Models › embeddinggemma-2 family

embeddinggemma-2

google Apache 2.0 out 41 days ago

744M parameters, text in, embeddings out.

Verified 2026-10-25 Rank #47 in Embeddings · score 89.9 method v1 Sources 3 Downloads 30d 206.9k · #248 of 619 tracked

Changed this week: 42 new quantized builds on the Hub (115 in all).2026-10-24 · 40 new quantized builds on the Hub (113 in all).2026-10-24

Can I run it?0.4GB at INT4Estimatefits a 16 GB cardmethod
Can I use it?Apache 2.0Permissive; patent grantlicence file
Does it work?67.53on MTEB#10 of 64 measuredleaderboard2026-10-24
Do people use it?206.9kdownloads, 30 days#248 of 619 we track · top 40%+323 likes, downloads +31% this week2,882 likesHugging Face

Worth reading and watching

Picked by rule from 14 tracked pieces: the lab's own post, the most shared, a field report, the latest.

The lab's own post

EmbeddingGemma 2: An open, lightweight multimodal embedding model

Google launches EmbeddingGemma 2, an open multimodal model expanding beyond text to unify code, images, video, and audio.

  • Developers can dynamically truncate output vectors from 768 dimensions down to 512, 256, or 128 dimensions.
  • The model processes up to 5.5 minutes of audio, 29 images, or 58 video frames within its 8K token context window.
blog.google 2026-10-06 429 HN pts · 46 comments 678 words
Latest · 17 days ago

Embedding Gemma2 Use Cases

A GitHub project ports four jeffhub use cases to Google's EmbeddingGemma-2, replacing the original decider with cosine similarity scoring.

  • The ONNX build loads all three encoders (text + vision + audio) by default.
  • A text-only deployment could ship just onnx/model_quantized.* to save space.
github.com 2026-10-08 2 HN pts 845 words

14 best of 14 tracked

The 2 picks above, plus these 12. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleEmbeddingGemma 2developers.googleblog.com · 2026-10-06 · 9 HN pts · official
  2. ArticleEmbeddingGemma 2simonwillison.net · 2026-10-07 · 1 HN pts
  3. DiscussionGoogle EmbeddingGemma 2twitter.com · 2026-10-06 · 11 HN pts
  4. ArticleEmbeddingGemma 2 model card - Google AI for Developersai.google.dev · undated · official
  5. ArticleGenerating Embeddings with EmbeddingGemma 2 and Sentence Transformers  |  Google AI for Developersai.google.dev · undated · official
  6. VideoEmbeddingGemma 2: An open model for natively multimodal embeddings [video]youtube.com · 2026-10-06 · 2 HN pts
  7. VideoEmbeddingGemma 2: Google's Offline Search Beyond Textyoutube.com · 2026-10-07 · 1 HN pts
  8. ArticleGoogle Launches EmbeddingGemma 2 for On-Device Multimodal Retrieval | AccessAllGPT Researchaccessallgpt.com · 2026-10-07
  9. ArticleEmbeddingGemma 2: Benchmarks, Specs and How to Run It | CellCogcellcog.ai · 2026-10-06
  10. ArticleEmbeddingGemma 2: Open Multimodal Embeddings | S5 Labss5labs.io · 2026-10-06
  11. ArticleEmbeddingGemma 2 | AI Wikiaiwiki.ai · undated
  12. ArticleEmbeddingGemma 2 Review (2026): Honest Verdict on ...kompozy.io · undated

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

No API provider serves it, so its downloads are people running it themselves: 115 quantized builds.

  • As a service0no API provider listed
  • On own hardware115quantized builds
  • Built on137130 fine-tunes · 7 adapters · 0 merges
  • In apps0Hugging Face Spaces

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Fits a 16 GB card at INT4

About 0.4 GB at INT4, 0.9 GB at FP8, 1.8 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.4 GB of weights (estimate; KV cache and activations extra)INT40.4 GBFP8: about 0.9 GB of weights (estimate; KV cache and activations extra)FP80.9 GBBF16: about 1.8 GB of weights (estimate; KV cache and activations extra)BF161.8 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 0.4 GB of weights (estimate; KV cache and activations extra)INT40.4 GBFP8: about 0.9 GB of weights (estimate; KV cache and activations extra)FP80.9 GBBF16: about 1.8 GB of weights (estimate; KV cache and activations extra)BF161.8 GB

Estimate params × bytes per param × 1.2 · how we estimate

How it scores

Third-party measurements only; author-reported numbers stay in Reference, marked as such.

