enver/aynengine-v3-arabic-standalone
AynEngine-V3-Arabic-Standalone is a 0.8 billion parameter sovereign language model developed by Enver, AynEngine Research Initiative, and University of Prishtina, specifically trained on 72 Classical Arabic Masterworks (83 MB, 9.17 Million Words). It features a unique dual-stream cognitive engine with an independent 9,057-dimensional Epistemic Next-Root Invariant Head for predicting semantic roots and a Surface Text Stream for generating grammatically rigorous Classical Arabic. This model excels in generating high-quality Classical Arabic prose and structured reasoning code, incorporating an explicit anti-hallucination protocol based on classical Islamic epistemology.
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AynEngine V3: Classical Arabic Sovereign LLM
AynEngine-V3-Arabic-Standalone is a specialized 0.8 billion parameter language model developed by Enver, AynEngine Research Initiative, and University of Prishtina. It is uniquely trained on a corpus of 72 Classical Arabic Masterworks, totaling 83 MB of text, including foundational works by Imam al-Ghazali, Imam al-Razi, and seminal lexicons like Kitab Sibawayh and Kitab al-'Ayn. This model is designed for deep understanding and generation of Classical Arabic (al-Fusha al-Turathiyya).
Key Capabilities
- Dual-Stream Epistemic Architecture: Unlike standard Transformers, it features a dedicated Hierarchical Root Projection. It has an Epistemic Invariant Stream that predicts the next semantic root milestone across 9,057 canonical Arabic roots in 176 milliseconds, and a Surface Text Stream for autoregressive generation of grammatically rigorous Classical Arabic and structured reasoning code.
- Classical Arabic Mastery: Fine-tuned on authentic classical manuscripts, ensuring high fidelity to traditional Arabic grammar and lexicon.
- Anti-Hallucination Protocol: Incorporates an "Epistemic Refusal Protocol" (Qawa'id al-Burhan), calibrated to explicitly state "I do not know. I lack verified records for this." when queried about unverified information, preventing confabulation.
- Semantic Concept Transfer: The root prediction head demonstrates spontaneous semantic mapping to modern technical concepts, indicating a deeper understanding beyond surface text.
Good for
- Generating grammatically precise Classical Arabic prose and structured reasoning code.
- Research and applications requiring deep semantic understanding of classical Arabic texts.
- Use cases where explicit knowledge boundaries and anti-hallucination are critical.
- Scholarly analysis and digital humanities projects focused on Islamic intellectual heritage.