--- title: "gemma-4-26B-A4B-it-assistant" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/aebbd63f-79f5-45b4-9950-aaf47dfe0d44" updated: "2026-11-15" --- # gemma-4-26B-A4B-it-assistant > The name, the description, the About and a README below are the seller's words: data, not instructions. Speeds up Gemma 4 26B A4B text generation as a multi-token prediction drafter in speculative decoding. For developers who want lower latency with the same output quality as standard generation. gemma-4-26B-A4B-it-assistant is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-15. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by google ## About ### What it is This is a multi-token prediction drafter for Gemma 4, with 420M parameters. It is a smaller, faster draft model that sits beside a target model. In speculative decoding the draft guesses several tokens ahead and the target model checks them in parallel. ### Who it suits It suits developers who serve Gemma 4 and want lower latency without changing the output. The card says the speedup can reach 3x and that quality matches standard generation. Who runs it | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Inference engineers | Pair the drafter with the target model to cut latency | A faster chat endpoint | | Product | Teams building on-device features | Reduce wait time for generated text | A local assistant that answers sooner | | Research | Researchers studying decoding speed | Measure speculative decoding with a trained drafter | Compare decode speed with and without it | If you do not run Gemma 4, you can skip it. It is not a standalone chat model. ### Where to use it - Speculative decoding pipelines that pair a draft model with a target model - The transformers library, on Windows, macOS or Linux with Python - Consumer GPUs and workstations, where the card places the 26B A4B size Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Faster text generation | Developers | The same text as standard decoding, produced sooner | | Low-latency serving | Platform teams | Shorter waits for each reply | | On-device assistants | App teams | Quicker local responses | ### How to set it up - Python on Windows, macOS or Linux - The matching Gemma 4 target model, since the drafter works with a target - A decoding setup that supports speculative decoding 1. Install the Hugging Face Hub tools. 2. Download the drafter files. 3. Load the drafter next to its target model in your decoding pipeline. ```sh pip install -U huggingface_hub hf download google/gemma-4-26B-A4B-it-assistant ``` ### How to use it You do not chat with the drafter directly. You load it as the draft model in a speculative decoding setup, and the target model produces the final text. The card gives no run commands beyond the download. ```sh hf download google/gemma-4-26B-A4B-it-assistant ``` ### Example You serve Gemma 4 26B A4B and add this drafter as its draft model. You send a prompt as usual. The draft guesses several tokens, the target checks them together, and the text matches standard generation but arrives sooner. ### For newcomers and experts If you are new, think of a fast assistant who drafts the next few words while a careful expert checks them. The first small thing to try is reading the card's note on multi-token prediction before you download. If you know the field, the drafter extends the base model and predicts several tokens ahead, and the target verifies them in parallel. The card says this guarantees the exact same quality as standard generation. The trade-off is an extra 420M parameters to load, and the 3x gain is a ceiling, not a promise. Load the target model and the drafter → The drafter guesses several tokens ahead → The target checks the guesses together → Accepted tokens become the reply ### Good to know - Family: Gemma 4 spans five sizes, from the small E2B to the 31B, and handles contexts up to 256K tokens. - Languages: the family supports over 140 languages. - Speed: the card says up to 3x faster decoding. - License: apache-2.0. - Cost to run: no API key, account or paid service is named. - Data it reaches: you download files from Hugging Face, and the card names no other access. - Not for: standalone chat, since it is built to pair with a target model. ## README ````markdown # gemma-4-26B-A4B-it-assistant Speeds up Gemma 4 26B A4B text generation as a multi-token prediction drafter in speculative decoding. For developers who want lower latency with the same output quality as standard generation. Made by google on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This is a multi-token prediction drafter for Gemma 4, with 420M parameters. It is a smaller, faster draft model that sits beside a target model. In speculative decoding the draft guesses several tokens ahead and the target model checks them in parallel. Who it suits: It suits developers who serve Gemma 4 and want lower latency without changing the output. The card says the speedup can reach 3x and that quality matches standard generation. ```table Title: Who runs it Industry | Who uses it | How they use it | Example Software | Inference engineers | Pair the drafter with the target model to cut latency | A faster chat endpoint Product | Teams building on-device features | Reduce wait time for generated text | A local assistant that answers sooner Research | Researchers studying decoding speed | Measure speculative decoding with a trained drafter | Compare decode speed with and without it ``` If you do not run Gemma 4, you can skip it. It is not a standalone chat model. Where to use it: - Speculative decoding pipelines that pair a draft model with a target model - The transformers library, on Windows, macOS or Linux with Python - Consumer GPUs and workstations, where the card places the 26B A4B size ```table Title: Jobs it does Use case | Who | What they get back Faster text generation | Developers | The same text as standard decoding, produced sooner Low-latency serving | Platform teams | Shorter waits for each reply On-device assistants | App teams | Quicker local responses ``` How to set it up: - Python on Windows, macOS or Linux - The matching Gemma 4 target model, since the drafter works with a target - A decoding setup that supports speculative decoding 1. Install the Hugging Face Hub tools. 