Fanar: An Arabic-Centric Multimodal Generative AI Platform
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| Format: | Preprint |
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2025
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| author | Fanar Team Abbas, Ummar Ahmad, Mohammad Shahmeer Alam, Firoj Altinisik, Enes Asgari, Ehsannedin Boshmaf, Yazan Boughorbel, Sabri Chawla, Sanjay Chowdhury, Shammur Dalvi, Fahim Darwish, Kareem Durrani, Nadir Elfeky, Mohamed Elmagarmid, Ahmed Eltabakh, Mohamed Fatehkia, Masoomali Fragkopoulos, Anastasios Hasanain, Maram Hawasly, Majd Husaini, Mus'ab Jung, Soon-Gyo Lucas, Ji Kim Magdy, Walid Messaoud, Safa Mohamed, Abubakr Mohiuddin, Tasnim Mousi, Basel Mubarak, Hamdy Musleh, Ahmad Naeem, Zan Ouzzani, Mourad Popovic, Dorde Sadeghi, Amin Sencar, Husrev Taha Shinoy, Mohammed Sinan, Omar Zhang, Yifan Ali, Ahmed Kheir, Yassine El Ma, Xiaosong Ruan, Chaoyi |
| author_facet | Fanar Team Abbas, Ummar Ahmad, Mohammad Shahmeer Alam, Firoj Altinisik, Enes Asgari, Ehsannedin Boshmaf, Yazan Boughorbel, Sabri Chawla, Sanjay Chowdhury, Shammur Dalvi, Fahim Darwish, Kareem Durrani, Nadir Elfeky, Mohamed Elmagarmid, Ahmed Eltabakh, Mohamed Fatehkia, Masoomali Fragkopoulos, Anastasios Hasanain, Maram Hawasly, Majd Husaini, Mus'ab Jung, Soon-Gyo Lucas, Ji Kim Magdy, Walid Messaoud, Safa Mohamed, Abubakr Mohiuddin, Tasnim Mousi, Basel Mubarak, Hamdy Musleh, Ahmad Naeem, Zan Ouzzani, Mourad Popovic, Dorde Sadeghi, Amin Sencar, Husrev Taha Shinoy, Mohammed Sinan, Omar Zhang, Yifan Ali, Ahmed Kheir, Yassine El Ma, Xiaosong Ruan, Chaoyi |
| contents | We present Fanar, a platform for Arabic-centric multimodal generative AI systems, that supports language, speech and image generation tasks. At the heart of Fanar are Fanar Star and Fanar Prime, two highly capable Arabic Large Language Models (LLMs) that are best in the class on well established benchmarks for similar sized models. Fanar Star is a 7B (billion) parameter model that was trained from scratch on nearly 1 trillion clean and deduplicated Arabic, English and Code tokens. Fanar Prime is a 9B parameter model continually trained on the Gemma-2 9B base model on the same 1 trillion token set. Both models are concurrently deployed and designed to address different types of prompts transparently routed through a custom-built orchestrator. The Fanar platform provides many other capabilities including a customized Islamic Retrieval Augmented Generation (RAG) system for handling religious prompts, a Recency RAG for summarizing information about current or recent events that have occurred after the pre-training data cut-off date. The platform provides additional cognitive capabilities including in-house bilingual speech recognition that supports multiple Arabic dialects, voice and image generation that is fine-tuned to better reflect regional characteristics. Finally, Fanar provides an attribution service that can be used to verify the authenticity of fact based generated content.
The design, development, and implementation of Fanar was entirely undertaken at Hamad Bin Khalifa University's Qatar Computing Research Institute (QCRI) and was sponsored by Qatar's Ministry of Communications and Information Technology to enable sovereign AI technology development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13944 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fanar: An Arabic-Centric Multimodal Generative AI Platform Fanar Team Abbas, Ummar Ahmad, Mohammad Shahmeer Alam, Firoj Altinisik, Enes Asgari, Ehsannedin Boshmaf, Yazan Boughorbel, Sabri Chawla, Sanjay Chowdhury, Shammur Dalvi, Fahim Darwish, Kareem Durrani, Nadir Elfeky, Mohamed Elmagarmid, Ahmed Eltabakh, Mohamed Fatehkia, Masoomali Fragkopoulos, Anastasios Hasanain, Maram Hawasly, Majd Husaini, Mus'ab Jung, Soon-Gyo Lucas, Ji Kim Magdy, Walid Messaoud, Safa Mohamed, Abubakr Mohiuddin, Tasnim Mousi, Basel Mubarak, Hamdy Musleh, Ahmad Naeem, Zan Ouzzani, Mourad Popovic, Dorde Sadeghi, Amin Sencar, Husrev Taha Shinoy, Mohammed Sinan, Omar Zhang, Yifan Ali, Ahmed Kheir, Yassine El Ma, Xiaosong Ruan, Chaoyi Computation and Language Artificial Intelligence I.2.0; D.2.0 We present Fanar, a platform for Arabic-centric multimodal generative AI systems, that supports language, speech and image generation tasks. At the heart of Fanar are Fanar Star and Fanar Prime, two highly capable Arabic Large Language Models (LLMs) that are best in the class on well established benchmarks for similar sized models. Fanar Star is a 7B (billion) parameter model that was trained from scratch on nearly 1 trillion clean and deduplicated Arabic, English and Code tokens. Fanar Prime is a 9B parameter model continually trained on the Gemma-2 9B base model on the same 1 trillion token set. Both models are concurrently deployed and designed to address different types of prompts transparently routed through a custom-built orchestrator. The Fanar platform provides many other capabilities including a customized Islamic Retrieval Augmented Generation (RAG) system for handling religious prompts, a Recency RAG for summarizing information about current or recent events that have occurred after the pre-training data cut-off date. The platform provides additional cognitive capabilities including in-house bilingual speech recognition that supports multiple Arabic dialects, voice and image generation that is fine-tuned to better reflect regional characteristics. Finally, Fanar provides an attribution service that can be used to verify the authenticity of fact based generated content. The design, development, and implementation of Fanar was entirely undertaken at Hamad Bin Khalifa University's Qatar Computing Research Institute (QCRI) and was sponsored by Qatar's Ministry of Communications and Information Technology to enable sovereign AI technology development. |
| title | Fanar: An Arabic-Centric Multimodal Generative AI Platform |
| topic | Computation and Language Artificial Intelligence I.2.0; D.2.0 |
| url | https://arxiv.org/abs/2501.13944 |