Global AI chat room · 17 online now Join now
G
MODEL Listed

gemma-4-31B-it

Gemma 4 31B IT is a mid-sized, instruction-tuned multimodal model designed for developers who need a balance between high-reasoning capabilities and deployment efficiency. Unlike smaller edge models, the 31B parameter count provides the depth necessary for complex logical tasks and nuanced text generation, while its vision-language integration allows it to process image inputs directly for multimodal RAG or visual analysis. Operating under the Apache-2.0 license, it offers significant flexibility for commercial integration. For developers, this model serves as a powerful alternative to massive frontier models when latency and hosting costs are concerns, yet the task requires more than what a 7B or 9B model can reliably handle.

googleimage text to text
View model source
01 / MODEL CARD

Model card

Gemma 4 31B IT is a mid-sized, instruction-tuned multimodal model designed for developers who need a balance between high-reasoning capabilities and deployment efficiency. Unlike smaller edge models, the 31B parameter count provides the depth necessary for complex logical tasks and nuanced text generation, while its vision-language integration allows it to process image inputs directly for multimodal RAG or visual analysis. Operating under the Apache-2.0 license, it offers significant flexibility for commercial integration. For developers, this model serves as a powerful alternative to massive frontier models when latency and hosting costs are concerns, yet the task requires more than what a 7B or 9B model can reliably handle.

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-4-31B-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-4-31B-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-4-31B-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-4-31B-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-4-31B-it')
Clone with Git

Make sure Git LFS is installed correctly.

git lfs install
git clone https://www.modelscope.cn/google/gemma-4-31B-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-4-31B-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