#10 of 64 on MTEB so far

67.53. Each tick is a model on this site with a measured MTEB row; author-reported numbers stay in Reference. Mean(task) x100 over 41 tasks. 41 tasks, split=test

BAAI/bge-multilingual-gemma2: 78.95 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsQwen/Qwen3-Embedding-8B: 75.22 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsQwen/Qwen3-Embedding-4B: 74.6 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-base-en: 72.33 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsQwen/Qwen3-Embedding-0.6B: 70.47 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsgoogle/embeddinggemma-300m: 69.67 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsQwen/Qwen3-Embedding-8B: 69.44 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsQwen/Qwen3-Embedding-4B: 68.46 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/multi-qa-MiniLM-L6-cos-v1: 67.83 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsjinaai/jina-embeddings-v3: 66.6 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-large-en-v1.5: 65.89 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-large-instruct: 65.56 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-base-en-v1.5: 65.14 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsthenlper/gte-large: 64.77 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-small-en-v1.5: 64.3 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsnomic-ai/nomic-embed-text-v2-moe: 63.64 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsSnowflake/snowflake-arctic-embed-l-v2.0: 63.6 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsnomic-ai/nomic-embed-text-v1: 63.6 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-m3: 63.21 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/e5-large-v2: 62.79 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/e5-base-v2: 62.67 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsAlibaba-NLP/gte-multilingual-base: 62.51 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsnomic-ai/nomic-embed-text-v1.5: 62.2 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-large: 62.07 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsQwen/Qwen3-Embedding-0.6B: 61.83 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/e5-small-v2: 61.32 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsSnowflake/snowflake-arctic-embed-m: 61.09 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsibm-granite/granite-embedding-small-english-r2: 61.07 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-base: 61 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-small: 59.69 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/all-mpnet-base-v2: 59.62 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsSnowflake/snowflake-arctic-embed-l-v2.0: 58.56 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/paraphrase-multilingual-mpnet-base-v2: 58.27 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsSnowflake/snowflake-arctic-embed-m: 57.53 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/paraphrase-multilingual-MiniLM-L12-v2: 56.42 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsgoogle/embeddinggemma-300m: 55.69 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-large-en-v1.5: 55.44 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsnomic-ai/nomic-embed-text-v2-moe: 54.81 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-base-en-v1.5: 54.75 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsjinaai/jina-embeddings-v3: 54.29 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsibm-granite/granite-embedding-small-english-r2: 53.93 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-small-en-v1.5: 53.86 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-large-instruct: 53.47 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsthenlper/gte-large: 53.29 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-large: 51.53 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsjinaai/jina-embeddings-v2-small-en: 51.02 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsAlibaba-NLP/gte-multilingual-base: 50.87 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsnomic-ai/nomic-embed-text-v1: 50.72 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/e5-base-v2: 49.67 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/e5-large-v2: 49.31 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-base: 48.97 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/LaBSE: 48.81 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/e5-small-v2: 48.46 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsnomic-ai/nomic-embed-text-v1.5: 47.97 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-small: 46.43 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsjinaai/jina-embeddings-v2-base-en: 45.97 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/all-mpnet-base-v2: 44.54 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/paraphrase-multilingual-mpnet-base-v2: 37.14 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/paraphrase-multilingual-MiniLM-L12-v2: 35.93 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsjinaai/jina-embeddings-v2-base-en: 26.67 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsjinaai/jina-embeddings-v2-small-en: 26.47 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/LaBSE: 18.05 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/results010067.53 · #10 of 64
BAAI/bge-multilingual-gemma2: 78.95 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsQwen/Qwen3-Embedding-8B: 75.22 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsQwen/Qwen3-Embedding-4B: 74.6 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-base-en: 72.33 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsQwen/Qwen3-Embedding-0.6B: 70.47 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsgoogle/embeddinggemma-300m: 69.67 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsQwen/Qwen3-Embedding-8B: 69.44 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsQwen/Qwen3-Embedding-4B: 68.46 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/multi-qa-MiniLM-L6-cos-v1: 67.83 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsjinaai/jina-embeddings-v3: 66.6 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-large-en-v1.5: 65.89 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-large-instruct: 65.56 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-base-en-v1.5: 65.14 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsthenlper/gte-large: 64.77 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-small-en-v1.5: 64.3 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsnomic-ai/nomic-embed-text-v2-moe: 63.64 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsSnowflake/snowflake-arctic-embed-l-v2.0: 63.6 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsnomic-ai/nomic-embed-text-v1: 63.6 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-m3: 63.21 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/e5-large-v2: 62.79 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/e5-base-v2: 62.67 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsAlibaba-NLP/gte-multilingual-base: 62.51 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsnomic-ai/nomic-embed-text-v1.5: 62.2 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-large: 62.07 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsQwen/Qwen3-Embedding-0.6B: 61.83 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/e5-small-v2: 61.32 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsSnowflake/snowflake-arctic-embed-m: 61.09 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsibm-granite/granite-embedding-small-english-r2: 61.07 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-base: 61 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-small: 59.69 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/all-mpnet-base-v2: 59.62 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsSnowflake/snowflake-arctic-embed-l-v2.0: 58.56 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/paraphrase-multilingual-mpnet-base-v2: 58.27 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsSnowflake/snowflake-arctic-embed-m: 57.53 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/paraphrase-multilingual-MiniLM-L12-v2: 56.42 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsgoogle/embeddinggemma-300m: 55.69 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-large-en-v1.5: 55.44 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsnomic-ai/nomic-embed-text-v2-moe: 54.81 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-base-en-v1.5: 54.75 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsjinaai/jina-embeddings-v3: 54.29 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsibm-granite/granite-embedding-small-english-r2: 53.93 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsBAAI/bge-small-en-v1.5: 53.86 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-large-instruct: 53.47 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsthenlper/gte-large: 53.29 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-large: 51.53 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsjinaai/jina-embeddings-v2-small-en: 51.02 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsAlibaba-NLP/gte-multilingual-base: 50.87 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsnomic-ai/nomic-embed-text-v1: 50.72 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/e5-base-v2: 49.67 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/e5-large-v2: 49.31 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-base: 48.97 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/LaBSE: 48.81 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/e5-small-v2: 48.46 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsnomic-ai/nomic-embed-text-v1.5: 47.97 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsintfloat/multilingual-e5-small: 46.43 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsjinaai/jina-embeddings-v2-base-en: 45.97 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/all-mpnet-base-v2: 44.54 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/paraphrase-multilingual-mpnet-base-v2: 37.14 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/paraphrase-multilingual-MiniLM-L12-v2: 35.93 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsjinaai/jina-embeddings-v2-base-en: 26.67 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultsjinaai/jina-embeddings-v2-small-en: 26.47 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/resultssentence-transformers/LaBSE: 18.05 · observed 2026-10-25 · https://huggingface.co/datasets/mteb/results010067.53 · #10 of 64

