Gemma SEA LION v4.5 E2B IT

--- library_name: transformers license: mit language: - en - zh - vi - id - th - fil - ta - ms - my license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any base_model: - google/gemma-4-E2B-it ---  # Model Card for Gemma-SEA-LION-v4.5-E2B-IT *Last update: 2026-05-19* SEA-LION is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asia (SEA) region. **Gemma-SEA-LION-v4.5-E2B-IT** built upon the gemma-4-E2B-it architecture with 2.3B effective (5.1B with embeddings). To ensure deep domain adaptation, the model underwent distillation from google/gemma-4-31B-it on an updated aisingapore/SEA-Instruct-2602, instilling multilingual and multicultural fluency across English and key SEA languages: Burmese, Indonesian, Filipino (Tagalog), Malay, Tamil, Thai, and Vietnamese. Gemma-SEA-LION-v4.5-E2B-IT inherits the following features from Gemma 4: - **Native System Prompt Support** – Gemma 4 introduces native support for the `system` role, enabling more structured and controllable conversations. - **Reasoning** – Highly capable reasoning model, with configurable thinking modes. - **Extended Multimodalities** – Processes Text, Image with variable aspect ratio and resolution support (all models), Video, and Audio (featured natively). - **Optimized for On-Device** – designed for efficient local execution on laptops and mobile devices. - **Enhanced Coding & Agentic Capabilities** – Achieves notable improvements in coding benchmarks alongside native function-calling support, powering highly capable autonomous agents. ## Model Details ### Model Description SEA-LION stands for Southeast Asian Languages In One Network. We performed post-training in English and SEA languages on `gemma-4-E2B-it`, a multimodal learning model using the Gemma 4 architecture, to create Gemma-SEA-LION-v4.5-E2B-IT. For tokenization, the model employs the default tokenizer used in `gemma-4-E2B-it`. - **Developed by:** AI Products Pillar, AI Singapore - **Funded by:** Singapore NRF - **Shared by:** AI Products Pillar, AI Singapore - **Model type:** Transformer Decoder with Vision and Audio Encoder - **Training Stage:** Post-Training (Logit Distillation & Model Merging) - **Context length:** 128k - **Language(s):** fine-tuned on Burmese, Indonesian,...