Instructions to use neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-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 neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-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 neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-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 neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-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 neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-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 neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf:Q4_K_M

Use Docker

docker model run hf.co/neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-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": "neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf:Q4_K_M

Ollama
How to use neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf with Ollama:
```
ollama run hf.co/neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf:Q4_K_M
```
Unsloth Desktop

How to use neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-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": "neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf with Docker Model Runner:
```
docker model run hf.co/neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf:Q4_K_M
```

Lemonade

How to use neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-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 neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-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 neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-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 "neopolita/Qwen3.6-19B-A3B-Niwaki-v2-2bit-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"

Qwen3.6-19B-A3B-Niwaki-v2-2bit-GGUF

GGUF builds of Qwen3.6-19B-A3B-Niwaki-v2-2bit-mlx — Qwen3.6-35B-A3B pruned to 19B total / ~3.3B active parameters — for llama.cpp and everything built on it. No custom code: unlike the MLX repo, these files run on stock llama.cpp.

Niwaki (庭木) are Japan's garden trees, sculpted by meticulous pruning so that every branch serves the form of the whole. This model applies that spirit to a Mixture-of-Experts. A paper with the full method is coming soon.

Files

file	size	wt2 ppl (llama.cpp, 512-ctx)
Qwen3.6-19B-A3B-Niwaki-v2-2bit-UD-Q3K.gguf (recommended)	12.2 GB	11.44 ±0.08
Qwen3.6-19B-A3B-Niwaki-v2-2bit-Q4_K_M.gguf	14.8 GB	11.49 ±0.08

First-generation GGUF builds under the identical protocol:

model (UD-Q3K builds)	size	wt2 ppl
Qwen3.6-27B-A3B-Niwaki-2bit	13.5 GB	10.41 ±0.07
Qwen3.6-19B-A3B-Niwaki-2bit	9.0 GB	13.15 ±0.10
Qwen3.6-11B-A3B-Niwaki-4bit	5.9 GB	17.24 ±0.13

This build beats the same-size first-generation 19B by 13% (11.44 vs 13.15) at 3.2 GB more on disk — the format pad described below.

Reference Qwen3.6-35B-A3B at Q8_0 measures 6.95 under the identical protocol (llama-perplexity, WikiText-2 test, 512-token windows). These llama.cpp numbers are not directly comparable to the MLX repo's 2048-window benchmarks; the relative standings match across both.

Generation battery (measured on the canonical MLX weights; reference scores 0.63 / 0.51 under the identical battery): bigram-diversity avg/min = 0.84 / 0.54 across an 8-prompt code/reasoning/chat/creative battery.

The recommended UD-Q3K build is quantized structure-aware (importance matrices calibrated on a mixed corpus), mirroring the artifact's native allocation: the always-active backbone (attention, shared experts, embeddings) is kept at high precision (Q6_K) while the routed experts ride a compact carrier (q3_k, imatrix-guided). It matches or beats uniform Q4_K_M quality at ~18% fewer bytes on this model family.

Format note: GGUF requires a uniform expert count per model, so the shared-only layers carry zero-valued expert tensors stored at ~~1.6 bits/weight (~~3.1 GB of the file). They contribute nothing to outputs; this is why these files are larger than the MLX repo at equal quality.

Model dimensions


total / active parameters	~19B / ~3.3B
layers / routed experts / top-k	40 / 256 / 8 (layers 10–29 are shared-expert-only)
expert intermediate size	512 (unchanged)
context	as base model
conversion note	speculative-decoding (MTP) draft block not included

Usage

llama-cli -m Qwen3.6-19B-A3B-Niwaki-v2-2bit-UD-Q3K.gguf -p "your prompt" -n 256
# or serve:
llama-server -m Qwen3.6-19B-A3B-Niwaki-v2-2bit-UD-Q3K.gguf

Requires a recent llama.cpp with Qwen3.6 (hybrid linear-attention) support. Canonical benchmarks and the MLX-native artifact: Qwen3.6-19B-A3B-Niwaki-v2-2bit-mlx. Family: v2-4bit · first-generation 27B-2bit · 19B-2bit · 11B-4bit.

Base model by the Qwen team (Apache 2.0); pruning, distillation, and GGUF builds by the Niwaki project, 2026-08.