Source · leaderboard · 2026-10-24

The story so far

Weights, every dated article, and repo commits on one rail.

What people built on it

252 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

252 derivatives on the Hub, most of them fine-tunes

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

115 quantized (Hugging Face model tree)130 fine-tunes (Hugging Face model tree)7 adapters (Hugging Face model tree)115 quantized130 fine-tunes7 adapters
115 quantized (Hugging Face model tree)130 fine-tunes (Hugging Face model tree)7 adapters (Hugging Face model tree)115 quantized130 fine-tunes7 adapters

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

3 rows

Benchmarks 3 rows

measured third-party leaderboard row (3)author-reported number from the model card or eval filesNo confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
measured MTEB · Embed text for search51.33 nDCG@10 x100 (mean of 10 tasks)10 tasks, split=testleaderboard2026-10-24
measured MTEB · Embed text for search67.53 mean(task) x100 over 41 tasks41 tasks, split=testleaderboard2026-10-24
measured MTEB Reranking · Rerank search results46.03 MAP x100 (mean of 2 tasks)2 tasks, split=testleaderboard2026-10-24

Use-case evidence

4 use cases

What it is good at 4 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

14 fields

Listed in Embeddings

Facts

Pipeline tagfeature-extraction
Librarytransformers
ArchitecturesEmbeddingGemma2Model
Model typeembedding_gemma2
Params by dtypeBF16: 744,371,512
Size bandS
MoEno
LicenseApache 2.0 (apache-2.0)
Hub snapshot2026-10-25

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for embeddinggemma-2: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)1.8 GB · Edge (<4 GB)
FP8 (1 byte/param)0.9 GB · Edge (<4 GB)
INT4 (0.5 bytes/param)0.4 GB · Edge (<4 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra.

