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Jais 2: A Family of Arabic-Centric Open Large Language Models

Summary

Most large language models are trained mostly on English. Arabic, with its complex morphology, diglossia and many regional dialects, is poorly covered, so general multilingual models handle it only partly. This is especially true for dialects and culturally specific content. The Jais team (MBZUAI, Cerebras, Inception) built Jais 2, a pair of open-weight Arabic–English models with 8B and 70B parameters, both trained from scratch.

Pretraining used more than 600B tokens of curated Arabic, including dialects and Arabizi, plus about 1.6T tokens of web, math and code data. The architecture changes a Llama-3-style transformer and uses a vocabulary built specifically for Arabic. Training ran in stages: continual pretraining, instruction tuning with task-specific slices, then preference alignment.

The authors report top results among the open models they compared on OALL2 and AraGen. They also report strong scores on culturally grounded tasks and competitive English, despite a smaller token budget than comparable models. The release gives Arabic NLP a strong, commercially usable open baseline.

Key points

  • Two sizes:
    • 8B: 32 layers, 26 heads, hidden size 3,328.
    • 70B: 68 layers, 56 heads, hidden size 7,168.
    • Both are decoder-only and use RoPE, and training was carried out using the Condor Galaxy 1 and Condor Galaxy 2 supercomputing systems.
  • Design changes from a Llama 3 baseline: a feedforward ratio widened to 8×, ReLU² activations, µP for scaling hyperparameters, and a custom 150K-token Arabic-centric vocabulary.
  • Post-training combines SFT with DPO and GRPO. The instruction data is split into dedicated tasks: dialect translation, dialect identification, cuisine, poetry, Islamic QA, dream interpretation and summarization.
  • Evaluation includes multiple-choice benchmarks (OALL2), generative benchmarks (AraGen) and new domain benchmarks, for example one for Arabic cuisine.
  • Both models are released on HuggingFace under Apache 2.0. The 70B chat app runs on Cerebras hardware at up to 2,000 tokens per second.

Interesting ideas

  • I1. Instruction data is organised as separate task slices, such as dialect identification, poetry and dream interpretation, rather than one generic pool, which makes each skill easier to target and measure. — For Mova (learn): LLM teacher tiers could produce silver data per phenomenon (e.g. passives, ellipsis, quantifiers), so each slice's gain can be measured on its own.
  • I2. Dialectal and dual-script text, including Arabizi, is treated as first-class training input rather than noise to be filtered out. — For Mova (tokens): the en tokenizer and lemmatizer could deliberately cover informal spellings, regional variants and chat-style English, mapping them to standard lemmas instead of failing on them.
  • I3. The authors built their own benchmarks for culturally specific domains (cuisine, poetry, religion) because general leaderboards miss those skills. — For Mova (answer): small genre-specific test sets for fables and tales could check whether readers capture morals and character roles, not only surface facts.
  • I4. Multiple-choice evaluation (OALL2) is kept separate from free-generation evaluation (AraGen), since a model can be good at one and weak at the other. — For Mova (answer): school-course readers could be scored both on choosing an option and on producing a justified free answer, to show whether the meaning graph really supports the answer.
  • I5. Safety is handled at three layers: data preparation, fine-tuning and prompting, so no single mechanism has to carry the whole load. — For Mova (frames): speech-act and implicature handling could be checked at several stages (frame extraction, world building, answering), each with its own explainable filter.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

14 papers of our library cite it (of the 140884 whose bibliographies we read); 9 of them have a page here, 5 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2608.13580 · arXiv 2608.13580

@misc{anwar2026jais,
  title = {{Jais 2: A Family of Arabic-Centric Open Large Language Models}},
  author = {Anwar, Mohamed and Freihat, Abed Alhakim and Ibrahim, George and Awad, Mostafa and Sadallah, Abdelrahman and Gosal, Gurpreet and Ramakrishnan, Gokulakrishnan and Chandran, Sarath and Mishra, Biswajit and Joshi, Rituraj and Frikha, Ahmed and Goffinet, Etienne and Maiti, Abhishek and Filali, Ali El and AlBarri, Sarah and Ghosh, Samujjwal and Pal, Rahul and Mullah, Parvez and Shukla, Awantika and siddiki, Sajid and Kamboj, Samta and Pandit, Onkar and Sahu, Sunil Kumar and Elbadawy, AbdelRahman and Mohamed, Amr and Chamma, Ahmad and Dufraisse, Evan and Bounhar, Abdelaziz and Bouch, Dani and Abdine, Hadi and Shang, Guokan and Koto, Fajri and Wang, Yuxia and Xie, Zhuohan and Mekky, Ali and Elbadry, Rania and Ahmad, Sarfraz and Ahsan, Momina and Herraoui, Omar El and Orel, Daniil and Iqbal, Hasan and Elzeky, Kareem and Abassy, Mervat and Elozeiri, Kareem and Eletter, Saadeldine and Atif, Farah and Mukhituly, Nurdaulet and Li, Haonan and Han, Xudong and Singh, Aaryamonvikram and Quraishi, Zainul Abedien Ahmed and Sengupta, Neha and Murray, Larry and Sheinin, Avraham and Hestness, Joel and Vassilieva, Natalia and Ren, Hector Xuguang and Liu, Zhengzhong and Vazirgiannis, Michalis and Nakov, Preslav},
  year = {2026},
  doi = {10.48550/arxiv.2608.13580},
  eprint = {2608.13580},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2608.13580}
}

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