Instructions to use GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-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 GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-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 GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS

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 GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS

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 GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS

Use Docker

docker model run hf.co/GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-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": "GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS

Ollama
How to use GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF with Ollama:
```
ollama run hf.co/GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS
```

Unsloth Studio

How to use GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-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 GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-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 GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-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 GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF to start chatting

How to use GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-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 GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS

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": "GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-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 GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS

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 "GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS" \
  --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 GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS
```

Lemonade

How to use GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-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 GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS

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 GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat

Qwen3.6-27B IQ4_XS Pure with MTP GGUF

This GGUF combines the Ununnilium IQ4_XS pure quantization of Qwen3.6-27B with the MTP (Multi-Token Prediction) head extracted from the unsloth IQ4_XS MTP build, enabling native speculative decoding at a compact file size.

File

File	Size	Quantization
`qwen3.6-27b-IQ4_XS-pure-with-MTP-IQ4.gguf`	~13.57 GB	Body: IQ4_XS, MTP: IQ4_NL/Q5_K/Q8_0 mix

What's Inside

Body (851 tensors): IQ4_XS quantization via llama-quantize --pure from Ununnilium's build, using the unsloth imatrix for calibration
MTP head (15 tensors): Extracted from unsloth/Qwen3.6-27B-MTP-GGUF (IQ4_XS variant), preserving the original mixed quantization:
- attn_q, attn_k, attn_output, ffn_gate/up/down → IQ4_NL
- attn_v → Q5_K
- nextn.eh_proj → Q8_0
- Norm tensors → F16

Why This Exists

The Ununnilium pure GGUF strips MTP tensors to save space, but that means speculative decoding can't use the trained native draft head. This file grafts the MTP head back in, restoring native MTP speculative decoding while keeping the aggressive IQ4_XS body quantization for VRAM efficiency.

Provenance

Base model: Qwen/Qwen3.6-27B
Body quantization: Ununnilium/Qwen3.6-27B-IQ4_XS-pure-GGUF
MTP head source: unsloth/Qwen3.6-27B-MTP-GGUF (IQ4_XS variant)

Quickstart

llama.cpp

llama-server -hf GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF:IQ4_XS \
  --spec-type draft-mtp \
  --spec-draft-n-max 3 \
  --spec-draft-p-min 0.75

LM Studio

Search for GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF and load the file. Enable speculative decoding in settings with draft-mtp type.

llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF",
    filename="qwen3.6-27b-IQ4_XS-pure-with-MTP-IQ4.gguf",
    n_gpu_layers=-1,
    n_ctx=131072,
    speculative="draft-mtp",
    speculative_n_draft=3,
)

Recommended Server Flags

llama-server \
  --model qwen3.6-27b-IQ4_XS-pure-with-MTP-IQ4.gguf \
  --ctx-size 131072 \
  --n-gpu-layers 99 \
  --parallel 1 \
  --batch-size 2048 \
  --ubatch-size 128 \
  --cache-type-k q4_0 \
  --cache-type-v q4_0 \
  --threads 6 \
  --threads-batch 12 \
  --flash-attn on \
  --no-mmap \
  --spec-type draft-mtp \
  --spec-draft-n-max 3 \
  --spec-draft-p-min 0.75 \
  --temp 0.8 \
  --top-p 0.95 \
  --top-k 20 \
  --min-p 0.0

MTP Performance

Tested on AMD Radeon RX 7800 XT (16 GB VRAM) with Vulkan backend:

Metric	Value
Cumulative token acceptance	~82%
Draft acceptance rate	~92%
Generation speed	~42-65 t/s (varies by workload)
Prompt processing	~100-120 t/s

Acceptance rates above 60% mean MTP provides a net speedup. The ~82% cumulative token acceptance observed in testing indicates strong speculative decoding performance.

Model Architecture

Parameter	Value
Architecture	qwen35
Parameters	27B
Layers	64 + 1 MTP
Hidden size	5120
FFN size	17408
Attention heads	24 Q / 4 KV
Context length	262,144 (native)
Vocab size	248,320

Caveats

MTP support requires llama.cpp build with draft-mtp speculative decoding support (PR #22673 or newer)
The MTP head was trained against the original Qwen3.6-27B trunk; acceptance may vary if the body has been further fine-tuned
Vision/MTP combination has been fragile in llama.cpp testing; use text-only first
--parallel 1 is required when using MTP (parallel slots not yet supported)

Downloads last month: 1,382

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for GianniDPC/Qwen3.6-27B-IQ4_XS-pure-with-MTP-GGUF

Base model

Qwen/Qwen3.6-27B

Quantized

(682)

this model