All variants

40 variants

Lineage

Original model Family: google/embeddinggemma-2 252 derivatives on the Hub

quantized: 115finetune: 130adapter: 7

Top variants (40 shown, ranked by downloads)

Relation
Quant
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
onnx-community/embeddinggemma-2-ONNX quantized — — — apache-2.0 onnx-community 28 275.7k
unsloth/embeddinggemma-2-GGUF quantized GGUF — — apache-2.0 unsloth 345 125k
litert-community/embeddinggemma-2-740m-litert-lm finetune — — — apache-2.0 litert-community 36 130.7k
ggml-org/embeddinggemma-2-GGUF quantized GGUF — — apache-2.0 ggml-org 54 90.8k
litert-community/embeddinggemma-2-text-270m-litert-lm finetune — — — apache-2.0 litert-community 10 12.7k
litert-community/embeddinggemma-2-text-vision-440m-litert-lm finetune — — — apache-2.0 litert-community 6 10.5k
unsloth/embeddinggemma-2 finetune — 744M S apache-2.0 unsloth 12 5.9k
ngquocvinh/EmbeddingGemma-2-GGUF quantized GGUF — — apache-2.0 ngquocvinh 1 6.4k
mlx-community/embeddinggemma-2-bf16 finetune -bf16 744M S apache-2.0 mlx-community 11 5.4k
abacusai/Smaug-AI-Image-Detector finetune — 439M S apache-2.0 abacusai 6 5.2k
mlx-community/embeddinggemma-2-8bit quantized -8bit 744M S apache-2.0 mlx-community 8 4.9k
mrm8488/embeddinggemma-2-ft-typed-decision finetune — 272M S apache-2.0 mrm8488 63 1.4k
qnguyen3/QUAN-Transcribe-1.0 finetune — 102M S gemma qnguyen3 64 544
FluidInference/embeddinggemma-2-coreml quantized — — — apache-2.0 FluidInference 27 1.6k
AtomicChat/embeddinggemma-2-GGUF quantized GGUF — — apache-2.0 AtomicChat 2 2.7k
webmp3/Sakura-EmbeddingGemma-2-HighQuality-GGUF quantized GGUF — — apache-2.0 webmp3 1 1.9k
webmp3/Sakura-Gemma4-E2B-EmbeddingGemma2-DualMode-GGUF quantized GGUF — — gemma webmp3 1 1.9k
prithivMLmods/EmbeddingGemma-2-GGUF quantized GGUF — — apache-2.0 prithivMLmods 1 1.3k
mlx-community/embeddinggemma-2-mxfp8 quantized — 744M S apache-2.0 mlx-community 5 1.1k
BennyDaBall/EmbeddingGemma-2-NVFP4-GGUF quantized NVFP4 — — apache-2.0 BennyDaBall 0 1.3k
mlx-community/embeddinggemma-2-4bit quantized -4bit 744M S apache-2.0 mlx-community 3 951
infosave/EmbeddingGemma-2-cmf quantized — — — apache-2.0 infosave 1 790
mlx-community/embeddinggemma-2-mxfp4 quantized mxfp4 744M S apache-2.0 mlx-community 3 666
EverPaper/embeddinggemma-2-coreai finetune — — — apache-2.0 EverPaper 16 0
SargeDev/Solar-JEV-Gemma-embed-2-GGUF quantized GGUF — — apache-2.0 SargeDev 0 787
Fastiraz/embeddinggemma-2-GGUF quantized GGUF — — apache-2.0 Fastiraz 0 697
nativ-community/embeddinggemma-2-8bit quantized -8bit 744M S apache-2.0 nativ-community 3 525
webmp3/Sakura-EmbeddingGemma-2-AutoRound-GGUF quantized GGUF — — gemma webmp3 1 585
webmp3/Sakura-Gemma4-E4B-EmbeddingGemma2-DualMode-GGUF quantized GGUF — — gemma webmp3 0 574
PixelML/sea-asr-id finetune — 112M S apache-2.0 PixelML 8 159
mlx-community/embeddinggemma-2-nvfp4 quantized nvfp4 744M S apache-2.0 mlx-community 2 435
mlx-community/embeddinggemma-2-5bit quantized — 744M S apache-2.0 mlx-community 3 368
yan2026/embeddinggemma-2-GGUF quantized GGUF — — gemma yan2026 0 494
mlx-community/embeddinggemma-2-6bit quantized — 744M S apache-2.0 mlx-community 3 337
spectral-ai-lab/arbiter-272m finetune — 272M S other spectral-ai-lab 5 180
nativ-community/embeddinggemma-2-bf16 finetune -bf16 744M S apache-2.0 nativ-community 3 266
pastel-labs/embeddinggemma-2-photo-anchor finetune — — — cc-by-4.0 pastel-labs 8 0
mlboydaisuke/EmbeddingGemma-2-Transcribe-LiteRT finetune — — — apache-2.0 mlboydaisuke 6 95
turintech/embeddinggemma-2-Artemis-GGUF quantized GGUF — — apache-2.0 turintech 1 308
HukukcuApp/tr-legal-embedding-gemma-2 finetune — 271M S gemma HukukcuApp 4 138

