gsmlg/embeddinggemma-2

EmbeddingGemma 2 — text embedding ONNX mirror

Text-embedding subset of EmbeddingGemma 2 by Google DeepMind, mirrored for on-device use with lib_onnx (ONNX Runtime for Dart/Flutter).

Files are unmodified copies of:

Files	Source	Revision
onnx/model*.onnx, onnx/model*.onnx_data, config.json, config_sentence_transformers.json, tokenizer.json, tokenizer_config.json	onnx-community/embeddinggemma-2-ONNX	daa72c51243991dfcaf9f9137d2c573d8f7790c0
tokenizer.model (SentencePiece)	google/embeddinggemma-2	f9972ac22e696a105c482202646979e33e4b5a1e

The vision and audio encoders are not mirrored. License: Apache 2.0, as published by the upstream repositories (Gemma license).

Model contract

Input	Type	Shape	Text-only value
input_ids	int64	[batch, sequence]	Gemma tokenizer ids
attention_mask	int64	[batch, sequence]	1 for tokens, 0 for padding
image_features	float32	[num_image_tokens, 512]	empty [0, 512]
video_features	float32	[num_video_tokens, 512]	empty [0, 512]
audio_features	float32	[num_audio_tokens, 512]	empty [0, 512]

Output	Shape	Meaning
sentence_embedding	[batch, 768]	pooled, projected embedding (L2-normalize before cosine/dot)
last_hidden_state	[batch, sequence, 768]	per-token states

Matryoshka truncation to 512, 256 or 128 dimensions is supported: keep the leading dimensions and re-normalize.

Prefix text inputs with a task instruction:

• query: task: search result | query: {text}
• document: title: {title or none} | text: {text}
• symmetric tasks: task: sentence similarity | query: {text} (also classification, clustering)

Variants

File	Precision	Size (data)
onnx/model_q4f16.onnx	4-bit weights, fp16	157 MB
onnx/model_q4.onnx	4-bit weights	174 MB
onnx/model_quantized.onnx	int8	314 MB
onnx/model_fp16.onnx	fp16	542 MB
onnx/model.onnx	fp32	1.08 GB

Each .onnx graph loads its .onnx_data external-data file from the same directory.

SHA-256

4d777ef5bdc1aa36227abdfb77c3e49e7b9c892d16e1b6bda41c393504828be4  tokenizer.json
e594c8a90eb08d8bda498ff4747977dc827ae0c3c56b5c0d41a605a22d02ef03  tokenizer.model
a49e227d7e0e5f4ee606d79879d084264366367bc6318f344ab179e35d4fd6a4  onnx/model_fp16.onnx
b9dbe09415d77c8686ba6e216a244086f4ada1608238c13f2bfaa587ba627207  onnx/model_fp16.onnx_data
f9eeba97acddf139b8ee2ddf04bc30dceafa88de93fadf74d7644e0d61a477a9  onnx/model_q4.onnx
c3975f2d1ab7a1878ae31a7d7a9b7804a827aff3800b60dfceafce21cac3df49  onnx/model_q4.onnx_data
53feeced79582d661e30adeaa9829cea90d92b1c78347e26fd123773580f71d0  onnx/model_q4f16.onnx
c39fbaa1fb4221f04beb82786a06b81999c514fd39e84fcd58ef79413c87fa56  onnx/model_q4f16.onnx_data
d06edd601f851c633a2519304cbeb8dc6170d7ceb61b436625c17fb9b6e74953  onnx/model_quantized.onnx
278a7ff1248c3618e4bd11a607fc54f7bdc7778854230f3956d3f86bd9db4f3b  onnx/model_quantized.onnx_data
bc47de15f81208a5c99e5ab10f746d5e33b51ea228b7dc0bef9c133a94f1c1c3  onnx/model.onnx
9fd452bfc6916e5a92f080f8dada35598ca4512b21c65897d4c5974d5f9630a2  onnx/model.onnx_data