{"id":4877,"date":"2026-09-26T22:26:48","date_gmt":"2026-09-26T13:26:48","guid":{"rendered":"https:\/\/localmodelwatch.tsuchitsuchi.com\/publisher-google-en\/"},"modified":"2026-09-27T07:21:02","modified_gmt":"2026-09-26T22:21:02","slug":"publisher-google-en","status":"publish","type":"page","link":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/publisher-google-en\/","title":{"rendered":"Google Open Models: Model List and Licenses"},"content":{"rendered":"<h2>About Google<\/h2>\n<p>Google publishes 1,134 models on its <a href=\"https:\/\/huggingface.co\/google\">google organization on Hugging Face<\/a>. For local LLM users, the center of it is <strong>Gemma<\/strong>, the open model family built by Google DeepMind. The organization card describes Gemma as &#8220;a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models&#8221;.<\/p>\n<p>Besides Gemma, the organization hosts many models for other purposes, such as MedGemma for medicine, TranslateGemma for translation, Magenta RealTime for music and TimesFM for time-series forecasting. The table below lists only the generative models this site covers.<\/p>\n<h2>What It Releases<\/h2>\n<ul>\n<li><strong>Gemma 4 covers phones to workstations in a single generation.<\/strong> According to its model card it comes in five sizes, E2B, E4B, 12B, 26B A4B (MoE) and 31B; E2B and E4B target phones and laptops, while 12B, 26B A4B and 31B target consumer GPUs and workstations. Every model accepts images, and E2B, E4B and 12B also handle audio. The context window is 128K tokens for the small models and 256K for the medium ones.<\/li>\n<li><strong>The &#8220;E&#8221; means effective parameters.<\/strong> E2B and E4B use Per-Layer Embeddings; counting the embeddings, E2B is 5.1B and E4B is 8B. The 26B A4B is an MoE model with 3.8B of its 25.2B parameters active.<\/li>\n<li><strong>DiffusionGemma (26B A4B) generates text by diffusion instead of one token at a time.<\/strong> Built on the Gemma 4 26B A4B architecture, it denoises blocks of tokens in parallel to raise decoding speed.<\/li>\n<\/ul>\n<h2>Licenses<\/h2>\n<p>The Gemma 4 and DiffusionGemma model cards give the license as <strong>Apache-2.0<\/strong> (linking to the Gemma 4 license page). TranslateGemma from January 2026 uses the earlier <strong>Gemma license<\/strong> (<code>gemma<\/code>), and models such as TimesFM use non-commercial licenses. Terms differ from model to model within the same organization, so check the license column in the table and each model&#8217;s license text. This summary is not legal advice.<\/p>\n<h2>Running Them Locally<\/h2>\n<ul>\n<li><strong>There are official GGUF builds.<\/strong> Gemma 4 has QAT (Quantization-Aware Training) versions, and Google itself distributes Q4_0 GGUF files for E2B, E4B, 12B, 26B A4B and 31B. The model card says QAT preserves quality similar to bfloat16 while dramatically reducing the memory needed to load the model. These run directly in llama.cpp, Ollama and LM Studio.<\/li>\n<li><strong>There are also builds for vLLM and phones.<\/strong> Compressed-tensors (w4a16) versions for vLLM and mobile-optimized versions of E2B and E4B are available separately.<\/li>\n<li><strong>When pairing a QAT model with an assistant model for speculative decoding, the assistant must also be a QAT checkpoint with the same precision<\/strong>, the model card warns.<\/li>\n<\/ul>\n<p><em>Sources: <a href=\"https:\/\/huggingface.co\/google\">the google organization card<\/a>; model cards of <a href=\"https:\/\/huggingface.co\/google\/gemma-4-31B\">google\/gemma-4-31B<\/a>, <a href=\"https:\/\/huggingface.co\/google\/gemma-4-12B-it-qat-q4_0-gguf\">google\/gemma-4-12B-it-qat-q4_0-gguf<\/a> and <a href=\"https:\/\/huggingface.co\/google\/diffusiongemma-26B-A4B-it\">google\/diffusiongemma-26B-A4B-it<\/a> (all as of 2026-09-26).