_audio_fp16_T3000", "compute": "gpu"},
+        {"name": "reader", "bundle": "eg2_reader_fp16_T750", "compute": "ane"}
+      ]
+    },
+    {
+      "id": "split7-gpu",
+      "title": "Encoder in two bundles (GPU, Neural Engine) · reader GPU",
+      "platforms": ["macos"],
+      "stages": [
+        {"name": "front", "bundle": "eg2_front_layer7_fp32_T3000", "compute": "gpu"},
+        {"name": "body", "bundle": "eg2_body_layer7_fp16_T3000", "compute": "ane"},
+        {"name": "reader", "bundle": "eg2_reader_fp16_T750", "compute": "gpu"}
+      ]
+    }
+  ]
+}

provenance/README.md

@@ -0,0 +1,19 @@
+# Provenance
+
+The records behind the numbers of the card, copied from the conversion's work directory with its paths replaced by
+placeholders (`<work>` = the work directory, `<eg2-transcribe>` = the reader / app repository, `<venv>`, `<home>`).
+`SOURCES.tsv` lists each copy, the record it was copied from, the speed-ladder row it backs, the sha256 of the record
+as written and the sha256 of the copy.
+
+| Folder | Records |
+|---|---|
+| `encoder/` | the fp16 encoder: Mac M4 Max GPU gate and timing; iPhone 18 Pro GPU gate and timing, AOT h19p and JIT |
+| `reader/` | reader v1 + word table, fp16: Mac GPU gate, timing and the zero-half red arm; Mac Neural Engine gate; the iPhone 18 Pro GPU timing of the same compiled graph with an interim checkpoint's weights |
+| `pipeline/` | the 3,257.4 s recording end to end on the Mac (encoder GPU ∥ reader Neural Engine with two windows in flight; encoder GPU → reader GPU one window at a time) and the float32 PyTorch path on the same windows |
+| `strip/` | `strip_<name>.json`: the bundles of `macos/` and `ios/` are the gated bundles with their MLIR debug locations removed (coreai-torch `strip_debug_info`); `aot_h19p_<name>.json`: the bundles of `ios-h19p/` are compiled from those stripped `.aimodel` bundles and hold the same `resources.bin` as the compiles of the gated graphs; `compare_mac.json`: on the Mac the stripped bundles return outputs bit-identical to the gated ones |
+| `app/` | the eg2-transcribe app with reader v1 on this repository's files: on the Mac M4 Max its default form and both bundles on the GPU, on the iPhone 18 Pro its default form and the timing record of its passes (the 3,257.4 s recording, the 20 fixture utterances, launch to transcript after a Core AI cache clear and from the cache); its own `SOURCES.tsv` |
+| `encoder-split7/` | the second encoder form (`macos-encoder-split7-30s/`, Mac only): its gate, timing and measurement window, the one-bundle encoder's timing in that window, the strip records of its two bundles and the Mac compare; its own `README.md` and `SOURCES.tsv` |
+
+The `ios-h19p/` bundles were compiled from the stripped `.aimodel` bundles; the app ran them on the iPhone 18 Pro with
+reader v1 (`app/`: fixture transcripts 20/20 = PyTorch, long-recording WER 5.77 %). The iPhone encoder and reader rows
+of the card were measured on the compiles of the unstripped graphs.

