Instructions to use t-tech/T-lite-it-2.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use t-tech/T-lite-it-2.1-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="t-tech/T-lite-it-2.1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("t-tech/T-lite-it-2.1-GGUF", dtype="auto")

llama-cpp-python

How to use t-tech/T-lite-it-2.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="t-tech/T-lite-it-2.1-GGUF",
	filename="T-lite-it-2.1-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use t-tech/T-lite-it-2.1-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-lite-it-2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf t-tech/T-lite-it-2.1-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-lite-it-2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf t-tech/T-lite-it-2.1-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-lite-it-2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf t-tech/T-lite-it-2.1-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-lite-it-2.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf t-tech/T-lite-it-2.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/t-tech/T-lite-it-2.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use t-tech/T-lite-it-2.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "t-tech/T-lite-it-2.1-GGUF"
# 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-lite-it-2.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/t-tech/T-lite-it-2.1-GGUF:Q4_K_M

SGLang

How to use t-tech/T-lite-it-2.1-GGUF 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-lite-it-2.1-GGUF" \
    --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-lite-it-2.1-GGUF",
		"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-lite-it-2.1-GGUF" \
        --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-lite-it-2.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use t-tech/T-lite-it-2.1-GGUF with Ollama:
```
ollama run hf.co/t-tech/T-lite-it-2.1-GGUF:Q4_K_M
```

Unsloth Studio

How to use t-tech/T-lite-it-2.1-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-lite-it-2.1-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-lite-it-2.1-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-lite-it-2.1-GGUF to start chatting

How to use t-tech/T-lite-it-2.1-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-lite-it-2.1-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-lite-it-2.1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use t-tech/T-lite-it-2.1-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-lite-it-2.1-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-lite-it-2.1-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use t-tech/T-lite-it-2.1-GGUF with Docker Model Runner:
```
docker model run hf.co/t-tech/T-lite-it-2.1-GGUF:Q4_K_M
```

Lemonade

How to use t-tech/T-lite-it-2.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull t-tech/T-lite-it-2.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.T-lite-it-2.1-GGUF-Q4_K_M

List all available models

lemonade list

T-lite-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-lite-it-2.1 converted to the GGUF format with llama.cpp.
See the original BF16 model here: t-tech/T-lite-it-2.1.

Description

T-lite-it-2.1 is an efficient Russian model built upon the Qwen 3 architecture, featuring significant improvements in instruction following and adds support for tool-calling capabilities — a key advancement over T-lite-it-1.0, which lacks tool-use support. Outperforms Qwen3-8B in tool calling scenarios, which is essential for agentic applications. Built for both general tasks and complex workflows, with higher Russian text generation throughput enabled by optimized tokenizer.

NOTE: This model supports only non-thinking mode and does not generate <think></think> in its output. Meanwhile, specifying enable_thinking=False is no longer required.

📊 Benchmarks

Model	Ru Arena Hard	ruIFeval*	ruBFCL
T-lite-it-2.1	83.9	75.9	56.5
T-lite-it-2.1-q8_0	79.5	76.2	56.6
T-lite-it-2.1-q6_k	79.5	77.8	56.7
T-lite-it-2.1-q5_k_m	78.6	76.3	56.6
T-lite-it-2.1-q5_0	78.9	76.8	56.3
T-lite-it-2.1-q5_k_s	76.1	75.3	56.0
T-lite-it-2.1-q4_k_m	71.7	75.9	54.7

* IFeval metric is mean of 4 values: prompt and instruct levels for strict and loose accuracy.

Available quantisations

Recommendation: choose the highest-quality quantisation that fits your hardware (VRAM / RAM).

Filename (→ `-gguf`)	Quant method	Bits	Size (GB)
`T-lite-it-2.1-q8_0`	Q8_0	8	8.7
`T-lite-it-2.1-q6_k`	Q6_K	6	6.7
`T-lite-it-2.1-q5_k_m`	Q5_K_M	5	5.9
`T-lite-it-2.1-q5_k_s`	Q5_K_S	5	5.7
`T-lite-it-2.1-q5_0`	Q5_0	5	5.7
`T-lite-it-2.1-q4_k_m`	Q4_K_M	4	5.0

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-lite-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