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
🛡️ HEBATRON: Hebrew-Specialized Mamba2-MoE
HEBATRON is a state-of-the-art, high-performance language model specialized for the Hebrew language[cite: 1]. Developed through a collaboration between PwC Israel and MAFAT, it introduces a unique hybrid architecture combining Mamba2 and Mixture-of-Experts (MoE)[cite: 1].
🚀 Model Summary
HEBATRON is designed to handle the structural and morphological complexities of Hebrew while providing linear scaling for long-context tasks[cite: 1]. It is a localized and enhanced version of the Nemotron-3-Nano-30B framework, optimized for native-level reasoning in Hebrew and English[cite: 1].
📂 Technical Specifications
| Feature | Specification |
|---|---|
| Model Name | HEBATRON[cite: 1] |
| Architecture | Hybrid Mamba2 (SSM) + Sparse MoE[cite: 1] |
| Total Parameters | 30B[cite: 1] |
| Active Parameters | ~3B per token[cite: 1] |
| Context Window | 65,536 (64k) tokens[cite: 1] |
| Hardware | NVIDIA Blackwell (B300) & H200 GPUs[cite: 1] |
| Precision | FP8 Mixed-Precision[cite: 1] |
🧬 Training Curriculum
The model was trained using a three-phase Curriculum Learning strategy[cite: 1]:
- Phase 1: Formal Foundation (75.5B tokens) Focused on high-quality, structured Hebrew (legal, academic, and literary texts) to establish core grammatical rules[cite: 1].
- Phase 2: Colloquial Expansion (3.36B tokens) Integration of social media, forums, and informal web data to handle slang and modern registers[cite: 1].
- Phase 3: Long-Context Extension (20.4B tokens) Fine-tuning on dense, long-form documents to stabilize the 64k context window[cite: 1].
Alignment: Supervised Fine-Tuning (SFT) was performed on 2 million samples, including localized knowledge distillation and the "Hebrew IFEval" dataset[cite: 1].
📊 Performance Evaluation
Hebrew Reasoning Benchmarks
- SNLI (Semantic Reasoning): 91.2% accuracy[cite: 1]
- Israeli Trivia: 72.1% (+14pt vs base)[cite: 1]
- Hebrew Average Reasoning: 73.8% (Surpassing DictaLM-3.0-Thinking)[cite: 1]
- GSM8K (Math): 83.3% accuracy in native Hebrew[cite: 1]
English Reasoning Benchmarks
- Psychometric Psi (EN): 91.6%[cite: 1]
- English Reasoning Average: 86.0%[cite: 1]
🎯 Intended Use & Limitations
- Intended Use: Advanced Hebrew document analysis, long-context summarization (legal/technical), and complex bilingual reasoning[cite: 1].
- Limitations: Users should verify outputs for factual accuracy as with any Large Language Model[cite: 1].
🤝 Credits
- Developed by: PwC Israel & MAFAT[cite: 1]
- Technical Lead: Sarel Weinberger (Co-founder, Binatna)[cite: 1]
- Research Collaborators: Shaltiel Shmidman (Dicta), Dan Revital (PwC Next)[cite: 1]