provenance/SOURCES.tsv

@@ -0,0 +1,20 @@
+copy	record (under <work>/exports)	ladder row	sha256 of the record	sha256 of the copy
+encoder/mac-gpu-gate.json	fp16/gate_gpu_T3000.json	1	e4bd1441ccdc69a0494286b4da2d472d8e84c8cc6a848668c4ec719e99273ae3	7a9c355ec9cdbf0f8e8bf145804edc7375eaef4c00ca98e8bdf908e4f75356d2
+encoder/mac-gpu-timing.json	fp16/timing_gpu_T3000.json	1	4f43fcebe68a77e13a81e362ed8b91ab85d0a6daa0e45e2f64962aa1c0f72a1c	d5214fb7990763900bbc3fd38a2b4f3f1de111ed5073cbecd4ff9ea614f21121
+encoder/iphone-gpu-aot-gate.json	fp16/gate_iphone18pro_gpu-aot_T3000.json	27	7ab943d31eca5d05815418a61e5258db7898bce98df0953c1a762c33d036f7f7	c77db323059c8b7e4161617e9d56d85fea6886d827b29d7a96f52b48cda78628
+encoder/iphone-gpu-aot-timing.json	fp16/timing_iphone18pro_gpu-aot_T3000.json	27	0de13d764b7f01a8cf010089c2725f66da5b3ac283c3548caa4ad98a49133a3a	76edfed9e8cdd402a19d3756830bfd6849cf6ab4a0c10091257d4ad032a81790
+encoder/iphone-gpu-jit-gate.json	fp16/gate_iphone18pro_gpu-jit_T3000.json	27a	f6261882be73d43e5af16b29e2afc3745e759f2a6c80db8ada8dbaccdc7d0976	7ee36ee462ee61984a8c34e37454c7efe053f5f1af11a319a8da4e4484803df3
+encoder/iphone-gpu-jit-timing.json	fp16/timing_iphone18pro_gpu-jit_T3000.json	27a	90109c3ee1aad2f9839445e97c0077ee4d9002eb05bff6d0855fc321d11d4a7a	ff5ac9ff98d04cc65662d7e39b6b19c806fca6fa4a48da7da247aa0db5c8bd56
+reader/mac-gpu-gate.json	reader/fp16/gate_gpu_T750.json	53	d27d21887890197932d7bc7a74b54662404886b1faf5a3fa84440522035589f1	79e9a0b6c9abe0aa837dedeb9f9dab42b635c71d5a2fb5f900cb1d985038d770
+reader/mac-gpu-timing.json	reader/fp16/timing_gpu_T750.json	53	fb013eda956e75122b79e089d5d2b0a7b06eaef1c1ec850a1d06e196b3e79661	aee54214b4396b913af784b1e804fd2b6eebd1640a3d2bca465023bcb631399f
+reader/mac-gpu-redarm-zerohalf.json	reader/fp16/redarm_zerohalf_gpu_T750.json	53	32ebfc4c3ab58f54c7773056d255ed692c3ac413cb0682ac7f06cbd804617749	bbb8c9f87fefdb3d7368e22957e678e25606b6b701d571d9b8be0fbb304685d9
+reader/mac-ane-gate.json	reader/fp16/gate_ane_T750.json	56	35e9a78b319462b804ac39a1a80118d75370208309ae0992ff20add2a05849da	54d7c1e932b97423408a3cbc156850a65394851490f52e6ef05045ead27aa69c
+reader/iphone-gpu-aot-timing-interim-weights.json	r4b/timing_iphone18pro_reader_fp16-gpu-aot_T750_r4b20805-t-reader.json	80	7f2ef097540053859dd7e9d1a475727b9f3b341ff13b146ce2f71818e1dfb955	0ee906463e2f13db456b3f1a4afcaead4c27582d7fb3da909d11d8d627346cf5
+pipeline/mac-encoder-gpu-reader-ane-overlap.json	pipeline/pipeline_long_fp16_ane_overlap.json	73	69767253281f3e793cc7b17b4fb513d07ad8eab1e4a99ec00241df142a2fabe8	000974d3a74a96dc7bdda4300b13b6761b00d0370e8b79d2f9d3159ad795e958
+pipeline/mac-encoder-gpu-reader-gpu.json	pipeline/pipeline_long_fp16_gpu.json	69	f40f22063411141c60f9bd59cf056a259f617d64b117e9aae354dcfc3cf4177a	96b74d514caff52dbc4a6f54fada6a3b549d2a62012f1cacc824bd555f9793a3
+pipeline/torch-fp32.json	pipeline/pipeline_long_torch.json	-	77e053d8e1102d5b0044b9404839b4998decfb0b6a52a40170a450b3ce990fe3	00e108772c6e3fdbd6a940e4cf279cb58a9204c28658e9a7893e60e2ca463731
+strip/strip_eg2_audio_fp16_T3000.json	ship/provenance/strip_eg2_audio_fp16_T3000.json	-	5a9a1ac4eb27901bb2c794777d982e133dbbd297ebda045c944116ff12c5cc5f	5a9a1ac4eb27901bb2c794777d982e133dbbd297ebda045c944116ff12c5cc5f
+strip/strip_eg2_reader_fp16_T750.json	ship/provenance/strip_eg2_reader_fp16_T750.json	-	d40f076deeb528cf0ea8686d6465b7ab6968fe7624246cba43b27db2565f7444	d40f076deeb528cf0ea8686d6465b7ab6968fe7624246cba43b27db2565f7444
+strip/aot_h19p_eg2_audio_fp16_T3000.json	ship/provenance/aot_h19p_eg2_audio_fp16_T3000.json	-	92a4b4f98445115dbb8c5ea16d0dc2c1d01a4a1a6ff83466340b63d4f31d402b	92a4b4f98445115dbb8c5ea16d0dc2c1d01a4a1a6ff83466340b63d4f31d402b
+strip/aot_h19p_eg2_reader_fp16_T750.json	ship/provenance/aot_h19p_eg2_reader_fp16_T750.json	-	6c67cc3d21d37ecc6a481d81302b340c44ab56b1cc5a5f85f010a916ace9c53a	6c67cc3d21d37ecc6a481d81302b340c44ab56b1cc5a5f85f010a916ace9c53a
+strip/compare_mac.json	ship/provenance/compare_mac.json	-	ca3d569a7c00d86a6f367bdcdbd9a4865b482520323d00bb600ba6bbbb858f22	ca3d569a7c00d86a6f367bdcdbd9a4865b482520323d00bb600ba6bbbb858f22

