Instructions to use t-tech/T-pro-it-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use t-tech/T-pro-it-2.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="t-tech/T-pro-it-2.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("t-tech/T-pro-it-2.0")
model = AutoModelForCausalLM.from_pretrained("t-tech/T-pro-it-2.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use t-tech/T-pro-it-2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "t-tech/T-pro-it-2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "t-tech/T-pro-it-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/t-tech/T-pro-it-2.0

SGLang

How to use t-tech/T-pro-it-2.0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "t-tech/T-pro-it-2.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "t-tech/T-pro-it-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "t-tech/T-pro-it-2.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "t-tech/T-pro-it-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use t-tech/T-pro-it-2.0 with Docker Model Runner:
```
docker model run hf.co/t-tech/T-pro-it-2.0
```

the-hir0 commited on Mar 31

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@@ -299,4 +299,41 @@ T-pro-it-2.0 natively supports a context length of 32,768 tokens.
 For conversations where the input significantly exceeds this limit, follow the recommendations from the [Qwen3 model card](https://huggingface.co/Qwen/Qwen3-235B-A22B#processing-long-texts) on processing long texts.
 For example, in SGLang, you can enable 128K context support with the following command:
-`llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768`

 For conversations where the input significantly exceeds this limit, follow the recommendations from the [Qwen3 model card](https://huggingface.co/Qwen/Qwen3-235B-A22B#processing-long-texts) on processing long texts.
 For example, in SGLang, you can enable 128K context support with the following command:
+`llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768`
+## 📖 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"
+   }
+```