EmbeddingGemma 2 Core ML v1.0.0

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+b280/EmbeddingGemma2.aar filter=lfs diff=lfs merge=lfs -text
+b280/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+b70/EmbeddingGemma2.aar filter=lfs diff=lfs merge=lfs -text
+b70/tokenizer/tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

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NOTICE

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+embeddinggemma-coreml
+Copyright 2026 Delavalom
+
+This product includes software developed by third parties:
+
+- EmbeddingGemma 2 (google/embeddinggemma-2, revision
+  914f7f89142e33e77833254d9c9b90c3cef7303b), model weights, architecture and
+  tokenizer, by Google DeepMind, licensed under the Apache License, Version 2.0.
+  https://huggingface.co/google/embeddinggemma-2
+  The Core ML model published from this repository (EmbeddingGemma2.mlpackage)
+  is a modified version of those weights. Modified by Delavalom, 2026:
+  re-implemented in PyTorch, converted to Core ML, cast to FP16, and compressed
+  (int8 per-channel convolution weights, int4 per-block embedding table). The
+  tokenizer files are redistributed unmodified.
+  The upstream model card states: "Deployments must adhere to the Gemma
+  Prohibited Use Policy" (https://ai.google.dev/gemma/prohibited_use_policy).
+
+- Reference code in vendor/reference/ is copied unmodified from Hugging Face
+  Transformers, Copyright The Hugging Face Team, licensed under the Apache
+  License, Version 2.0. It is kept for reading only and is never imported.
+  https://github.com/huggingface/transformers (see vendor/reference/SOURCE.txt
+  for the exact commit).
+
+- tests/upstream.py is a modified copy of functions from the same Hugging Face
+  Transformers release (v5.20.2: modeling_gemma4.py,
+  modeling_embedding_gemma2.py, image_processing_gemma4.py), Copyright The
+  Hugging Face Team, licensed under the Apache License, Version 2.0. Modified by
+  Delavalom, 2026: the minimal functions were extracted into one test-only
+  module, with config lookups replaced by arguments; the maths is unchanged.
+  It is used only as a test oracle and is not shipped.
+
+- tests/upstream_towers.py is a modified copy of the tower modules from the same
+  release (modeling_embedding_gemma2.py, modeling_gemma4.py,
+  image_processing_gemma4.py), Copyright The Hugging Face Team, licensed under
+  the Apache License, Version 2.0. Modified by Delavalom, 2026: config objects
+  replaced by arguments, masks built inline, no output records or kernels; the
+  maths is unchanged. It is used only as a test oracle and is not shipped.

README.md

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+---
+license: apache-2.0
+base_model: google/embeddinggemma-2
+base_model_relation: quantized
+library_name: coreml
+pipeline_tag: feature-extraction
+tags: [coreml, embeddings, multimodal-embedding, image-text, sentence-similarity, ios, macos, neural-engine]
+---
+
+<!-- Rendered by stages/s7_publish (card.py + model_card.md) from the stage reports. Do not edit on the Hub. -->
+
+# EmbeddingGemma 2 for Core ML
+
+This is [`google/embeddinggemma-2`](https://huggingface.co/google/embeddinggemma-2) (Google DeepMind's text and image embedding model, one
+shared 768-d space) converted to **multifunction Core ML models that run on the Apple Neural Engine**, one per image
+budget. Every stage of the conversion was checked against the original PyTorch model. It is an unofficial conversion. It is not made or
+endorsed by Google.
+
+- **Base model:** `google/embeddinggemma-2` at revision `914f7f89142e33e77833254d9c9b90c3cef7303b` (`model.safetensors` sha256 `197a32965d4b1105faf060417baa899e193fb73cd401f42ec9295234d5553d79`).
+- **Modalities:** text and images. Audio and video are not converted.
+- **Variant shipped:** `int8_table4`: int8 per-channel conv weights, int4 per-block (32) table; tables and norms FP16.
+- **Layout:** one folder per image budget, `b70/` and `b280/`. Each holds the text functions and that budget's image
+  functions; a device uses one budget only, so it downloads one folder's archive.
