Fanar 2.0: Arabic Generative AI Stack
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arXiv
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| Natura: | Preprint |
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2026
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| author | FANAR TEAM Abbas, Ummar Ahmad, Mohammad Shahmeer Ahmad, Minhaj Al-Homaid, Abdulaziz Al-Nuaimi, Anas Altinisik, Enes Asgari, Ehsaneddin Chawla, Sanjay Chowdhury, Shammur Dalvi, Fahim Darwish, Kareem Durrani, Nadir Elfeky, Mohamed Elmagarmid, Ahmed Eltabakh, Mohamed Ersoy, Asim Fatehkia, Masoomali Hashim, Mohammed Qusay Hawasly, Majd Hefeeda, Mohamed Husaini, Mus'ab Isufaj, Keivin Jung, Soon-Gyo Lachemat, Houssam Lucas, Ji Kim Mohamed, Abubakr Mohiuddin, Tasnim Mousi, Basel Mubarak, Hamdy Musleh, Ahmad Ouzzani, Mourad Sadeghi, Amin Sencar, Husrev Taha Shinoy, Mohammed Sinan, Omar Zhang, Yifan |
| author_facet | FANAR TEAM Abbas, Ummar Ahmad, Mohammad Shahmeer Ahmad, Minhaj Al-Homaid, Abdulaziz Al-Nuaimi, Anas Altinisik, Enes Asgari, Ehsaneddin Chawla, Sanjay Chowdhury, Shammur Dalvi, Fahim Darwish, Kareem Durrani, Nadir Elfeky, Mohamed Elmagarmid, Ahmed Eltabakh, Mohamed Ersoy, Asim Fatehkia, Masoomali Hashim, Mohammed Qusay Hawasly, Majd Hefeeda, Mohamed Husaini, Mus'ab Isufaj, Keivin Jung, Soon-Gyo Lachemat, Houssam Lucas, Ji Kim Mohamed, Abubakr Mohiuddin, Tasnim Mousi, Basel Mubarak, Hamdy Musleh, Ahmad Ouzzani, Mourad Sadeghi, Amin Sencar, Husrev Taha Shinoy, Mohammed Sinan, Omar Zhang, Yifan |
| contents | We present Fanar 2.0, the second generation of Qatar's Arabic-centric Generative AI platform. Sovereignty is a first-class design principle: every component, from data pipelines to deployment infrastructure, was designed and operated entirely at QCRI, Hamad Bin Khalifa University. Fanar 2.0 is a story of resource-constrained excellence: the effort ran on 256 NVIDIA H100 GPUs, with Arabic having only ~0.5% of web data despite 400 million native speakers. Fanar 2.0 adopts a disciplined strategy of data quality over quantity, targeted continual pre-training, and model merging to achieve substantial gains within these constraints. At the core is Fanar-27B, continually pre-trained from a Gemma-3-27B backbone on a curated corpus of 120 billion high-quality tokens across three data recipes. Despite using 8x fewer pre-training tokens than Fanar 1.0, it delivers substantial benchmark improvements: Arabic knowledge (+9.1 pts), language (+7.3 pts), dialects (+3.5 pts), and English capability (+7.6 pts). Beyond the core LLM, Fanar 2.0 introduces a rich stack of new capabilities. FanarGuard is a state-of-the-art 4B bilingual moderation filter for Arabic safety and cultural alignment. The speech family Aura gains a long-form ASR model for hours-long audio. Oryx vision family adds Arabic-aware image and video understanding alongside culturally grounded image generation. An agentic tool-calling framework enables multi-step workflows. Fanar-Sadiq utilizes a multi-agent architecture for Islamic content. Fanar-Diwan provides classical Arabic poetry generation. FanarShaheen delivers LLM-powered bilingual translation. A redesigned multi-layer orchestrator coordinates all components through intent-aware routing and defense-in-depth safety validation. Taken together, Fanar 2.0 demonstrates that sovereign, resource-constrained AI development can produce systems competitive with those built at far greater scale. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16397 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fanar 2.0: Arabic Generative AI Stack FANAR TEAM Abbas, Ummar Ahmad, Mohammad Shahmeer Ahmad, Minhaj Al-Homaid, Abdulaziz Al-Nuaimi, Anas Altinisik, Enes Asgari, Ehsaneddin Chawla, Sanjay Chowdhury, Shammur Dalvi, Fahim Darwish, Kareem Durrani, Nadir Elfeky, Mohamed Elmagarmid, Ahmed Eltabakh, Mohamed Ersoy, Asim Fatehkia, Masoomali Hashim, Mohammed Qusay Hawasly, Majd Hefeeda, Mohamed Husaini, Mus'ab Isufaj, Keivin Jung, Soon-Gyo Lachemat, Houssam Lucas, Ji Kim Mohamed, Abubakr Mohiuddin, Tasnim Mousi, Basel Mubarak, Hamdy Musleh, Ahmad Ouzzani, Mourad Sadeghi, Amin Sencar, Husrev Taha Shinoy, Mohammed Sinan, Omar Zhang, Yifan Computation and Language Artificial Intelligence We present Fanar 2.0, the second generation of Qatar's Arabic-centric Generative AI platform. Sovereignty is a first-class design principle: every component, from data pipelines to deployment infrastructure, was designed and operated entirely at QCRI, Hamad Bin Khalifa University. Fanar 2.0 is a story of resource-constrained excellence: the effort ran on 256 NVIDIA H100 GPUs, with Arabic having only ~0.5% of web data despite 400 million native speakers. Fanar 2.0 adopts a disciplined strategy of data quality over quantity, targeted continual pre-training, and model merging to achieve substantial gains within these constraints. At the core is Fanar-27B, continually pre-trained from a Gemma-3-27B backbone on a curated corpus of 120 billion high-quality tokens across three data recipes. Despite using 8x fewer pre-training tokens than Fanar 1.0, it delivers substantial benchmark improvements: Arabic knowledge (+9.1 pts), language (+7.3 pts), dialects (+3.5 pts), and English capability (+7.6 pts). Beyond the core LLM, Fanar 2.0 introduces a rich stack of new capabilities. FanarGuard is a state-of-the-art 4B bilingual moderation filter for Arabic safety and cultural alignment. The speech family Aura gains a long-form ASR model for hours-long audio. Oryx vision family adds Arabic-aware image and video understanding alongside culturally grounded image generation. An agentic tool-calling framework enables multi-step workflows. Fanar-Sadiq utilizes a multi-agent architecture for Islamic content. Fanar-Diwan provides classical Arabic poetry generation. FanarShaheen delivers LLM-powered bilingual translation. A redesigned multi-layer orchestrator coordinates all components through intent-aware routing and defense-in-depth safety validation. Taken together, Fanar 2.0 demonstrates that sovereign, resource-constrained AI development can produce systems competitive with those built at far greater scale. |
| title | Fanar 2.0: Arabic Generative AI Stack |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2603.16397 |