Instructions to use cortexso/qwen3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cortexso/qwen3 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cortexso/qwen3",
	filename="qwen3-0.6b-q2_k.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use cortexso/qwen3 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/qwen3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/qwen3:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cortexso/qwen3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cortexso/qwen3: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 cortexso/qwen3:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cortexso/qwen3: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 cortexso/qwen3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cortexso/qwen3:Q4_K_M

Use Docker

docker model run hf.co/cortexso/qwen3:Q4_K_M

LM Studio
Jan

vLLM

How to use cortexso/qwen3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cortexso/qwen3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cortexso/qwen3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cortexso/qwen3:Q4_K_M

Ollama
How to use cortexso/qwen3 with Ollama:
```
ollama run hf.co/cortexso/qwen3:Q4_K_M
```

Unsloth Studio new

How to use cortexso/qwen3 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 cortexso/qwen3 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 cortexso/qwen3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cortexso/qwen3 to start chatting

Pi new

How to use cortexso/qwen3 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf cortexso/qwen3: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": "qwen3"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use cortexso/qwen3 with Docker Model Runner:
```
docker model run hf.co/cortexso/qwen3:Q4_K_M
```

Lemonade

How to use cortexso/qwen3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cortexso/qwen3:Q4_K_M

Run and chat with the model

lemonade run user.qwen3-Q4_K_M

List all available models

lemonade list

Overview

Qwen Team developed and released the Qwen3 series, a state-of-the-art family of language models optimized for advanced reasoning, dialogue, instruction-following, and agentic use cases. Qwen3 introduces innovative thinking/non-thinking mode switching, long context capabilities, and multilingual support, all while achieving high efficiency and performance.

The Qwen3 models span several sizes and include support for seamless reasoning, complex tool usage, and detailed multi-turn conversations, making them ideal for applications such as research assistants, code generation, enterprise chatbots, and more.

Variants

Qwen3

No	Variant	Branch	Cortex CLI command
1	Qwen3-0.6B	0.6b	`cortex run qwen3:0.6b`
2	Qwen3-1.7B	1.7b	`cortex run qwen3:1.7b`
3	Qwen3-4B	4b	`cortex run qwen3:4b`
4	Qwen3-8B	8b	`cortex run qwen3:8b`
5	Qwen3-14B	14b	`cortex run qwen3:14b`
6	Qwen3-32B	32b	`cortex run qwen3:32b`
7	Qwen3-30B-A3B	30b-a3b	`cortex run qwen3:30b-a3b`

Each branch contains multiple quantized GGUF versions:

Qwen3-0.6B: q2_k, q3_k_l, q3_k_m, q3_k_s, q4_k_m, q4_k_s, q5_k_m, q5_k_s, q6_k, q8_0
Qwen3-1.7B: q2_k, q3_k_l, q3_k_m, q3_k_s, q4_k_m, q4_k_s, q5_k_m, q5_k_s, q6_k, q8_0
Qwen3-4B: q2_k, q3_k_l, q3_k_m, q3_k_s, q4_k_m, q4_k_s, q5_k_m, q5_k_s, q6_k, q8_0
Qwen3-8B: q2_k, q3_k_l, q3_k_m, q3_k_s, q4_k_m, q4_k_s, q5_k_m, q5_k_s, q6_k, q8_0
Qwen3-32B: q2_k, q3_k_l, q3_k_m, q3_k_s, q4_k_m, q4_k_s, q5_k_m, q5_k_s, q6_k, q8_0
Qwen3-30B-A3B: *q2_k, q3_k_l, q3_k_m, q3_k_s, q4_k_m, q4_k_s, q5_k_m, q5_k_s, q6_k, q8_0

Use it with Jan (UI)

Install Jan using Quickstart
Use in Jan model Hub:
```
cortexso/qwen3
```

Use it with Cortex (CLI)

Install Cortex using Quickstart
Run the model with command:
```
cortex run qwen3
```

Credits

Author: Qwen Team
Converter: Menlo Research
Original License: License
Blogs: Qwen3: Think Deeper, Act Faster

Downloads last month: 1,921

GGUF

Model size

0.8B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit