Instructions to use tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP",
	filename="Qwen3.6-27B-Autoround-Q3_K_L.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP 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 tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L
# Run inference directly in the terminal:
llama cli -hf tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L
# Run inference directly in the terminal:
llama cli -hf tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L

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 tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L
# Run inference directly in the terminal:
./llama-cli -hf tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L

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 tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L

Use Docker

docker model run hf.co/tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L

LM Studio
Jan
Ollama
How to use tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP with Ollama:
```
ollama run hf.co/tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L
```

Unsloth Studio

How to use tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP 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 tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP 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 tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP to start chatting

How to use tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L

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": "tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L

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 tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L

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 "tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L" \
  --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 tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP with Docker Model Runner:
```
docker model run hf.co/tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L
```

Lemonade

How to use tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP:Q3_K_L

Run and chat with the model

lemonade run user.Qwen3.6-27B-AutoRound-GGUF-NoMTP-Q3_K_L

List all available models

lemonade list

This is a repository of GGUF files (without MTP , It is old version before updating MTP) re-uploaded from sphaela/Qwen3.6-27B-AutoRound-GGUF

Qwen3.6-27B GGUF (AutoRound Quantized)

This repository contains GGUF quantized versions of Qwen/Qwen3.6-27B created using Intel's AutoRound quantization method.

About AutoRound

AutoRound is an advanced quantization technique from Intel that aims to minimize accuracy loss through automated rounding optimization. The iterative calibration mode (--enable_alg_ext) runs gradient-based optimization for 200 iterations per block, finding optimal rounding thresholds that minimize reconstruction error.

Downloads last month: 286

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

Inference Providers NEW

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

Model tree for tooltd/Qwen3.6-27B-AutoRound-GGUF-NoMTP

Base model

Qwen/Qwen3.6-27B

Quantized

(546)

this model