Instructions to use kaitchup/Qwen3.5-4B-GGUF-MoQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use kaitchup/Qwen3.5-4B-GGUF-MoQ 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 kaitchup/Qwen3.5-4B-GGUF-MoQ
# Run inference directly in the terminal:
llama cli -hf kaitchup/Qwen3.5-4B-GGUF-MoQ

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kaitchup/Qwen3.5-4B-GGUF-MoQ
# Run inference directly in the terminal:
llama cli -hf kaitchup/Qwen3.5-4B-GGUF-MoQ

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 kaitchup/Qwen3.5-4B-GGUF-MoQ
# Run inference directly in the terminal:
./llama-cli -hf kaitchup/Qwen3.5-4B-GGUF-MoQ

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 kaitchup/Qwen3.5-4B-GGUF-MoQ
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kaitchup/Qwen3.5-4B-GGUF-MoQ

Use Docker

docker model run hf.co/kaitchup/Qwen3.5-4B-GGUF-MoQ

LM Studio
Jan
Ollama
How to use kaitchup/Qwen3.5-4B-GGUF-MoQ with Ollama:
```
ollama run hf.co/kaitchup/Qwen3.5-4B-GGUF-MoQ
```

Unsloth Studio

How to use kaitchup/Qwen3.5-4B-GGUF-MoQ 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 kaitchup/Qwen3.5-4B-GGUF-MoQ 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 kaitchup/Qwen3.5-4B-GGUF-MoQ to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kaitchup/Qwen3.5-4B-GGUF-MoQ to start chatting

How to use kaitchup/Qwen3.5-4B-GGUF-MoQ with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kaitchup/Qwen3.5-4B-GGUF-MoQ

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": "kaitchup/Qwen3.5-4B-GGUF-MoQ"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kaitchup/Qwen3.5-4B-GGUF-MoQ with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kaitchup/Qwen3.5-4B-GGUF-MoQ

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 kaitchup/Qwen3.5-4B-GGUF-MoQ

Run Hermes

hermes

OpenClaw new

How to use kaitchup/Qwen3.5-4B-GGUF-MoQ with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kaitchup/Qwen3.5-4B-GGUF-MoQ

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 "kaitchup/Qwen3.5-4B-GGUF-MoQ" \
  --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"

Docker Model Runner
How to use kaitchup/Qwen3.5-4B-GGUF-MoQ with Docker Model Runner:
```
docker model run hf.co/kaitchup/Qwen3.5-4B-GGUF-MoQ
```

Lemonade

How to use kaitchup/Qwen3.5-4B-GGUF-MoQ with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kaitchup/Qwen3.5-4B-GGUF-MoQ

Run and chat with the model

lemonade run user.Qwen3.5-4B-GGUF-MoQ-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

Subscribe and Support

GGUF models made with the method ("Mixture of Quantizations") proposed by Waleed Ahmad.

They are currently the best GGUF versions of Qwen3.5-4B.

Downloads last month: 1,072

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for kaitchup/Qwen3.5-4B-GGUF-MoQ

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