Global AI chat room · 18 online now Join now
G
MODEL Listed

gemma 4 26B A4B it

Gemma 4 26B A4B is a multimodal model from Google, designed for developers needing high-performance image-to-text and text-to-text capabilities within an open-weights framework. Unlike smaller edge models, the 26B parameter scale allows for more nuanced reasoning and complex visual analysis while remaining deployable on consumer-grade hardware or private clouds. It is particularly effective for automated document parsing, visual QA, and augmenting RAG pipelines with image-based context. With an Apache-2.0 license, it offers significant flexibility for commercial integration, providing a competitive alternative to proprietary multimodal APIs by reducing latency and eliminating per-token costs for high-volume inference.

googleimage-text-to-text
View model source
01 / MODEL CARD

Model card

Gemma 4 26B A4B is a multimodal model from Google, designed for developers needing high-performance image-to-text and text-to-text capabilities within an open-weights framework. Unlike smaller edge models, the 26B parameter scale allows for more nuanced reasoning and complex visual analysis while remaining deployable on consumer-grade hardware or private clouds. It is particularly effective for automated document parsing, visual QA, and augmenting RAG pipelines with image-based context. With an Apache-2.0 license, it offers significant flexibility for commercial integration, providing a competitive alternative to proprietary multimodal APIs by reducing latency and eliminating per-token costs for high-volume inference.

Model typeimage-text-to-text
Providergoogle
Licenseapache-2.0
02 / FILES & VERSIONS

Model files and versions

Model cardModel description and metadata available in this entry
Listed
Source repositoryhttps://huggingface.co/google/gemma-4-26B-A4B-it
View model source
Version informationUse the source repository for the latest version
—
03 / DOWNLOAD

Download this model

We recommend using the ModelScope CLI or SDK. Install ModelScope first, then choose a full snapshot, single file, SDK or Git LFS workflow.

This entry points to Hugging Face. The commands use the matching ModelScope repository format; confirm that the repository exists on ModelScope before running them. Model repository: google/gemma-4-26B-A4B-it
Install ModelScope

Install the CLI and SDK dependency before downloading.

pip install modelscope
Download the full model repository

Download the complete weights, configuration and model card.

modelscope download --model google/gemma-4-26B-A4B-it
Download one file to a local directory

README.md is used as an example; replace it with another repository file when needed.

modelscope download --model google/gemma-4-26B-A4B-it README.md --local_dir ./dir
Download with the SDK

Useful in Python projects and automation scripts.

from modelscope import snapshot_download
model_dir = snapshot_download('google/gemma-4-26B-A4B-it')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/google/gemma-4-26B-A4B-it.git
Clone without downloading LFS blobs

Fetch the repository structure first, then pull large files when needed.

GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/google/gemma-4-26B-A4B-it.git
04 / WORKFLOW

How to use

  1. 01
    Step 1

    Read the model card and source information.

  2. 02
    Step 2

    Start with a small, non-sensitive evaluation.

  3. 03
    Step 3

    Review quality, licensing and usage limits.

  4. 04
    Step 4

    Adopt it only after validation.

05 / DISCUSSIONS

Discussions

Use this space to keep checking source information, usage experience and maintenance status.

Open source page
Email