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t-tech/
T-pro-it-2.0-AWQ

Name: T-pro-it-2.0-AWQ
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T-pro-it-2.0-AWQ

Main BF16 model: t-tech/T-pro-it-2.0

🚨 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.

T‑pro‑it‑2.0‑AWQ is a fine‑grained AWQ‑quantised version of T‑pro‑it‑2.0 (built on the Qwen‑3 family). AWQ 4-bit (W4A16_ASYM) offers comparable performance with approximately one-quarter the memory footprint and significantly faster inference.

Description

T-pro-it-2.0 is a model built upon the Qwen 3 model family and incorporates both continual pre-training and alignment techniques.

📚 Dataset

Instruction Pre-Training: 40B tokens of instruction data, with one-third focused on reasoning tasks.

Supervised Fine-Tuning (SFT): ~500K high-quality and diverse instructions with balanced complexity. Reasoning tasks make up about 20% of the dataset.

Preference Tuning: ~100K carefully selected instructions, filtered by length and type for general tasks and with domain-balanced selection for reasoning tasks.

📊 Benchmarks

TBD

Note on AWQ

For convenience and performance, we have provided awq-quantized model checkpoint for T-pro-it-2.0, whose name ends with -AWQ. You can find more details in the quantization_config field in config.json. However, please keep in mind the following known limitation:

There may be issues when running inference with transformers or sglang. We currently only guarantee stable performance with vllm version 0.9.0 or higher.

Switching Between Thinking and Non‑Thinking Modes

To enable or disable reasoning mode in HuggingFace, set the enable_thinking flag in tokenizer.apply_chat_template.
For more details, see:

Recommended Generation Parameters

Mode	Temperature	presence_penalty
No‑think (general requests)	≤ 0.3	1.0
Think mode (standard requests)	≈ 0.6	1.0
Complex reasoning requests	≥ 0.8	1.0

👨‍💻 Examples of usage

Deployment

For deployment, you can use vllm>=0.9.0 or to create an OpenAI-compatible API endpoint:

vLLM:
vllm serve t-tech/T‑pro‑it‑2.0‑AWQ --enable-reasoning --reasoning-parser qwen3

📖 Citation

If you use this model in your research or projects, please cite:

bibtex

@inproceedings{stoianov-etal-2026-pro,
    title = "{T}-pro 2.0: An Efficient {R}ussian Hybrid-Reasoning Model and Playground",
    author = "Stoianov, Dmitrii  and
      Taranets, Danil  and
      Tsymboi, Olga  and
      Latypov, Ramil  and
      Dautov, Almaz  and
      Kruglikov, Vladislav  and
      Nikita, Surkov  and
      Abramov, German  and
      Gein, Pavel  and
      Abulkhanov, Dmitry  and
      Gashkov, Mikhail  and
      Zelenkovskiy, Viktor  and
      Batalov, Artem  and
      Medvedev, Aleksandr  and
      Potapov, Anatolii",
    editor = "Croce, Danilo  and
      Leidner, Jochen  and
      Moosavi, Nafise Sadat",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.eacl-demo.22/",
    doi = "10.18653/v1/2026.eacl-demo.22",
    pages = "297--319",
    ISBN = "979-8-89176-382-1"
   }

t-tech/T-pro-it-2.0-AWQ