Instructions to use t-tech/T-pro-it-2.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use t-tech/T-pro-it-2.0-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="t-tech/T-pro-it-2.0-GGUF", filename="T-pro-it-2.0-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use t-tech/T-pro-it-2.0-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf t-tech/T-pro-it-2.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf t-tech/T-pro-it-2.0-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf t-tech/T-pro-it-2.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf t-tech/T-pro-it-2.0-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf t-tech/T-pro-it-2.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf t-tech/T-pro-it-2.0-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf t-tech/T-pro-it-2.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf t-tech/T-pro-it-2.0-GGUF:Q4_K_M
Use Docker
docker model run hf.co/t-tech/T-pro-it-2.0-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use t-tech/T-pro-it-2.0-GGUF with Ollama:
ollama run hf.co/t-tech/T-pro-it-2.0-GGUF:Q4_K_M
- Unsloth Studio
How to use t-tech/T-pro-it-2.0-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for t-tech/T-pro-it-2.0-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for t-tech/T-pro-it-2.0-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for t-tech/T-pro-it-2.0-GGUF to start chatting
- Pi
How to use t-tech/T-pro-it-2.0-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf t-tech/T-pro-it-2.0-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "t-tech/T-pro-it-2.0-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use t-tech/T-pro-it-2.0-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf t-tech/T-pro-it-2.0-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default t-tech/T-pro-it-2.0-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use t-tech/T-pro-it-2.0-GGUF with Docker Model Runner:
docker model run hf.co/t-tech/T-pro-it-2.0-GGUF:Q4_K_M
- Lemonade
How to use t-tech/T-pro-it-2.0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull t-tech/T-pro-it-2.0-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.T-pro-it-2.0-GGUF-Q4_K_M
List all available models
lemonade list
T-pro-it-2.0-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-2.0 converted to the GGUF format with
llama.cpp.
See the original BF16 model here: t-tech/T-pro-it-2.0.
📊 Benchmarks
TBD
Available quantisations
Recommendation: choose the highest-quality quantisation that fits your hardware (VRAM / RAM).
Filename (→ -gguf) |
Quant method | Bits | Size (GB) |
|---|---|---|---|
t-pro-it-2.0-q4_k_m |
Q4_K_M | 4 | 19.8 |
t-pro-it-2.0-q5_k_s |
Q5_K_S | 5 | 22.6 |
t-pro-it-2.0-q5_0 |
Q5_0 | 5 | 22.6 |
t-pro-it-2.0-q5_k_m |
Q5_K_M | 5 | 23.2 |
t-pro-it-2.0-q6_k |
Q6_K | 6 | 26.9 |
t-pro-it-2.0-q8_0 |
Q8_0 | 8 | 34.8 |
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 for more usage guide.
We advise you to clone 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.
./llama-cli -hf t-tech/T-pro-it-2.0-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 for more usage guide.
You can run T-pro-2.0 with one command:
ollama run t-tech/T-pro-it-2.0:q8_0
See also t-tech ollama homepage.
Switching Between Thinking and Non-Thinking Mode
You can add /think and /no_think to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
📖 Citation
If you use this model in your research or projects, please cite:
@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"
}
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