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README.md

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+---
+language:
+- en
+base_model: t-tech/T-pro-it-2.1
+tags:
+- llama-cpp
+- gguf
+license: apache-2.0
+---
+
+# T-pro-it-2.1-GGUF
+
+**🚨 Users are advised to exercise caution and are responsible for any additional training and oversight required to ensure the model's responses meet acceptable ethical and safety standards. The responsibility for incorporating this model into industrial or commercial solutions lies entirely with those who choose to deploy it.**
+
+
+This repository contains **T-pro-it-2.1** converted to the **GGUF** format with
+[llama.cpp](https://github.com/ggerganov/llama.cpp).  
+See the original BF16 model here: [t-tech/T-pro-it-2.1](https://huggingface.co/t-tech/T-pro-it-2.1).
+
+
+## Description
+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). 
+Outperforms Qwen3-32B in tool calling scenarios, which is essential for agentic applications. Built for both general tasks and complex workflows.
+
+**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.**
+
+
+## 📊 Benchmarks
+
+|                     | Ru Arena Hard | ruIFeval* | ruBFCL |
+|---------------------|---------------|----------|--------|
+| T-pro-it-2.1        | 93.8          | 80.7     | 66.0   |
+| T-pro-it-2.1-Q8_0   | 94.2          | 80.8     | 65.8   |
+| T-pro-it-2.1-Q6_K   | 93.4          | 80.0     | 65.9   |
+| T-pro-it-2.1-Q5_K_M | 92.7          | 81.4     | 65.7   |
+| T-pro-it-2.1-Q5_K_S | 92.3          | 80.4     | 65.2   |
+| T-pro-it-2.1-Q5_0   | 93.8          | 79.9     | 64.8   |
+| T-pro-it-2.1-Q4_K_M | 92.6          | 80.7     | 64.8   |
+
+\* IFeval metric is mean of 4 values: prompt and instruct levels for strict and loose accuracy.
+
+> **Recommendation:** choose the **highest-quality quantisation that fits your hardware** (VRAM / RAM).
+
+| Filename (→ `-gguf`) | Quant method | Bits | Size (GB) |
+|----------------------|--------------|------|-----------|
+| `T-pro-it-2.1-q8_0`        | Q8_0      | 8  | 34.8 |
+| `T-pro-it-2.1-q6_k`        | Q6_K      | 6  | 26.9 |
+| `T-pro-it-2.1-q5_k_m`      | Q5_K_M    | 5  | 23.2 |
+| `T-pro-it-2.1-q5_k_s`      | Q5_K_S    | 5  | 22.6 |
+| `T-pro-it-2.1-q5_0`        | Q5_0      | 5  | 22.6 |
+| `T-pro-it-2.1-q4_k_m`      | Q4_K_M    | 4  | 19.8 |
+
+*Size figures assume **no GPU off-loading**. Off-loading lowers RAM usage and uses VRAM instead.*
+
+## Quickstart
+
+### llama.cpp
+
+Check out our [llama.cpp documentation](https://qwen.readthedocs.io/en/latest/run_locally/llama.cpp.html) for more usage guide.
+
+We advise you to clone [`llama.cpp`](https://github.com/ggerganov/llama.cpp) and install it following the official guide. We follow the latest version of llama.cpp. 
+In the following demonstration, we assume that you are running commands under the repository `llama.cpp`.
+
+```shell
+./llama-cli -hf t-tech/T-pro-it-2.1-GGUF:Q8_0 --jinja --color -ngl 99 -fa -sm row --temp 0.6 --presence-penalty 1.0 -c 40960 -n 32768 --no-context-shift
+```
+
+### ollama
+
+Check out our [ollama documentation](https://qwen.readthedocs.io/en/latest/run_locally/ollama.html) for more usage guide.
+
+You can run T-pro-2.1 with one command:
+
+```shell
+ollama run t-tech/T-pro-it-2.1:q8_0
+```
+
+See also [t-tech ollama homepage](https://ollama.com/t-tech/T-pro-it-2.1).

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