From the model card
Fields below are copied from the tags and counters on the HuggingFace repository google/gemma-4-31B-it at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- Pipeline tag
- image-text-to-text
- Library
- Transformers
- Weight formats
- safetensors
- License tag
apache-2.0— read the license file in the repo before relying on it- Lineage
-
- base model google/gemma-4-31B
- fine-tune of google/gemma-4-31B
- Papers cited
- arXiv:2607.02770
- Downloads (HF counter at last fetch)
- 7,973,406
- Likes (HF counter at last fetch)
- 3,712
- Model card
- https://huggingface.co/google/gemma-4-31B-it
Use cases
- High-quality multimodal QA and visual reasoning on single or multi-image inputs
- Document and chart understanding requiring larger model capacity
- Local deployment for privacy-sensitive VLM applications
- Research into open-weight multimodal model capabilities at 30B scale
- Replacing proprietary VLM APIs for cost-sensitive production workloads
Pros
- Apache 2.0 license for commercial use without restrictions
- 31B scale provides strong visual and language reasoning
- Part of actively maintained Gemma 4 family with Google DeepMind quality control
- HuggingFace Transformers native integration
Cons
- 31B parameters require multi-GPU or high-VRAM single GPU (A100 or H100) setup
- Larger context images significantly increase memory requirements
- Inference speed at 31B is slow for interactive applications without batching
- Quantized deployment may reduce accuracy on complex reasoning tasks
- Newer Gemma generations may supersede this quickly given Google's release cadence
Tags
transformerssafetensorsgemma4image-text-to-textconversationalarxiv:2607.02770base_model:google/gemma-4-31Bbase_model:finetune:google/gemma-4-31Blicense:apache-2.0eval-resultsendpoints_compatibledeploy:sagemakerdeploy:azureregion:us