<\/em><\/p>\n<h2>Our Coverage and Data<\/h2>\n<p>Local Model Watch has not published an article on Google&#8217;s models yet. The model list below is collected by code from Hugging Face every day. Part of our <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/publishers-en\/\">publisher index<\/a>; model families link to their <a href=\"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/models-en\/\">family pages<\/a>.<\/p>\n<h2>At a Glance<\/h2>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Item<\/th>\n<th>Value<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Hugging Face organization<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\">google<\/a><\/td>\n<\/tr>\n<tr>\n<td>Models on Hugging Face<\/td>\n<td>1134<\/td>\n<\/tr>\n<tr>\n<td>Articles on its own models<\/td>\n<td>0<\/td>\n<\/tr>\n<tr>\n<td>Articles on derived models<\/td>\n<td>0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<h2>Recent Models on Hugging Face<\/h2>\n<p>The publisher lists <strong>1134 models<\/strong> in total on Hugging Face. Below are up to 15 of the most recent generative models (text, image, video, audio and so on), the scope of this site, per organization. Classifiers, feature extractors, research components and repositories without a model card are omitted, and the publisher&#8217;s own quantized builds and helper variants are folded into the row of the original model. Parameter counts are left blank for repositories that pack weights into integer types, where Hugging Face&#8217;s count is not the real parameter count.<\/p>\n<div class=\"lmw-table-scroll\" tabindex=\"0\" style=\"overflow-x:auto;-webkit-overflow-scrolling:touch;max-width:100%;\">\n<p class=\"lmw-table-hint\" style=\"margin:0 0 4px;font-size:0.85em;opacity:0.7;\">\u2192 Scroll horizontally to see all columns<\/p>\n<table style=\"width:max-content;min-width:100%;border-collapse:collapse;\">\n<thead>\n<tr>\n<th>Released<\/th>\n<th>Model<\/th>\n<th>Task<\/th>\n<th>Parameters<\/th>\n<th>License<\/th>\n<th>Official variants<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>2026-06-09<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/diffusiongemma-26B-A4B-it\">google\/diffusiongemma-26B-A4B-it<\/a><\/td>\n<td>vision-language<\/td>\n<td>25.8B<\/td>\n<td><code>apache-2.0<\/code><\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>2026-05-29<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/magenta-realtime-2\">google\/magenta-realtime-2<\/a><\/td>\n<td>audio<\/td>\n<td>\u2014<\/td>\n<td><code>cc-by-4.0<\/code><\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>2026-05-23<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/gemma-4-12B\">google\/gemma-4-12B<\/a><\/td>\n<td>vision-language<\/td>\n<td>12.0B<\/td>\n<td><code>apache-2.0<\/code><\/td>\n<td>it-qat-w4a16-ct, it-qat-q4_0-gguf, it-qat-q4_0-unquantized-assistant, it-qat-q4_0-unquantized and 2 more<\/td>\n<\/tr>\n<tr>\n<td>2026-03-12<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/gemma-4-31B\">google\/gemma-4-31B<\/a><\/td>\n<td>vision-language<\/td>\n<td>32.7B<\/td>\n<td><code>apache-2.0<\/code><\/td>\n<td>it-qat-w4a16-ct, it-qat-q4_0-unquantized-assistant, it-qat-q4_0-gguf, it-qat-q4_0-unquantized and 2 more<\/td>\n<\/tr>\n<tr>\n<td>2026-03-12<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/gemma-4-26B-A4B\">google\/gemma-4-26B-A4B<\/a><\/td>\n<td>vision-language<\/td>\n<td>26.5B<\/td>\n<td><code>apache-2.0<\/code><\/td>\n<td>it-qat-q4_0-unquantized-assistant, it-qat-q4_0-gguf, it-qat-q4_0-unquantized, it-assistant and 1 more<\/td>\n<\/tr>\n<tr>\n<td>2026-03-03<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/gemma-4-E2B\">google\/gemma-4-E2B<\/a><\/td>\n<td>vision-language<\/td>\n<td>5.1B<\/td>\n<td><code>apache-2.0<\/code><\/td>\n<td>it-qat-w4a16-ct, it-qat-mobile-transformers, it-qat-mobile-ct, it-qat-q4_0-unquantized-assistant