+- **Size:** apps download one file, `b<budget>/EmbeddingGemma2.aar` (an LZFSE Apple Archive of the package and the tokenizer:
+  343.2 MB for budget 70, 352.9 MB for budget 280; the s4 gate is 360 MB for the
+  larger), described by `b<budget>/manifest.json` (size, SHA-256 and image budget). Unpacked it is 435.0 /
+  454.9 MB; the package alone is 402.8 / 422.8 MB. The same files are also in each
+  folder unpacked, each listed with its size and SHA-256 in `b<budget>/files.json`.
+- **Runs on:** iOS 18+ and macOS 15+ (the model uses `ios18` ops). It is built for `.cpuAndNeuralEngine`.
+- **Swift package:** [`EmbeddingGemma`](https://github.com/Delavalom/embeddinggemma-coreml) (download, verification,
+  tokenizer, image preprocessing, plus an on-disk vector index). See "Use from Swift" below.
+
+## License and terms
+
+- **The weights in this repo are a modified version of `google/embeddinggemma-2`, under the Apache License, Version 2.0.**
+  This is the license the upstream model card declares at the pinned revision (`license: apache-2.0`, linking
+  Google's [Gemma 4 license](https://ai.google.dev/gemma/docs/gemma_4_license), which is the Apache 2.0 text).
+  The original model is by Google DeepMind. See `LICENSE` (the full Apache 2.0 text) and `NOTICE`.
+- **Changes made (Apache 2.0, section 4(b)):** the original weights were re-implemented in PyTorch, converted to Core
+  ML, cast to FP16, and compressed (int8 per-channel convolution weights, int4 per-block embedding table). The
+  vectors are close to the original's but not identical (see "Accuracy").
+- **Upstream use policy, passed through:** the upstream model card says *"Deployments must adhere to the
+  [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy)."* That requirement comes from
+  the upstream model, and it applies to this conversion too. The upstream card also says developers are responsible
+  for application-level safeguards (retrieval filtering, fairness testing) suited to their use case. Read
+  Google's [model card](https://huggingface.co/google/embeddinggemma-2) for its intended uses, limitations and ethical
+  considerations. They all apply here.
+- The tokenizer files (`tokenizer/tokenizer.json`, `tokenizer/tokenizer_config.json`) are unmodified copies from
+  the upstream revision above.
+- The conversion code and the Swift package are Apache-2.0 too (the GitHub repo's `LICENSE` and `NOTICE`).
+
+## Functions
+
+One `.mlpackage` per image budget, `b70/EmbeddingGemma2.mlpackage` and `b280/EmbeddingGemma2.mlpackage`, each with
+nine functions with fixed shapes: the four text functions (the same in both) and the five image functions of its
+budget. Both were split from one 14-function package, and every function gives the same output bytes as there.
+Every function returns `embedding`, float32 `[1, 768]`, L2-normalized.
+
+| function | input | tokens |
+|---|---|---|
+| `embed_text_16`, `_32`, `_64`, `_128` | `input_ids` int32 `[1, L]`, `attention_mask` int32 `[1, L]` (1 = token, 0 = padding) | L = 16 / 32 / 64 / 128 |
+| `embed_image_70_{tall,portrait,square,landscape,wide}` (in `b70/`) | `pixels` `[1, 3, H, W]`, RGB in 0-1 | 60-70 image tokens |
+| `embed_image_280_{tall,portrait,square,landscape,wide}` (in `b280/`) | `pixels` `[1, 3, H, W]`, RGB in 0-1 | 256-280 image tokens |
+
+Image shapes (H x W). The bucket comes from the aspect ratio r = width / height: tall r < 0.6, portrait r < 0.83,
+square r <= 1.2, landscape r <= 1.67, wide above that.
