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README.md
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T-pro-it-2.1 — is an efficient russian model built upon the Qwen 3 model family with improved instruction following and tool-calling capabilities compared to [T-pro-it-2.0](https://huggingface.co/t-tech/T-pro-it-2.0).
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Outperforms Qwen3-32B in tool calling scenarios, which is essential for agentic applications. Built for both general tasks and complex workflows.
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**NOTE: This model supports only non-thinking mode and does not generate `<think></think>` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
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### 📚 Dataset
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\* IFeval metric is mean of 4 values: prompt and instruct levels for strict and loose accuracy.
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## Recommended Generation Parameters
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```
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T-pro-it-2.1 — is an efficient russian model built upon the Qwen 3 model family with improved instruction following and tool-calling capabilities compared to [T-pro-it-2.0](https://huggingface.co/t-tech/T-pro-it-2.0).
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Outperforms Qwen3-32B in tool calling scenarios, which is essential for agentic applications. Built for both general tasks and complex workflows.
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More train details in our Habr: https://habr.com/ru/companies/tbank/articles/979650/
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**NOTE: This model supports only non-thinking mode and does not generate `<think></think>` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**
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### 📚 Dataset
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\* IFeval metric is mean of 4 values: prompt and instruct levels for strict and loose accuracy.
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More benchmarks can be found in our [Habr post](https://habr.com/ru/companies/tbank/articles/979650/).
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## Recommended Generation Parameters
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```
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