and 4 more<\/td>\n<\/tr>\n<tr>\n<td>2026-03-03<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/gemma-4-E4B\">google\/gemma-4-E4B<\/a><\/td>\n<td>vision-language<\/td>\n<td>8.0B<\/td>\n<td><code>apache-2.0<\/code><\/td>\n<td>it-qat-w4a16-ct, it-qat-mobile-transformers, it-qat-mobile-ct, it-qat-q4_0-unquantized-assistant and 4 more<\/td>\n<\/tr>\n<tr>\n<td>2026-01-13<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/translategemma-27b-it\">google\/translategemma-27b-it<\/a><\/td>\n<td>vision-language<\/td>\n<td>28.8B<\/td>\n<td><code>gemma<\/code><\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>2026-01-13<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/translategemma-12b-it\">google\/translategemma-12b-it<\/a><\/td>\n<td>vision-language<\/td>\n<td>13.2B<\/td>\n<td><code>gemma<\/code><\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>2026-01-13<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/translategemma-4b-it\">google\/translategemma-4b-it<\/a><\/td>\n<td>vision-language<\/td>\n<td>5.0B<\/td>\n<td><code>gemma<\/code><\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>2026-01-08<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/medgemma-1.5-4b-it\">google\/medgemma-1.5-4b-it<\/a><\/td>\n<td>vision-language<\/td>\n<td>4.3B<\/td>\n<td><code>health-ai-developer-foundations<\/code><\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>2025-12-19<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/medasr\">google\/medasr<\/a><\/td>\n<td>audio<\/td>\n<td>105M<\/td>\n<td><code>health-ai-developer-foundations<\/code><\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>2025-10-26<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/t5gemma-2-4b-4b\">google\/t5gemma-2-4b-4b<\/a><\/td>\n<td>vision-language<\/td>\n<td>8.9B<\/td>\n<td><code>gemma<\/code><\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>2025-10-26<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/t5gemma-2-1b-1b\">google\/t5gemma-2-1b-1b<\/a><\/td>\n<td>vision-language<\/td>\n<td>2.1B<\/td>\n<td><code>gemma<\/code><\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>2025-10-26<\/td>\n<td><a href=\"https:\/\/huggingface.co\/google\/t5gemma-2-270m-270m\">google\/t5gemma-2-270m-270m<\/a><\/td>\n<td>vision-language<\/td>\n<td>786M<\/td>\n<td><code>gemma<\/code><\/td>\n<td>\u2014<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><em>From the Hugging Face API. Last changed 2026-09-26 (JST). Collected by code.<\/em><\/p>\n<p><em>Last updated 2026-09-26 (JST). The explanation at the top of this page was written with the help of AI from the primary sources it cites. The tables and lists under &#8220;Our Coverage and Data&#8221; are assembled by code from our article log and the Hugging Face API.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>About Google Google publishes 1,134 models on its google organization on Hugging Face. For local LLM users, the center of it is Gemma, the open model family built by Google DeepMind. The organization card describes Gemma as &#8220;a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-4877","page","type-page","status-publish","hentry"],"lang":"en","translations":{"en":4877,"ja":4876},"_links":{"self":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/pages\/4877","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/comments?post=4877"}],"version-history":[{"count":1,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/pages\/4877\/revisions"}],"predecessor-version":[{"id":5144,"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/pages\/4877\/revisions\/5144"}],"wp:attachment":[{"href":"https:\/\/localmodelwatch.tsuchitsuchi.com\/en\/wp-json\/wp\/v2\/media?parent=4877"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}