provenance/app/SOURCES.tsv

@@ -0,0 +1,5 @@
+copy	record (under <work>/exports)	ladder rows	sha256 of the record	sha256 of the copy
+mac-gpu-ane-gate.json	app/gate_mac_v1_gpu-ane_124853.json	90 91	cf02fab0f6df851ddc902abdec4d0abb344f02fe27e64fb5594bcc813c9c737d	76581ebfc070a026715d4f693d1ca6b4e92ab0fb8c6a003873a51bfd8e6fe207
+mac-gpu-gate.json	app/gate_mac_v1_gpu_124853.json	88 89	8fb7b5eced8672b9caac26c32de5921d013388df09b1e110f9f401910b1ea5db	0090d9b721d76bfc803a1dfa47e947cde28637a81a0c2fee52f66ceb187f9f70
+iphone-gpu-gate.json	app/gate_iphone18pro_v1_r6a1255.json	94 95	8cb3e8104f1cf11ce4a7bb49abdb1dbc99812d0d1a618ea2d641cddf88cd7f91	5ba6b91f0c27f634009e4807fb91f96f0c6abd6ea9bba33a8678b50d1639259e
+iphone-gpu-timing.json	app/timing_iphone18pro_v1_r6a1255.json	94 95	8b87ce36a26a67bf3f78fe51402f92fba495a35722c104e0d35f73950b1b4e3f	8b87ce36a26a67bf3f78fe51402f92fba495a35722c104e0d35f73950b1b4e3f

