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About this paper
Dmitrii Stoianov, Danil Taranets, Olga Tsymboi, Ramil Latypov, Almaz Dautov, Vladislav Kruglikov, Nikita Surkov, German Abramov and 7 more
T-pro 2.0 introduces an open-weight Russian Large Language Model capable of hybrid reasoning, supported by an interactive platform and open-source resources. The model achieves up to 1.85x inference speedups through a Cyrillic-dense tokenizer and speculative decoding, while setting new benchmarks for Russian-language mathematical reasoning and general dialogue.
Key insights
- Targeted adaptation of multilingual foundation models with language-specific tokenizer optimization and a comprehensive instruction-following curriculum can yield highly performant, open-source LLMs for less-resourced languages without degrading cross-lingual performance.
- A meticulously curated and filtered instruction dataset, incorporating teacher-generated reasoning traces and balancing domain/difficulty, is crucial for developing robust hybrid reasoning capabilities in LLMs.
- Speculative decoding, even with a lightweight draft model, significantly enhances inference efficiency for LLMs, with higher speedups observed in predictable, technical content, making models more practical for real-world applications.
Summary by alphaXiv.
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