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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2506.09440 |
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| author | GigaChat team Valentin, Mamedov Kosarev, Evgenii Leleytner, Gregory Shchuckin, Ilya Berezovskiy, Valeriy Smirnov, Daniil Kozlov, Dmitry Averkiev, Sergei Ivan, Lukyanenko Proshunin, Aleksandr Israfilova, Ainur Baskov, Ivan Chervyakov, Artem Shakirov, Emil Kolesov, Mikhail Khomich, Daria Latortseva, Darya Porkhun, Sergei Fedorov, Yury Kutuzov, Oleg Kudriavtseva, Polina Soldatova, Sofiia Egor, Kolodin Pyatkin, Stanislav Menshykh, Dzmitry Sergei, Grafov Damirov, Eldar Vladimir, Karlov Gaitukiev, Ruslan Shatenov, Arkadiy Fenogenova, Alena Savushkin, Nikita Minkin, Fedor |
| author_facet | GigaChat team Valentin, Mamedov Kosarev, Evgenii Leleytner, Gregory Shchuckin, Ilya Berezovskiy, Valeriy Smirnov, Daniil Kozlov, Dmitry Averkiev, Sergei Ivan, Lukyanenko Proshunin, Aleksandr Israfilova, Ainur Baskov, Ivan Chervyakov, Artem Shakirov, Emil Kolesov, Mikhail Khomich, Daria Latortseva, Darya Porkhun, Sergei Fedorov, Yury Kutuzov, Oleg Kudriavtseva, Polina Soldatova, Sofiia Egor, Kolodin Pyatkin, Stanislav Menshykh, Dzmitry Sergei, Grafov Damirov, Eldar Vladimir, Karlov Gaitukiev, Ruslan Shatenov, Arkadiy Fenogenova, Alena Savushkin, Nikita Minkin, Fedor |
| contents | Generative large language models (LLMs) have become crucial for modern NLP research and applications across various languages. However, the development of foundational models specifically tailored to the Russian language has been limited, primarily due to the significant computational resources required. This paper introduces the GigaChat family of Russian LLMs, available in various sizes, including base models and instruction-tuned versions. We provide a detailed report on the model architecture, pre-training process, and experiments to guide design choices. In addition, we evaluate their performance on Russian and English benchmarks and compare GigaChat with multilingual analogs. The paper presents a system demonstration of the top-performing models accessible via an API, a Telegram bot, and a Web interface. Furthermore, we have released three open GigaChat models in open-source (https://huggingface.co/ai-sage), aiming to expand NLP research opportunities and support the development of industrial solutions for the Russian language. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_09440 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecture GigaChat team Valentin, Mamedov Kosarev, Evgenii Leleytner, Gregory Shchuckin, Ilya Berezovskiy, Valeriy Smirnov, Daniil Kozlov, Dmitry Averkiev, Sergei Ivan, Lukyanenko Proshunin, Aleksandr Israfilova, Ainur Baskov, Ivan Chervyakov, Artem Shakirov, Emil Kolesov, Mikhail Khomich, Daria Latortseva, Darya Porkhun, Sergei Fedorov, Yury Kutuzov, Oleg Kudriavtseva, Polina Soldatova, Sofiia Egor, Kolodin Pyatkin, Stanislav Menshykh, Dzmitry Sergei, Grafov Damirov, Eldar Vladimir, Karlov Gaitukiev, Ruslan Shatenov, Arkadiy Fenogenova, Alena Savushkin, Nikita Minkin, Fedor Computation and Language Artificial Intelligence Generative large language models (LLMs) have become crucial for modern NLP research and applications across various languages. However, the development of foundational models specifically tailored to the Russian language has been limited, primarily due to the significant computational resources required. This paper introduces the GigaChat family of Russian LLMs, available in various sizes, including base models and instruction-tuned versions. We provide a detailed report on the model architecture, pre-training process, and experiments to guide design choices. In addition, we evaluate their performance on Russian and English benchmarks and compare GigaChat with multilingual analogs. The paper presents a system demonstration of the top-performing models accessible via an API, a Telegram bot, and a Web interface. Furthermore, we have released three open GigaChat models in open-source (https://huggingface.co/ai-sage), aiming to expand NLP research opportunities and support the development of industrial solutions for the Russian language. |
| title | GigaChat Family: Efficient Russian Language Modeling Through Mixture of Experts Architecture |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2506.09440 |