T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground
Authors: name: Dmitrii Stoianov, name: Danil Taranets, name: Olga Tsymboi, name: Ramil Latypov, name: Almaz Dautov, name: Vladislav Kruglikov, name: Nikita Surkov, name: German Abramov, name: Pavel Gein, name: Dmitry Abulkhanov, name: Mikhail Gashkov, name: Viktor Zelenkovskiy, name: Artem Batalov, name: Aleksandr Medvedev, name: Anatolii Potapov
Organization: T-Tech

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.