+
+| budget | tall | portrait | square | landscape | wide |
+|---|---|---|---|---|---|
+| 70 | 576 x 240 | 480 x 336 | 384 x 384 | 336 x 480 | 240 x 576 |
+| 280 | 1152 x 528 | 960 x 672 | 768 x 768 | 672 x 960 | 528 x 1152 |
+
+- **Text** is `<bos>` + tokens(prompt + text) + `<eos>`, padded with `<pad>` (id 0) to the smallest length that
+  fits. Longer inputs are cut to 128 tokens, keeping `<bos>` and `<eos>` (the upstream model allows up to 8K
+  tokens). Use the upstream prompts: `task: search result | query: ` for queries, `title: none | text: ` for
+  documents (also `task: classification | query: ` and `task: clustering | query: `).
+- **Images** are resized straight to the bucket size (no crop or padding). Use 8-bit sRGB, antialiased bicubic
+  resampling, and scale by 1/255 with no mean or std normalization, as the reference processor does. The Swift
+  package does this.
+- **Matryoshka:** you can truncate the 768-d output to 512, 256 or 128 dims and re-normalize. The Swift package
+  defaults to 256. All accuracy numbers below are at 768 dims.
+
+## Accuracy against the original model
+
+Measured by the pipeline on 200 hand-written texts (120 of them search queries) and 200 images (CC0 and public
+domain photos from Wikimedia Commons, plus synthetic screenshots, memes and receipts; 14 categories), at both image
+budgets. Cosine values are the **minimum** over all inputs, not the mean.
+
+| stage | what is compared | min cosine, text | min cosine, image |
+|---|---|---|---|
+| s1 | our PyTorch rewrite (FP32) vs the official `sentence-transformers` model | 1 - 1.4e-12 | 1 - 1.0e-08 |
+| s2 | Core ML FP32 vs the official model | 1 - 1.2e-12 | 1 - 1.9e-08 |
+| s3 | Core ML FP16 on the Neural Engine vs the official model (at least 99.86% of ops on the Neural Engine) | 0.99996 | 0.99404 (1st percentile 0.99848) |
+| s4 | **shipped `int8_table4`** on `CPU_AND_NE` vs the official model | **0.99791** | **0.99133** (1st percentile 0.99539) |
+| s5 | the Swift package end to end on a Mac (tokenizer + preprocessing + model) vs the official model on the same decoded pixels; vs Python Core ML on identical inputs: 1 - 1.0e-15 | 0.99017 (text and image) | |
+
+Retrieval with the shipped variant (the 120 queries ranking the 200 images, budget 70 / 280):
+
+| metric | budget 70 | budget 280 |
+|---|---|---|
+| text-to-image recall@10 vs the FP reference on the same bucketed images | 0.962 | 0.966 |
+| 5th-percentile Spearman of the top 50, same reference | 0.966 | 0.972 |
+| text-to-image recall@10 vs the exact-aspect FP model (recorded, not gated) | 0.844 | 0.874 |
+
+For 98.3% of queries (worst budget), the exact-aspect model's top-1 image is in the shipped model's top
+10. Image-to-image precision@10 by category label is at most 0.012 below the exact-aspect FP model.
+Swift tokenization matches the reference ids for every fixture text (`True`). The 99th-percentile pixel
+difference against the reference preprocessing is 0.000000 (0-1 units).
+
+**Caveats:**
+
+- **The retrieval gates use a bucketed FP reference.** The upstream processor resizes each image to its own
+  aspect-preserving size. This model has five fixed shapes per budget, so even with no compression, the bucketed
+  vector differs from the exact-aspect one (min cosine 0.916 at budget 70, 0.893 at 280).
+  That is why recall@10 against the exact-aspect model is lower than recall@10 against the bucketed reference. The
+  compression and FP16 gates (min cosine >= 0.99, recall@10 >= 0.95, Spearman >= 0.9) are therefore measured
+  against the FP model on the same bucketed images. Bucketing gets its own task-level gates (top-1 in top 10
+  >= 0.95, category precision@10 drop <= 0.03). Measured by category labels, bucketing costs no retrieval
+  quality. Details: ADR-0007 in the GitHub repo.
