---
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](https://cdn-uploads.huggingface.co/production/uploads/60a75f5523ce37179774a20b/g009OEErZn7PasE9eP4k7.png)

# 🛡️ 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**, **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:

1. **Phase 1: Formal Foundation (75.5B tokens)**
   Focused on high-quality, structured Hebrew (legal, academic, and literary texts) to establish core grammatical rules.
2. **Phase 2: Colloquial Expansion (3.36B tokens)**
   Integration of social media, forums, and informal web data to handle slang and modern registers.
3. **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

### **Project Leadership**
* **MAFAT Lead:** Tal Geva (Project Lead), Matan Frank
* **Technical Lead:** Sarel Weinberger (PwC Next)

### **Core Teams**
* **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 & Collaborators**
* **Partners:** Amir Nissan Hacohen (Origin.ai)
* **Research Collaborators:** Shaltiel Shmidman (Dicta), Mike Erlihson
* **Infrastructure:** Netanel Ilouz (AWS)