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 Hugging Face. 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.
@article{arxiv.2512.10430,
title = {T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground},
author = {Dmitrii Stoianov and Danil Taranets and Olga Tsymboi and Ramil Latypov and Almaz Dautov and Vladislav Kruglikov and Nikita Surkov and German Abramov and Pavel Gein and Dmitry Abulkhanov and Mikhail Gashkov and Viktor Zelenkovskiy and Artem Batalov and Aleksandr Medvedev and Anatolii Potapov},
journal= {arXiv preprint arXiv:2512.10430},
year = {2025}
}