provenance/app/iphone-gpu-gate.json

@@ -0,0 +1,425 @@
+{
+ "what": "the app's self-test with reader v1 against the r4 bars and the r6a stop conditions",
+ "device": "iphone",
+ "form": "gpu",
+ "inputs": {
+  "gate": "<work>/device/pull/r6a1255-gate/result.json",
+  "long": "<work>/device/pull/r6a1255-long/result.json",
+  "iso": "<work>/exports/app/extractor_isolation_mac_v1_gpu_124853.json",
+  "transcribe": [
+   "<work>/device/pull/r6a1255-cold/result.json",
+   "<work>/device/pull/r6a1255-warm/result.json"
+  ]
+ },
+ "references": {
+  "ref_fixtures": {
+   "file": "ref_fixtures_coreai_path.json",
+   "finished": "2026-10-14T12:48:58+0900",
+   "transcripts_same_as_torch": 20
+  },
+  "torch": "exports/reader/ref_T750.npz, exports/reader/fp16/hyp_gpu_T750.tsv (v1)"
+ },
+ "device_record": {
+  "arch": "h19p",
+  "available_mb": 3521.837312,
+  "footprint_mb": 13.731584,
+  "machine": "iPhone19,2",
+  "os": "Version 27.2 (Build 24B5099f)",
+  "os_build": "24B5099f",
+  "physical_memory_gb": 12.12,
+  "power": {
+   "battery": "charging",
+   "level": 0.800000011920929,
+   "low_power_mode": false
+  },
+  "thermal": "nominal"
+ },
+ "model": {
+  "cache": {
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+   "fingerprint": "2e2e1a1925074c657b1146c7e61043d94b5590aaf6091c8b26f4c452a81be400",
+   "mode": "check",
+   "ms": 1.486666,
+   "previous": "2e2e1a1925074c657b1146c7e61043d94b5590aaf6091c8b26f4c452a81be400"
+  },
+  "folder": "eg2-transcribe",
+  "form": "gpu",
+  "platform_folders": [
+   "ios-h19p"
+  ],
+  "reader_config_version": "v1",
+  "stages": [
+   {
+    "aot": true,
+    "bundle": "eg2_audio_fp16_T3000.aimodelc",
+    "compute": "gpu",
+    "load_s": 0.098403167,
+    "name": "encoder",
+    "warm_up_ms": 97.186084
+   },
+   {
+    "aot": true,
+    "bundle": "eg2_reader_fp16_T750.aimodelc",
+    "compute": "gpu",
+    "load_s": 0.043606917,
+    "name": "reader",
+    "warm_up_ms": 43.432708
+   }
+  ],
+  "vocabulary": 200000,
+  "window": {
+   "drop_last": 1,
+   "frames": 3000,
+   "left_overlap": 96,
+   "tokens": 750
+  }
+ },
+ "fixtures": {
+  "source": "r6a1255-gate",
+  "utterances": 20,
+  "features_max_abs_diff": 4.76837158203125e-07,
+  "fp32_encoder_cos_min_app_features": 0.9999999995026775,
+  "fp32_encoder_cos_min_hf_features": 0.9999999990170414,
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+  "extractor_pass": true,
+  "transcripts_same_as_torch": 20,
+  "mismatches": [],
+  "fixture_pass": true,
+  "fp16_soft_cos_mean_app_features": 0.9999947903463485,
+  "fp16_soft_cos_min_app_features": 0.991114015115895,
+  "fp16_soft_cos_min_reference_features": 0.9962582505090712,
+  "labels_same_both_features": 20,
+  "host_labels_equal_python_gpu_path_on_features_npy": 20
+ },
+ "long": {
+  "source": "r6a1255-long",
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+  "decode_s": 0.488071708,
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+   "fft": 0.5949621569999999,
+   "log": 0.09767911500000007,
+   "mel": 0.22696038400000002,
+   "note": "CPU seconds summed over the parallel blocks of 512 frames: framing + FFT + |X|, mel product, log"
+  },
+  "torch_wer": {
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+   "errors": 572,
+   "ref_words": 9889,
+   "hyp_words": 9930,
+   "engine": "word Levenshtein (numpy); jiwer: bench_pipeline.py score"
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+  "python_path": {
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+   "json": [
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+    "pipeline_long_fp16_gpu_overlap.json"
+   ],
+   "labels": "labels_long_fp16_gpu_overlap.npy"
+  },
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+    "stage_ms_median": [
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+     34.4955
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+      34.4955
