From the model card
Fields below are copied from the tags and counters on the HuggingFace repository google/gemma-4-E4B-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
- any-to-any
- 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-E4B
- fine-tune of google/gemma-4-E4B
- Papers cited
- arXiv:2607.02770
- Downloads (HF counter at last fetch)
- 4,740,694
- Likes (HF counter at last fetch)
- 1,530
- Model card
- https://huggingface.co/google/gemma-4-E4B-it
Use cases
- On-device multimodal AI inference on Android or edge hardware
- Mobile application integration requiring vision and language understanding
- Privacy-sensitive multimodal inference where data must not leave the device
- Edge AI deployments combining text and image understanding at low power
- Research into efficient multimodal models at 4B scale
Pros
- Apache 2.0 license for unrestricted deployment
- Edge-optimized design for mobile and on-device inference
- 4B scale provides meaningful multimodal capability for its size
- Google DeepMind quality assurance and HuggingFace Transformers support
Cons
- 'Any-to-any' scope and deployment requirements need verification against specific edge hardware
- 4B multimodal models still require modern mobile GPU support for real-time inference
- Edge deployment tooling (TFLite, ONNX) compatibility requires validation
- Accuracy gaps vs. server-side models at 31B scale are significant
- Early in community adoption — fewer tutorials and integrations than larger Gemma variants
Tags
transformerssafetensorsgemma4image-text-to-textany-to-anyarxiv:2607.02770base_model:google/gemma-4-E4Bbase_model:finetune:google/gemma-4-E4Blicense:apache-2.0eval-resultsendpoints_compatibledeploy:sagemakerdeploy:azureregion:us