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
Published: 2025-12-11 08:40:10

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 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.