+- **Two images fall below the FP16 bar on the Neural Engine.** In FP16 on the Neural Engine, fixture images i141
+  and i161 reach 0.99404 / 0.99405 cosine. The other 398 are at 0.998 or above. On the GPU these two images pass,
+  but the GPU is 2.8x slower at budget 70. They change the top 10 of only a few queries: with only those two
+  vectors swapped in, recall@10 stays at 0.9975 (budget 70) and 0.9992 (budget 280). They were accepted at task
+  level, and images stay on the Neural Engine. The lowest image cosines of the shipped variant are i161 0.99133 (`embed_image_280_landscape`), i141 0.99322 (`embed_image_70_wide`), i165 0.99336 (`embed_image_280_portrait`).
+  Details: ADR-0006.
+- The test set is small: 200 texts, 200 images. Multilingual text, code and long documents were not tested
+  separately. For quality on standard benchmarks, see Google's model card. This conversion only claims closeness
+  to the original.
+
+## Speed and memory
+
+**Mac** (s4, `CPU_AND_NE`, median of 20 runs after warm-up). These were measured on the build Mac; the v1.0.0
+build Mac is an M1 Pro with macOS 26. `embed_text_16` takes 4.4 ms and `embed_image_70_square` takes
+43.8 ms. In every function, at least 99.81% of the ops are placed on the Neural Engine (`MLComputePlan`, minimum
+over the 14 functions, measured on the package the two budget packages were split from).
+
+**iPhone** (s6, PhotoSearchDemo's benchmark: each iPhone runs its own budget, `ImageBudget.auto`, 280 on A17 Pro
+and newer and 70 otherwise: the newest 1,000 photos at 70 or 200 at 280, and 20 queries; times cover preprocessing +
+prediction):
+
+| device | iOS | ms per image, 70 (median) | ms per image, 280 (median) | ms per query (median) | peak memory, MB |
+|---|---|---|---|---|---|
+| iPhone14,3 | 27.0.1 | 64.0 | not run | 6.7 | 630 |
+
+Worst device: 64.0 ms per image at 70, not measured (no A17 Pro or newer iPhone ran s6 yet) at 280, 6.7 ms per query, peak 630 MB (the
+gate is 800 MB; the target for budget 70 is 60 ms, which is recorded, not gated).
+
+**Storage:** the Swift package downloads the archive of the device's image budget, `b<budget>/EmbeddingGemma2.aar`
+(343.2 MB at 70, 352.9 MB at 280, resumable), checks its size and SHA-256 against
+`b<budget>/manifest.json`, unpacks it (435.0 / 454.9 MB) with the AppleArchive framework and
+compiles the package. It needs free space for the archive, the unpacked files and the compiled model while it
+installs; afterwards it keeps the compiled model and the tokenizer. The s4 gate measures the larger archive,
+352.9 MB (limit 360 MB).
+
+## Use from Swift
+
+```swift
+// Package.swift: .package(url: "https://github.com/Delavalom/embeddinggemma-coreml", from: "1.0.0")
+import EmbeddingGemma
+import EmbeddingIndex
+
+let model = try await EmbeddingGemma.load(.huggingFace(revision: EmbeddingGemma.defaultRevision))
+let photo = try await model.embed(image: cgImage, orientation: .up)  // [Float], 256-d by default, normalized
+let query = try await model.embed(query: "receipts from last week")
+```
+
+The package pins this repo (`delavalom/embeddinggemma-2-coreml`) at a commit and the SHA-256 of each budget's `manifest.json`, which pins its
+archive's size and SHA-256. `budget: .auto` picks 280 tokens on A17 Pro and newer iPhones and on Macs, and 70 on
+other devices, and loads that budget's folder (`b280/` or `b70/`) only.
