Instructions to use DuoNeural/Qwen3-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use DuoNeural/Qwen3-4B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="DuoNeural/Qwen3-4B-GGUF", filename="Qwen3-4B-IQ1_S.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 DuoNeural/Qwen3-4B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf DuoNeural/Qwen3-4B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf DuoNeural/Qwen3-4B-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 DuoNeural/Qwen3-4B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf DuoNeural/Qwen3-4B-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 DuoNeural/Qwen3-4B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf DuoNeural/Qwen3-4B-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 DuoNeural/Qwen3-4B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf DuoNeural/Qwen3-4B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/DuoNeural/Qwen3-4B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use DuoNeural/Qwen3-4B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DuoNeural/Qwen3-4B-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": "DuoNeural/Qwen3-4B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DuoNeural/Qwen3-4B-GGUF:Q4_K_M
- Ollama
How to use DuoNeural/Qwen3-4B-GGUF with Ollama:
ollama run hf.co/DuoNeural/Qwen3-4B-GGUF:Q4_K_M
- Unsloth Studio new
How to use DuoNeural/Qwen3-4B-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 DuoNeural/Qwen3-4B-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 DuoNeural/Qwen3-4B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DuoNeural/Qwen3-4B-GGUF to start chatting
- Pi new
How to use DuoNeural/Qwen3-4B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf DuoNeural/Qwen3-4B-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": "DuoNeural/Qwen3-4B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use DuoNeural/Qwen3-4B-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 DuoNeural/Qwen3-4B-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 DuoNeural/Qwen3-4B-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use DuoNeural/Qwen3-4B-GGUF with Docker Model Runner:
docker model run hf.co/DuoNeural/Qwen3-4B-GGUF:Q4_K_M
- Lemonade
How to use DuoNeural/Qwen3-4B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DuoNeural/Qwen3-4B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-4B-GGUF-Q4_K_M
List all available models
lemonade list
Qwen3-4B โ GGUF Quants
Quantized GGUF versions of Qwen/Qwen3-4B โ Alibaba's Qwen3 base model at the 4B parameter scale. Qwen3 represents a significant generational leap over Qwen2.5, with improved reasoning, coding, and instruction following packed into an efficient sub-5B footprint.
Available Files
| File | Quant | Size | Use Case |
|---|---|---|---|
Qwen3-4B-Q8_0.gguf |
Q8_0 | ~4.5GB | Maximum quality |
Qwen3-4B-Q6_K.gguf |
Q6_K | ~3.5GB | Near-lossless |
Qwen3-4B-Q5_K_M.gguf |
Q5_K_M | ~3.1GB | High quality |
Qwen3-4B-Q4_K_M.gguf |
Q4_K_M | ~2.6GB | Recommended default |
Qwen3-4B-Q3_K_M.gguf |
Q3_K_M | ~2.1GB | Low VRAM |
Qwen3-4B-IQ4_XS.gguf |
IQ4_XS | ~2.4GB | Imatrix 4-bit |
Qwen3-4B-IQ3_XXS.gguf |
IQ3_XXS | ~1.8GB | Imatrix 3-bit |
Qwen3-4B-IQ2_M.gguf |
IQ2_M | ~1.6GB | Imatrix 2-bit |
Qwen3-4B-IQ1_S.gguf |
IQ1_S | ~1.2GB | Extreme compression |
Qwen3-4B-fp16.gguf |
FP16 | ~8.0GB | Full precision |
imatrix.dat |
โ | โ | Importance matrix |
Usage
# llama.cpp
./llama-cli -m Qwen3-4B-Q4_K_M.gguf \
--ctx-size 8192 -n 512 \
-p "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n"
# Ollama
ollama run hf.co/DuoNeural/Qwen3-4B-GGUF:Q4_K_M
About Qwen3-4B
- Parameters: 4B
- Architecture: Qwen3 decoder-only transformer
- License: Apache 2.0
- Strengths: Reasoning, coding, instruction following, multilingual
- Ideal for: Hardware with 4-6GB VRAM, edge deployment, CPU inference
At Q4_K_M (~2.6GB) this fits on virtually any modern GPU or even CPU-only systems.
Quantized by DuoNeural using llama.cpp on RTX 5090.
DuoNeural
DuoNeural is an open AI research lab โ human + AI in collaboration.
| Platform | Link |
|---|---|
| HuggingFace | huggingface.co/DuoNeural |
| Website | duoneural.com |
| GitHub | github.com/DuoNeural |
| X / Twitter | @DuoNeural |
| duoneural@proton.me | |
| Newsletter | duoneural.beehiiv.com |
| Support | buymeacoffee.com/duoneural |
DuoNeural Research Publications
Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura โ DuoNeural.
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