---
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]:

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].
2. **Phase 2: Colloquial Expansion (3.36B tokens)**
   Integration of social media, forums, and informal web data to handle slang and modern registers[cite: 1].
3. **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]