+
+## Use from Python (coremltools, macOS 15+)
+
+```python
+import numpy as np, coremltools as ct
+from tokenizers import Tokenizer
+from torchvision.transforms.functional import pil_to_tensor, resize, InterpolationMode
+from PIL import Image
+
+BUDGET = 280  # the folder you downloaded: b280/ or b70/; its text functions are the same in both
+PKG = f"b{BUDGET}/EmbeddingGemma2.mlpackage"
+tok = Tokenizer.from_file(f"b{BUDGET}/tokenizer/tokenizer.json")
+# self-check: the layout the model was traced with is <bos> ... <eos>
+assert tok.encode("task: search result | query: screenshots of tweets").ids == \
+    [2, 8071, 236787, 3927, 1354, 1109, 7609, 236787, 75490, 529, 46331, 1]
+
+def fn(name):
+    return ct.models.MLModel(PKG, function_name=name, compute_units=ct.ComputeUnit.CPU_AND_NE)
+
+def embed_text(text, prompt="task: search result | query: "):
+    ids = tok.encode(prompt + text).ids
+    ids = ids if len(ids) <= 128 else ids[:127] + [ids[-1]]
+    n = next(L for L in (16, 32, 64, 128) if L >= len(ids))
+    x = np.zeros((1, n), np.int32); x[0, :len(ids)] = ids
+    m = np.zeros((1, n), np.int32); m[0, :len(ids)] = 1
+    return fn(f"embed_text_{n}").predict({"input_ids": x, "attention_mask": m})["embedding"][0]
+
+SHAPES = {70: {"tall": (576, 240), "portrait": (480, 336), "square": (384, 384), "landscape": (336, 480),
+               "wide": (240, 576)},
+          280: {"tall": (1152, 528), "portrait": (960, 672), "square": (768, 768), "landscape": (672, 960),
+                "wide": (528, 1152)}}
+
+def bucket(r):  # r = width / height
+    return ("tall" if r < 0.6 else "portrait" if r < 0.83 else "square" if r <= 1.2
+            else "landscape" if r <= 1.67 else "wide")
+
+def embed_image(path):
+    img = Image.open(path).convert("RGB")  # like the reference: 8-bit RGB, ICC profile ignored
+    name = bucket(img.width / img.height)
+    px = resize(pil_to_tensor(img), list(SHAPES[BUDGET][name]), InterpolationMode.BICUBIC, antialias=True)
+    px = px.float() / 255
+    return fn(f"embed_image_{BUDGET}_{name}").predict({"pixels": px[None].numpy()})["embedding"][0]
+
+def truncate(v, dims=256):  # Matryoshka
+    v = v[:dims]; return v / np.linalg.norm(v)
+```
+
+The model is compiled each time `MLModel` is created. To make many calls, keep one instance per function. The
+Python path is not gated; the gated path is the Swift package (s5).
+
+## Reproducibility
+
+The model was built by an 8-stage pipeline in the GitHub repo. Each stage writes `artifacts/<stage>/report.json`,
+which is checked against `gates.toml`. A stage counts only if its report passed and is current. Each report
+records `git_sha` and `code_hash` (sha256 over the stage's code, the lock file and its gate table), and
+`inputs_sha256` chains it to the reports it read. The reports behind this release:
+
+| stage | status | git_sha | code_hash |
+|---|---|---|---|
+| s0_reference | passed | `59b9bf9863cb` | `9a6a53c2abe21038` |
+| s1_rewrite | passed | `7645eff4d951` | `e98a54bc6bbc6abf` |
+| s2_coreml_fp32 | passed | `82b02a4da1ec` | `21bb94d9d33d0230` |
+| s3_coreml_ane | passed | `8e33af79825f` | `edb80276335be2a4` |
+| s4_compress | passed | `b9064e9abb2b` | `e68c9ca0822062d0` |
+| s5_swift | passed | `90679438068a` | `464d01a178d66eb5` |
+| s6_device | passed | `d5b2e6edc33e` | `70b253e29d57b0ce` |
+
+Reference environment: transformers 5.20.2, sentence-transformers 6.2.0; conversion:
+coremltools 9.0. To rebuild: `just setup`, then `just stage 0` through `just stage 5` (`just stage 6` needs
+an iPhone). Numbers measured on the CPU and the Neural Engine can
+vary slightly from run to run.