Repository changelog

2 changes
changed in the last 7 daysOther: 2026-10-24, 2026-10-24all changes

Articles and coverage

14 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official4

blog.google2026-10-06officialauthorityHN front pageHN 429 pts · 46 comments678 words
Google launches EmbeddingGemma 2, an open multimodal model expanding beyond text to unify code, images, video, and audio. Built on the Gemma 4 architecture with 740 million parameters and an Apache 2.0 license, it achieves a 9.92-point improvement on code performance. The model features an 8K token context window and supports dynamic vector truncation for storage efficiency.written, reviewed, cited
  • Developers can dynamically truncate output vectors from 768 dimensions down to 512, 256, or 128 dimensions.
  • The model processes up to 5.5 minutes of audio, 29 images, or 58 video frames within its 8K token context window.
developers.googleblog.com2026-10-06officialauthorityHN 9 pts · 0 comments1,271 words
EmbeddingGemma 2 is a compact open model under the Apache 2.0 license that maps text, code, images, video, and audio into a unified 768-dimensional space. Based on Gemma 4, this sub-1B model uses Matryoshka Representation Learning (MRL) to enable dynamic truncation down to 128 dimensions, achieving a 6x reduction in vector database storage requirements while retaining much of the original quality.written, reviewed, cited
  • Video is sampled at 1 frame per second by default, and audio should be 16 kHz mono.
  • At 128 dimensions, image, video, and speech retrieval quality drops to around 75%.
  • EmbeddingGemma 2 scores 14% higher than EmbeddingGemma 1 on MTEB (Code).
ai.google.devofficialauthority2,946 words
ai.google.devofficialauthority2,609 words

Analysis5

simonwillison.net2026-10-07authorityHN 1 pts · 1 comments230 words
The author appreciates that EmbeddingGemma 2 is under the Apache 2.0 license. The author prefers a hosted model where open weights allow self-hosting if a provider stops offering it.generated, cited
github.com2026-10-08authorityHN 2 pts · 0 comments845 words
A GitHub project ports four jeffhub use cases to Google's EmbeddingGemma-2, replacing the original decider with cosine similarity scoring. The 740M-parameter model runs on CPU via int8 ONNX Runtime (~314 MB) with ~170 ms latency and 24 docs/s throughput. Classification uses a temperature 0.05 softmax, while MRL truncation trades quality for 3–6× smaller vector stores.written, reviewed, cited
  • The ONNX build loads all three encoders (text + vision + audio) by default.
  • A text-only deployment could ship just onnx/model_quantized.* to save space.
  • Quality is at ceiling because the held-out rows are paraphrases of the exemplars.
youtube.com2026-10-06HN 2 pts · 0 comments
youtube.com2026-10-07HN 1 pts · 0 comments
accessallgpt.com2026-10-071,657 words

Community1

twitter.com2026-10-06HN 11 pts · 1 comments
All coverage tracked for this model (14) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
EmbeddingGemma 2 model card - Google AI for Developersai.google.dev official—2026-10-09exa
Generating Embeddings with EmbeddingGemma 2 and Sentence Transformers  |  Google AI for Developersai.google.dev official—2026-10-09exa
EmbeddingGemma 2 | AI Wikiaiwiki.ai—2026-10-09exa
EmbeddingGemma 2 Review (2026): Honest Verdict on ...kompozy.io—2026-10-09exa
Embedding Gemma2 Use Casesgithub.com2026-10-082026-10-09hn
EmbeddingGemma 2: Google's Offline Search Beyond Textyoutube.com2026-10-072026-10-09hn
EmbeddingGemma 2simonwillison.net2026-10-072026-10-09hn
Google Launches EmbeddingGemma 2 for On-Device Multimodal Retrieval | AccessAllGPT Researchaccessallgpt.com2026-10-072026-10-09exa
Google EmbeddingGemma 2twitter.com2026-10-062026-10-09hn
EmbeddingGemma 2: An open model for natively multimodal embeddings [video]youtube.com2026-10-062026-10-09hn
EmbeddingGemma 2developers.googleblog.com official2026-10-062026-10-09hn, exa
EmbeddingGemma 2: An open, lightweight multimodal embedding modelblog.google official2026-10-062026-10-09hn, exa
EmbeddingGemma 2: Benchmarks, Specs and How to Run It | CellCogcellcog.ai2026-10-062026-10-09exa
EmbeddingGemma 2: Open Multimodal Embeddings | S5 Labss5labs.io2026-10-062026-10-09exa

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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-09 02:04 UTC · Last verified 2026-10-25

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