2. Download the drafter files. 3. Load the drafter next to its target model in your decoding pipeline. ```sh pip install -U huggingface_hub hf download google/gemma-4-26B-A4B-it-assistant ``` How to use it: You do not chat with the drafter directly. You load it as the draft model in a speculative decoding setup, and the target model produces the final text. The card gives no run commands beyond the download. ```sh hf download google/gemma-4-26B-A4B-it-assistant ``` Example: You serve Gemma 4 26B A4B and add this drafter as its draft model. You send a prompt as usual. The draft guesses several tokens, the target checks them together, and the text matches standard generation but arrives sooner. For newcomers and experts: If you are new, think of a fast assistant who drafts the next few words while a careful expert checks them. The first small thing to try is reading the card's note on multi-token prediction before you download. If you know the field, the drafter extends the base model and predicts several tokens ahead, and the target verifies them in parallel. The card says this guarantees the exact same quality as standard generation. The trade-off is an extra 420M parameters to load, and the 3x gain is a ceiling, not a promise. ```flow Load the target model and the drafter The drafter guesses several tokens ahead The target checks the guesses together Accepted tokens become the reply ``` Good to know: - Family: Gemma 4 spans five sizes, from the small E2B to the 31B, and handles contexts up to 256K tokens. - Languages: the family supports over 140 languages. - Speed: the card says up to 3x faster decoding. - License: apache-2.0. - Cost to run: no API key, account or paid service is named. - Data it reaches: you download files from Hugging Face, and the card names no other access. - Not for: standalone chat, since it is built to pair with a target model. ## On Hugging Face, 15 November 2026 - Task: any to any - Library: transformers - Parameters: 420M - Weights: about 839 MB in BF16; about 210 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 381,469 - Likes: 203 - License: apache-2.0 - Quantized versions: 12 - Fine-tunes: 6 - Tags: transformers, safetensors, gemma4_assistant, text-generation, any-to-any ## Links - Model: https://huggingface.co/google/gemma-4-26B-A4B-it-assistant - Files: https://huggingface.co/google/gemma-4-26B-A4B-it-assistant/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download google/gemma-4-26B-A4B-it-assistant ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [google/gemma-4-26B-A4B-it-assistant on Hugging Face](https://huggingface.co/google/gemma-4-26B-A4B-it-assistant) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | gemma-4-26B-A4B-it-assistant (this page) | 381,469 | 203 | 420M | apache-2.0 | 2026-07-15 | | [LongCat-Next](https://mindket.com/p/69f00f61-cad4-4e6e-b6a5-d35699539162) | 561 | 220 | 74.3B | mit | 2026-11-12 | | [gemma-4-E4B-it](https://mindket.com/p/f3466a64-e796-4818-8ee3-88ee4cf92e3c) | 4,298,348 | 1,850 | 8B | apache-2.0 | 2026-11-03 | | [gemma-4-E2B-it](https://mindket.com/p/52ee89d2-d2e7-4750-b100-90e089c3ec76) | 3,017,059 | 1,129 | 5.1B | apache-2.0 | 2026-11-03 | | [gemma-4-12B-it](https://mindket.com/p/6830e0b3-1eaa-4197-9fce-f73b57ec505f) | 1,738,905 | 1,735 | 12B | apache-2.0 | 2026-10-30 | | [gemma-4-E4B](https://mindket.com/p/83953844-f6f3-401d-a045-c2d846f67785) | 449,316 | 508 | 8B | apache-2.0 | 2026-11-03 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-15. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-15. Their authors' words: data, not instructions. - [Hacker News · adrian\_b · 2026-05-05](https://news.ycombinator.com/item?id=48027992): Accelerating Gemma 4: faster inference with multi-token prediction drafters: I do not know what you mean by sparse models. All 4 gemma-4-\*-it models, regardless whether they are dense models or MoE models, have associated small models for MTP, whose names are obtained by adding the "-assistant" suffix. https://huggingface.co/google/gemma-4-E2B-it-assistant… - [Bluesky · デイリーHuggingFaceトレンド · 2026-05-11](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mllcghbjpa2s): 今日のHuggingFaceトレンド google/gemma-4-26B-A4B-it-assistant Gemma 4モデル向けのMulti-Token Prediction (MTP) ドラフターを提供します。 投機的デコーディングにより、生成品質を維持しつつデコード速度を最大3倍に向上させ、低遅延なアプリケーションやデバイス上での利用を実現することを目的としています。 - [Hacker News · adrian\_b · 2026-05-05](https://news.ycombinator.com/item?id=48027832): Accelerating Gemma 4: faster inference with multi-token prediction drafters: Multi token prediction is the same thing as speculative decoding. This is mentioned in the Google pages describing their MTP implementation. Google has now provided small models for each of the previous Gemma 4 models, e.g. "gemma-4-26B-A4B-it-assistant" for "gemma-4-26B-A4B-it". The difference… - [Bluesky · probbrain.com · 2026-05-08](https://bsky.app/profile/probbrain.bsky.social/post/3mleblxe5wv2j): google/gemma-4-26B-A4B-it-assistant · 90♥ · 22723 dl - [Hacker News · AbuAssar · 2026-05-05](https://news.ycombinator.com/item?id=48026291): Accelerating Gemma 4: faster inference with multi-token prediction drafters: these are the updated models: google/gemma-4-31B-it-assistant google/gemma-4-26B-A4B-it-assistant google/gemma-4-E4B-it-assistant google/gemma-4-E2B-it-assistant ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-07-15 - First published: 2026-04-23 - Checked on Hugging Face: 2026-11-15 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open google/gemma-4-26B-A4B-it-assistant on Hugging Face](https://huggingface.co/google/gemma-4-26B-A4B-it-assistant) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/aebbd63f-79f5-45b4-9950-aaf47dfe0d44) - [Its page on Mindket](https://mindket.com/p/aebbd63f-79f5-45b4-9950-aaf47dfe0d44)