+
+# Results
+
+Generated by `python -m scripts.gen_results` from `artifacts/*/report.json` and `docs/results/*.json`. Do not edit by hand.
+
+Model: `google/embeddinggemma-2` at revision `914f7f89142e33e77833254d9c9b90c3cef7303b`.
+
+## Gates
+
+| stage | status | metric | value | gate | ok |
+|---|---|---|---|---|---|
+| s0_reference | passed | license_apache | true | == true | yes |
+| s0_reference | passed | key_inventory_written | true | == true | yes |
+| s0_reference | passed | deterministic | true | == true | yes |
+| s1_rewrite | passed | strict_load | true | == true | yes |
+| s1_rewrite | passed | min_cosine_text | 1 - 1.4e-12 | >= 0.9999 | yes |
+| s1_rewrite | passed | min_cosine_image | 1 - 1e-08 | >= 0.9999 | yes |
+| s2_coreml_fp32 | passed | min_cosine_text | 1 - 1.2e-12 | >= 0.9999 | yes |
+| s2_coreml_fp32 | passed | min_cosine_image | 1 - 1.9e-08 | >= 0.9999 | yes |
+| s3_coreml_ane | passed | min_cosine_text | 0.999965 | >= 0.995 | yes |
+| s3_coreml_ane | passed | min_cosine_image | 0.994037 | >= 0.99 | yes |
+| s3_coreml_ane | passed | min_p1_cosine_image | 0.998482 | >= 0.995 | yes |
+| s3_coreml_ane | passed | min_recall_at_10 | 0.9675 | >= 0.95 | yes |
+| s3_coreml_ane | passed | min_p5_spearman_top50 | 0.987025 | >= 0.9 | yes |
+| s3_coreml_ane | passed | min_ane_fraction | 0.99864 | >= 0.95 | yes |
+| s4_compress | passed | min_cosine | 0.991329 | >= 0.99 | yes |
+| s4_compress | passed | min_recall_at_10 | 0.961667 | >= 0.95 | yes |
+| s4_compress | passed | min_p5_spearman_top50 | 0.966043 | >= 0.9 | yes |
+| s4_compress | passed | min_t2i_top1_in_top10 | 0.983333 | >= 0.95 | yes |
+| s4_compress | passed | max_i2i_label_p10_drop | 0.012 | <= 0.03 | yes |
+| s4_compress | passed | max_download_mb | 352.911 | <= 360 | yes |
+| s5_swift | passed | token_ids_identical | true | == true | yes |
+| s5_swift | passed | max_pixel_abs_diff | 0 | <= 0.003922 | yes |
+| s5_swift | passed | min_cosine_runtime | 1 - 1e-15 | >= 0.9999 | yes |
+| s5_swift | passed | min_cosine | 0.990173 | >= 0.99 | yes |
+| s5_swift | passed | min_recall_at_10 | 0.9575 | >= 0.95 | yes |
+| s5_swift | passed | min_p5_spearman_top50 | 0.96449 | >= 0.9 | yes |
+| s5_swift | passed | min_t2i_top1_in_top10 | 0.983333 | >= 0.95 | yes |
+| s5_swift | passed | max_i2i_label_p10_drop | 0.0065 | <= 0.03 | yes |
+| s6_device | passed | max_peak_rss_mb | 630.065 | <= 800 | yes |
+| s6_device | passed | max_cold_compile_peak_mb | 1517.66 | <= 2100 | yes |
+| s6_device | passed | min_devices | 1 | >= 1 | yes |
+| s7_publish | not run | - | - | - | - |
+
+## s4 variants
+
+Chosen: `int8_table4` (winner: `int8_table4`). Variants are tried smallest first; the first that passes every gate wins, so later ones may not have run. Download limit: 360 MB.
