@inproceedings{stoianov-etal-2026-pro,
title = "{T}-pro 2.0: An Efficient {R}ussian Hybrid-Reasoning Model and Playground",
author = "Stoianov, Dmitrii and
Taranets, Danil and
Tsymboi, Olga and
Latypov, Ramil and
Dautov, Almaz and
Kruglikov, Vladislav and
Nikita, Surkov and
Abramov, German and
Gein, Pavel and
Abulkhanov, Dmitry and
Gashkov, Mikhail and
Zelenkovskiy, Viktor and
Batalov, Artem and
Medvedev, Aleksandr and
Potapov, Anatolii",
editor = "Croce, Danilo and
Leidner, Jochen and
Moosavi, Nafise Sadat",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
month = mar,
year = "2026",
address = "Rabat, Marocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-demo.22/",
pages = "297--319",
ISBN = "979-8-89176-382-1",
abstract = "We introduce T-pro 2.0, an open-weight Russian LLM for hybrid reasoning and efficient inference.The model supports direct answering and reasoning-trace generation, using a Cyrillic-dense tokenizer and an adapted EAGLE speculative-decoding pipeline to reduce latency. To enable reproducible and extensible research, we release the model weights, the T-Wix 500k instruction corpus, the T-Math reasoning benchmark, and the EAGLE weights on HuggingFace. These resources allow users to study Russian-language reasoning and to extend or adapt both the model and the inference pipeline. A public web demo exposes reasoning and non-reasoning modes and illustrates the speedups achieved by our inference stack across domains.T-pro 2.0 thus serves as an accessible open system for building and evaluating efficient, practical Russian LLM applications.Demo: https://t-pro2eagle.streamlit.app/https://huggingface.co/collections/t-tech/t-pro-20"
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<abstract>We introduce T-pro 2.0, an open-weight Russian LLM for hybrid reasoning and efficient inference.The model supports direct answering and reasoning-trace generation, using a Cyrillic-dense tokenizer and an adapted EAGLE speculative-decoding pipeline to reduce latency. To enable reproducible and extensible research, we release the model weights, the T-Wix 500k instruction corpus, the T-Math reasoning benchmark, and the EAGLE weights on HuggingFace. These resources allow users to study Russian-language reasoning and to extend or adapt both the model and the inference pipeline. A public web demo exposes reasoning and non-reasoning modes and illustrates the speedups achieved by our inference stack across domains.T-pro 2.0 thus serves as an accessible open system for building and evaluating efficient, practical Russian LLM applications.Demo: https://t-pro2eagle.streamlit.app/https://huggingface.co/collections/t-tech/t-pro-20</abstract>
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%0 Conference Proceedings
%T T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground
%A Stoianov, Dmitrii
%A Taranets, Danil
%A Tsymboi, Olga
%A Latypov, Ramil
%A Dautov, Almaz
%A Kruglikov, Vladislav
%A Nikita, Surkov
%A Abramov, German
%A Gein, Pavel
%A Abulkhanov, Dmitry
%A Gashkov, Mikhail
%A Zelenkovskiy, Viktor
%A Batalov, Artem
%A Medvedev, Aleksandr
%A Potapov, Anatolii
%Y Croce, Danilo
%Y Leidner, Jochen
%Y Moosavi, Nafise Sadat
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Marocco
%@ 979-8-89176-382-1
%F stoianov-etal-2026-pro
%X We introduce T-pro 2.0, an open-weight Russian LLM for hybrid reasoning and efficient inference.The model supports direct answering and reasoning-trace generation, using a Cyrillic-dense tokenizer and an adapted EAGLE speculative-decoding pipeline to reduce latency. To enable reproducible and extensible research, we release the model weights, the T-Wix 500k instruction corpus, the T-Math reasoning benchmark, and the EAGLE weights on HuggingFace. These resources allow users to study Russian-language reasoning and to extend or adapt both the model and the inference pipeline. A public web demo exposes reasoning and non-reasoning modes and illustrates the speedups achieved by our inference stack across domains.T-pro 2.0 thus serves as an accessible open system for building and evaluating efficient, practical Russian LLM applications.Demo: https://t-pro2eagle.streamlit.app/https://huggingface.co/collections/t-tech/t-pro-20
%U https://aclanthology.org/2026.eacl-demo.22/
%P 297-319
Markdown (Informal)
[T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground](https://aclanthology.org/2026.eacl-demo.22/) (Stoianov et al., EACL 2026)
ACL
- Dmitrii Stoianov, Danil Taranets, Olga Tsymboi, Ramil Latypov, Almaz Dautov, Vladislav Kruglikov, Surkov Nikita, German Abramov, Pavel Gein, Dmitry Abulkhanov, Mikhail Gashkov, Viktor Zelenkovskiy, Artem Batalov, Aleksandr Medvedev, and Anatolii Potapov. 2026. T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 297–319, Rabat, Marocco. Association for Computational Linguistics.