Instructions to use llmware/qwen3-4b-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use llmware/qwen3-4b-instruct-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf llmware/qwen3-4b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf llmware/qwen3-4b-instruct-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf llmware/qwen3-4b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf llmware/qwen3-4b-instruct-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 llmware/qwen3-4b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf llmware/qwen3-4b-instruct-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 llmware/qwen3-4b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf llmware/qwen3-4b-instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/llmware/qwen3-4b-instruct-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use llmware/qwen3-4b-instruct-gguf with Ollama:
```
ollama run hf.co/llmware/qwen3-4b-instruct-gguf:Q4_K_M
```
Unsloth Desktop

How to use llmware/qwen3-4b-instruct-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llmware/qwen3-4b-instruct-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "llmware/qwen3-4b-instruct-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use llmware/qwen3-4b-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/llmware/qwen3-4b-instruct-gguf:Q4_K_M
```

Lemonade

How to use llmware/qwen3-4b-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull llmware/qwen3-4b-instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.qwen3-4b-instruct-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use llmware/qwen3-4b-instruct-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llmware/qwen3-4b-instruct-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 llmware/qwen3-4b-instruct-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use llmware/qwen3-4b-instruct-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llmware/qwen3-4b-instruct-gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "llmware/qwen3-4b-instruct-gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

qwen3-4b-instruct-gguf

qwen3-4b-instruct-gguf is a GGUF Q4_K_M int4 quantized version of Qwen3-4B-Instruct, providing a very fast inference implementation, optimized for AI PCs.

This is from the latest release series from Qwen, and has 'thinking' capability expressed as 'think' tokens.

This model will run on an AI PC with at least 16 GB of memory.

Model Description

Developed by: Qwen
Model type: qwen3
Parameters: 4 billion
Model Parent: Qwen/Qwen3-4B-Instruct
Language(s) (NLP): English
License: Apache 2.0
Uses: Chat, general-purpose LLM
Quantization: int4

Model Card Contact

llmware on github

llmware on hf

llmware website

Downloads last month: 908

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support