+
+| variant | download MB | package MB | min cosine | recall@10 | p5 Spearman top-50 | exact-aspect top-1 in top 10 | i2i p@10 drop | min ANE fraction | recall@10 vs exact-aspect | passes | description |
+|---|---|---|---|---|---|---|---|---|---|---|---|
+| int8_table4 | 358.669 | 452.4 | 0.991329 | 0.961667 | 0.966043 | 0.983333 | 0.012 | 0.998131 | 0.844167 | all | int8 per-channel conv weights, int4 per-block (32) table; tables and norms FP16 |
+
+Shipped per image budget (download MB = the device archive; `max_download_mb` is the larger): budget 70 `EmbeddingGemma2-b70.aar` 343.178 MB (9 functions), budget 280 `EmbeddingGemma2-b280.aar` 352.911 MB (9 functions). Same outputs as the 14-function package (CPU_ONLY, bitwise): True.
+
+## s5 Swift parity
+
+Pixels (0-1 units; PNG and synthetic are gated, JPEG includes the decoder and is recorded only, ADR-0004):
+
+| inputs | p99 | max | bit-exact |
+|---|---|---|---|
+| png | 0 | 0 | 60/60 |
+| synthetic | 0 | 0 | 20/20 |
+| jpeg | 0.00784314 | 0.121569 | 0/340 |
+
+| comparison | min cosine | recall@10 (worst budget) | p5 Spearman top-50 |
+|---|---|---|---|
+| Swift vs Python Core ML, same inputs (gated) | 1 - 1e-15 | - | - |
+| Swift vs FP reference on Swift's pixels (gated) | 0.990173 | 0.9575 | 0.96449 |
+| Swift vs s0 (recorded) | 0.971191 | 0.946667 | 0.952845 |
+| FP on Swift's pixels vs s0: the decoder (recorded) | 0.971614 | 0.968333 | 0.969143 |
+
+Against s0 (recorded only; on JPEGs it includes the decoder, ADR-0008):
+
+| vectors | n | min cosine | mean cosine |
+|---|---|---|---|
+| text query | 120 | 0.998874 | 0.99951 |
+| text document | 80 | 0.997913 | 0.999262 |
+| image 70_jpeg | 170 | 0.978874 | 0.999002 |
+| image 70_png | 30 | 0.995402 | 0.99898 |
+| image 280_jpeg | 170 | 0.971191 | 0.998763 |
+| image 280_png | 30 | 0.999682 | 0.999764 |
+
+## Device benchmarks (s6)
+
+| source | device | status | key metrics |
+|---|---|---|---|
+| artifacts/s6_device/report.json | iPhone14,3 | passed | max_peak_rss_mb=630.065, max_cold_compile_peak_mb=1517.66, min_devices=1 |
+| docs/results/s6_iPhone14,3.json | iPhone14,3 | passed | max_peak_rss_mb=630.065, max_cold_compile_peak_mb=1517.66, min_devices=1 |
+
+## Limitations
+
+- Text and images only. Text is limited to 128 tokens; images are limited to budgets of 70 or 280 tokens and five
+  fixed aspect buckets each.
+- Results are close to the original model, not bit-identical (see "Accuracy"). Do not mix vectors from this model
+  with vectors from the PyTorch model or another conversion in the same index. The Swift index stores the model
+  revision for that reason.
+- Intel Macs have no Neural Engine; they run on the CPU/GPU and are slower. The fastest settings are on the Neural
+  Engine with `.cpuAndNeuralEngine`.
+- The first load compiles the model on the device, which takes a while. The Swift package caches the compiled
+  model.
+- Everything in the upstream card's "Limitations" and "Ethical Considerations and Risks" applies.
+
+## Citation
+
+Please cite the original model as Google DeepMind's model card asks
+([`google/embeddinggemma-2`](https://huggingface.co/google/embeddinggemma-2)). For this conversion:
+
+```bibtex
+@software{embeddinggemma_coreml,
+  title  = {EmbeddingGemma 2 for Core ML},
+  author = {Delavalom},
+  year   = {2026},
+  url    = {https://huggingface.co/delavalom/embeddinggemma-2-coreml},
+  note   = {Core ML conversion of google/embeddinggemma-2}
+}
+```

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