Model card
The Gemma 4 31B-it-assistant is a high-parameter, instruction-tuned model designed for complex, multimodal workflows. Unlike standard text-only LLMs, this 'any-to-any' architecture allows developers to build applications that seamlessly process and reason across diverse data modalities. At 31B parameters, it strikes a strategic balance between high-level reasoning capabilities and deployment efficiency, making it suitable for edge-cloud hybrid architectures or high-throughput local inference. For developers, the primary value lies in its versatility: you can leverage it for sophisticated cross-modal retrieval, complex instruction following, and integrated multimodal reasoning tasks. Released under the Apache 2.0 license, it offers the flexibility needed for commercial integration without the constraints of restrictive proprietary licenses. Whether you are building advanced agents or multimodal RAG pipelines, this model provides a robust foundation for non-linear data processing.
Model files and versions
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.
google/gemma-4-31B-it-assistantInstall the CLI and SDK dependency before downloading.
pip install modelscopeDownload the complete weights, configuration and model card.
modelscope download --model google/gemma-4-31B-it-assistantREADME.md is used as an example; replace it with another repository file when needed.
modelscope download --model google/gemma-4-31B-it-assistant README.md --local_dir ./dirUseful in Python projects and automation scripts.
from modelscope import snapshot_download
model_dir = snapshot_download('google/gemma-4-31B-it-assistant')Make sure Git LFS is installed correctly.
git lfs install
git clone https://www.modelscope.cn/google/gemma-4-31B-it-assistant.gitFetch 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-assistant.gitHow to use
- 01Step 1
Read the model card and source information.
- 02Step 2
Start with a small, non-sensitive evaluation.
- 03Step 3
Review quality, licensing and usage limits.
- 04Step 4
Adopt it only after validation.
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