Update README.md

README.md

@@ -1,3 +1,4 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+Model Card: HEBATRONModel SummaryHEBATRON is a high-performance, Hebrew-specialized language model developed by PwC Israel in collaboration with MAFAT. It features a cutting-edge Mamba2 + Mixture-of-Experts (MoE) hybrid architecture, designed to provide superior reasoning capabilities and linear scaling for long-context processing. The model is a localized and enhanced version of the Nemotron-3-Nano-30B framework, optimized specifically for the linguistic complexities of the Hebrew language.  Technical SpecificationsModel Name: HEBATRON.  Architecture: Hybrid Mamba2 (State Space Model) + Sparse Mixture-of-Experts (MoE).  Parameters: 30B total parameters (~3B active parameters per token).  Context Window: 65,536 (64k) tokens.  Primary Languages: Hebrew and English.  Training Infrastructure: NVIDIA Blackwell (B300) and H200 GPUs on AWS EC2 P6/P5 instances, utilizing FP8 mixed-precision.  Training Curriculum & StrategyHEBATRON was trained using a three-phase Curriculum Learning strategy to master Hebrew morphology while retaining global reasoning skills:  Phase 1: Formal Foundation (~75.5B tokens): Focused on high-quality, structured Hebrew (legal, academic, and literary texts) to establish core grammatical and syntactical rules.  Phase 2: Colloquial Expansion (~3.36B tokens): Integration of social media, forums, and informal web data to handle slang and modern Hebrew registers.  Phase 3: Long-Context Extension (~20.4B tokens): Fine-tuning on dense, long-form documents to stabilize the Mamba2 and MoE routing mechanisms across its 64k context window.  Supervised Fine-Tuning (SFT): Alignment on 2 million samples, including localized knowledge distillation and a specialized "Hebrew IFEval" dataset for strict instructional adherence.  Performance EvaluationHEBATRON sets a new benchmark for sovereign Hebrew language models, particularly in logical reasoning and cultural knowledge.  Hebrew BenchmarksSNLI (Semantic Reasoning): 91.2% accuracy.  Israeli Trivia: 72.1% (a 14-point increase over the base model).  Hebrew Average Reasoning: 73.8%, surpassing other major local models like DictaLM-3.0-Thinking.  GSM8K (Mathematical Reasoning): 83.3% accuracy in native Hebrew.  English BenchmarksThe model successfully avoids catastrophic forgetting, maintaining high proficiency in English-centric tasks:  Psychometric Psi (EN): 91.6%.  English Reasoning Average: 86.0%.  Intended Use & LimitationsIntended Use: Advanced Hebrew document analysis, long-context summarization (legal/technical), and complex bilingual (HE/EN) reasoning tasks.  Limitations: While the Mamba2+MoE architecture excels at long-context processing, users should verify outputs for factual accuracy as with any LLM.  CreditsDeveloped by: PwC Israel & MAFAT.