T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground
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.
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Dmitrii Stoianov
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Danil Taranets
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Olga Tsymboi
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Ramil Latypov
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Almaz Dautov
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Almaz Dautov
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Vladislav Kruglikov
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Nikita Surkov
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German Abramov
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Dmitry Abulkhanov
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Dmitry Abulkhanov
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Mikhail Gashkov
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Mikhail Gashkov
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Artem Batalov
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Artem Batalov
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Aleksandr Medvedev
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Anatolii Potapov
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Anatoly Potapov
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T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground
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.
T-pro 2.0 is an open-weight Russian LLM for hybrid reasoning and efficient inference, using a Cyrillic-dense tokenizer and EAGLE speculative-decoding pipeline.
Cyrillic-dense tokenizer EAGLE speculative-decoding pipeline hybrid reasoning efficient inference reasoning-trace generation