+     ],
+     [
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+     "wer": 0.05774092425927799
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+     },
+     {
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+    "labels_differing_from_python": 19,
+    "labels_differing_positions_s": [
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+    "top1_logit_max": 40.53125,
+    "thermal": [
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+      "end": "nominal",
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+     },
+     {
+      "order": 3,
+      "start": "nominal",
+      "end": "nominal",
+      "cooled_s": 0
+     }
+    ]
+   }
+  },
+  "errors": [
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+  "long_vs_torch_pass": true,
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+  "overlap_labels_same_as_sequential": true
+ },
+ "transcribe": [
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+   },
+   "stages": [
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+     "bundle": "eg2_audio_fp16_T3000.aimodelc",
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+     "name": "encoder",
+     "warm_up_ms": 152.656667
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+     "bundle": "eg2_reader_fp16_T750.aimodelc",
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+     "name": "reader",
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+    "text": "he hoped there would be stew for dinner turnips and carrots and bruised potatoes and fat muttered pieces to be ladled out in thick peppered flour fatten sauce"
+   },
+   "device": {
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+    "available_mb": 3521.657112,
+    "footprint_mb": 13.797072,
+    "machine": "iPhone19,2",
+    "os": "Version 27.2 (Build 24B5099f)",
+    "os_build": "24B5099f",
+    "physical_memory_gb": 12.12,
+    "power": {
+     "battery": "charging",
+     "level": 0.800000011920929,
+     "low_power_mode": false
+    },
+    "thermal": "nominal"
+   },
+   "error": null
+  },
+  {
+   "source": "r6a1255-warm",
+   "milestones_s": {
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+    "process_start_to_load_start": 0.08220303058624268,
+    "process_start_to_transcript": 0.5103769302368164,
+    "transcribe": 0.11350893974304199
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+    "previous": "2e2e1a1925074c657b1146c7e61043d94b5590aaf6091c8b26f4c452a81be400"
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+     "aot": true,
+     "bundle": "eg2_audio_fp16_T3000.aimodelc",
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+     "name": "encoder",
+     "warm_up_ms": 98.953916
+    },
+    {
+     "aot": true,
+     "bundle": "eg2_reader_fp16_T750.aimodelc",
+     "compute": "gpu",
+     "load_s": 0.0446345,
+     "name": "reader",
+     "warm_up_ms": 44.191125
+    }
+   ],
+   "result": {
+    "audio_s": 10.435,
+    "decode_s": 0.018186209,
+    "log_mel_s": 0.000815333,
+    "total_s": 0.11349604199999999,
+    "x_realtime": 91.94153219898189,
+    "text": "he hoped there would be stew for dinner turnips and carrots and bruised potatoes and fat muttered pieces to be ladled out in thick peppered flour fatten sauce"
+   },
+   "device": {
+    "arch": "h19p",
+    "available_mb": 3521.657112,
+    "footprint_mb": 13.82984,
+    "machine": "iPhone19,2",
+    "os": "Version 27.2 (Build 24B5099f)",
+    "os_build": "24B5099f",
+    "physical_memory_gb": 12.12,
+    "power": {
+     "battery": "charging",
+     "level": 0.800000011920929,
+     "low_power_mode": false
+    },
+    "thermal": "nominal"
+   },
+   "error": null
+  }
+ ],
+ "verdicts": {
+  "extractor_pass": true,
+  "fixture_pass": true,
+  "long_vs_torch_pass": true,
+  "long_vs_python_pass": true
+ },
+ "pass": true,
+ "finished": "2026-10-14T12:59:51+0900"
+}

