language:
- he
- en license: apache-2.0 library_name: mamba tags:
- mamba2
- moe
- hebrew
- finance
- legal
- ssm model_name: HEBATRON base_model: nvidia/nemotron-3-nano-30b-base pipeline_tag: text-generation image
🛡️ HEBATRON: Hebrew-Specialized Mamba2-MoE HEBATRON is a state-of-the-art, high-performance language model specialized for the Hebrew language. Developed through a collaboration between PwC Israel and MAFAT and AWS, it introduces a unique hybrid architecture combining Mamba2 and Mixture-of-Experts (MoE).
🚀 Model Summary HEBATRON is designed to handle the structural and morphological complexities of Hebrew while providing linear scaling for long-context tasks. It is a localized and enhanced version of the Nemotron-3-Nano-30B framework, optimized for native-level reasoning in Hebrew and English.
📂 Technical Specifications Feature Specification Model Name HEBATRON Architecture Hybrid Mamba2 (SSM) + Sparse MoE Total Parameters 31.6B Active Parameters ~3B per token Context Window 65,536 (64k) tokens Hardware NVIDIA Blackwell (B300) & H200 GPUs Precision FP8 Mixed-Precision 🧬 Training Curriculum The model was trained using a three-phase Curriculum Learning strategy:
Phase 1: Formal Foundation (75.5B tokens) Focused on high-quality, structured Hebrew (legal, academic, and literary texts) to establish core grammatical rules. Phase 2: Colloquial Expansion (3.36B tokens) Integration of social media, forums, and informal web data to handle slang and modern registers. Phase 3: Long-Context Extension (20.4B tokens) Fine-tuning on dense, long-form documents to stabilize the 64k context window.
📊 Performance Evaluation Hebrew Reasoning Benchmarks SNLI (Semantic Reasoning): 91.2% accuracy Israeli Trivia: 72.1% (+14pt vs base) Hebrew Average Reasoning: 73.8% (Surpassing DictaLM-3.0-Thinking) GSM8K (Math): 83.3% accuracy in native Hebrew English Reasoning Benchmarks Psychometric Psi (EN): 91.6% English Reasoning Average: 86.0% 🎯 Intended Use & Limitations Intended Use: Advanced Hebrew document analysis, long-context summarization (legal/technical), and complex bilingual reasoning. Limitations: Users should verify outputs for factual accuracy as with any Large Language Model. 🤝 Credits Developed by: PwC Israel & MAFAT MAFAT Lead: Tal Geva [project Lead], Matan Frank Technical Lead: Sarel Weinberger (PwC Next) PwC Israel Team: Noam Kayzer, Dan Revital, Ori Bar Joseph, Smadar Arbatz, Or Levi, Kate Zinkovskaia, Zevi Apini, Omer Baruch (PwC Next) MAFAT Team: Noam Ordan, Nadav Cordova Partners: Amir Nissan Hacohen (Origin.ai) Research Collaborators: Shaltiel Shmidman (Dicta), Mike Erlihson AWS Infrastructures: Ilouz Netanel