Instructions to use EntityDeletr/Qwen3.5-4B-MTP-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 EntityDeletr/Qwen3.5-4B-MTP-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 EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16

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 EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16

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 EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16

Use Docker

docker model run hf.co/EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16

LM Studio
Jan
Ollama
How to use EntityDeletr/Qwen3.5-4B-MTP-GGUF with Ollama:
```
ollama run hf.co/EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16
```

Unsloth Studio

How to use EntityDeletr/Qwen3.5-4B-MTP-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 EntityDeletr/Qwen3.5-4B-MTP-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 EntityDeletr/Qwen3.5-4B-MTP-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use EntityDeletr/Qwen3.5-4B-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16

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": "EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use EntityDeletr/Qwen3.5-4B-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16

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 "EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16" \
  --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 EntityDeletr/Qwen3.5-4B-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16
```

Lemonade

How to use EntityDeletr/Qwen3.5-4B-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3.5-4B-MTP-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use EntityDeletr/Qwen3.5-4B-MTP-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 EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16

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 EntityDeletr/Qwen3.5-4B-MTP-GGUF:F16

Run Hermes

hermes

Atomic Chat

Usage

For now, you need to use the gg/spec-mtp-experiments branch on llama.cpp or a custom mtp fork.

You can switch to the mtp branch with git checkout gg/spec-mtp-experiments after cloning and entering the llama.cpp repository.

Add --spec-type mtp --spec-draft-n-max 5 --spec-draft-n-min 0 to your llama-server or llama-cli command.

Feel free to tweak --spec-draft-n-max and find out what works best for your setup.

Try not to push --spec-draft-n-min too far, keep it in single digits.

I found that (in my testing), token speed was as such when tweaking --spec-draft-n-min:

Setting for `--spec-draft-n-min`	generation t/s
0	47.7
1	47.2
2	47.8
3	44.2
4	44.9
5	36.2

with the launch command being

/llama.cpp/build/bin/llama-cli -st -p 'What is the antiderivative of x^3?' --verbose-prompt --prio 3 --batch-size 1024 --ubatch-size 1024 --mmap --perf --flash-attn on --fit-ctx 16384 -ctk q8_0 -ctv q8_0 -m /ai/models/Qwen3.5-4B-Q5_K_M-mtp.gguf -ngl all --spec-type mtp --spec-draft-n-max 5 --spec-draft-n-min "$i" -fitt 2048

Credits

Qwen for this amazing model.
Unsloth for the imatrix quantization file.
All of the llama.cpp and ggml contributors for allowing me to run AI models locally.

Downloads last month: 48

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

5-bit

16-bit

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