provenance/app/iphone-gpu-timing.json

@@ -0,0 +1,390 @@
+{
+ "what": "EG2 Transcribe app (reader v1, the shipped h19p bundles) on the iPhone 18 Pro: start-up with the model-cache check, the long recording, the demo clip, thermal and power",
+ "frame": {
+  "run": "r6a1255",
+  "probe": {
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+   "device": {
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+    "available_mb": 3521.722624,
+    "footprint_mb": 13.551336,
+    "machine": "iPhone19,2",
+    "os": "Version 27.2 (Build 24B5099f)",
+    "os_build": "24B5099f",
+    "physical_memory_gb": 12.12,
+    "power": {
+     "battery": "charging",
+     "level": 0.800000011920929,
+     "low_power_mode": false
+    },
+    "thermal": "nominal"
+   },
+   "model_cache": null,
+   "stages": null,
+   "sampler": {
+    "available_least_mb": 3522.214144,
+    "footprint_peak_mb": 17.778432,
+    "thermal_changes": [],
+    "thermal_end": "nominal"
+   },
+   "transcribe": {
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+    "decode_s": null,
+    "log_mel_s": null,
+    "total_s": null,
+    "x_realtime": null,
+    "text": null
+   },
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+  },
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+    "transcribe": 0.10427403450012207
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+   "device": {
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+    "footprint_mb": 13.797072,
+    "machine": "iPhone19,2",
+    "os": "Version 27.2 (Build 24B5099f)",
+    "os_build": "24B5099f",
+    "physical_memory_gb": 12.12,
+    "power": {
+     "battery": "charging",
+     "level": 0.800000011920929,
+     "low_power_mode": false
+    },
+    "thermal": "nominal"
+   },
+   "model_cache": {
+    "cleared": true,
+    "fingerprint": "2e2e1a1925074c657b1146c7e61043d94b5590aaf6091c8b26f4c452a81be400",
+    "mode": "check",
+    "ms": 11.065791,
+    "previous": null
+   },
+   "stages": [
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+     "compute": "gpu",
+     "load_s": 0.520539584,
+     "name": "encoder",
+     "warm_up_ms": 152.656667
+    },
+    {
+     "aot": true,
+     "bundle": "eg2_reader_fp16_T750.aimodelc",
+     "compute": "gpu",
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+     "name": "reader",
+     "warm_up_ms": 100.124583
+    }
+   ],
+   "sampler": {
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+    "thermal_changes": [],
+    "thermal_end": "nominal"
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+    "x_realtime": 100.08664328542929,
+    "text": "he hoped there would be stew for dinner turnips and carrots and bruised potatoes and fat muttered pieces to be ladled out in thick peppered flour fatten sauce"
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+  },
+  "warm": {
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+    "transcribe": 0.11350893974304199
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+   "device": {
+    "arch": "h19p",
+    "available_mb": 3521.657112,
+    "footprint_mb": 13.82984,
+    "machine": "iPhone19,2",
+    "os": "Version 27.2 (Build 24B5099f)",
+    "os_build": "24B5099f",
+    "physical_memory_gb": 12.12,
+    "power": {
+     "battery": "charging",
+     "level": 0.800000011920929,
+     "low_power_mode": false
+    },
+    "thermal": "nominal"
+   },
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+}

provenance/app/mac-gpu-ane-gate.json

@@ -0,0 +1,420 @@
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provenance/app/mac-gpu-gate.json

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provenance/encoder/iphone-gpu-aot-gate.json

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+}

provenance/encoder/iphone-gpu-aot-timing.json

@@ -0,0 +1,493 @@
+{
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provenance/encoder/iphone-gpu-jit-gate.json

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+}

provenance/encoder/iphone-gpu-jit-timing.json

@@ -0,0 +1,493 @@
+{
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+}

provenance/encoder/mac-gpu-gate.json

@@ -0,0 +1,826 @@
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provenance/encoder/mac-gpu-timing.json

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provenance/pipeline/torch-fp32.json

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provenance/reader/mac-ane-gate.json

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provenance/reader/mac-gpu-gate.json

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provenance/reader/mac-gpu-redarm-zerohalf.json

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provenance/reader/mac-gpu-timing.json

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+}

reader_config.json

@@ -0,0 +1,79 @@
+{
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+  "dtype": "float32",
+  "what": "EmbeddingGemma 2 audio soft tokens (embed_audio output), 25 per second"
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+  "heads": 8,
+  "ffn": 2048,
+  "activation": "gelu",
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+  "positions": "rotary (base 10000)",
+  "input": "LayerNorm -> Linear 512->512",
+  "output": "LayerNorm -> Linear 512->768",
+  "params": 13268994
+ },
+ "output": {
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+  "blank_logit": "[T]"
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+ "logits": "scale * cos(vector, table[w]) for w in vocab; blank from blank_logit",
+ "scale": 91.21830749511719,
+ "vocab": "vocab.txt",
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+  "dtype": "float16",
+  "shape": "[200000, 768]",
+  "prompt": "task: search result | query: ",
+  "normalized": true
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+  "left_overlap_tokens": 96,
+  "right_extra_tokens": 1,
+  "note": "encoder is causal; the 83 tokens at the start of a window and its last token differ from a continuous run (fixtures/long-speckled-band: soft_tokens_w30 vs soft_tokens_continuous)"
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+ "weights": {
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+  "format": "torch state_dict, float32",
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+}