Instructions to use chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF",
	filename="Qwen3.6-27B-Claude-Mythos-Distilled.BF16-mmproj.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-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 chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-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 chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-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 chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-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 chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF:Q4_K_M

Use Docker

docker model run hf.co/chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-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": "chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF:Q4_K_M

Ollama
How to use chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF with Ollama:
```
ollama run hf.co/chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF:Q4_K_M
```

Unsloth Studio

How to use chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-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 chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-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 chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-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 chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF to start chatting

How to use chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-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 chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF:Q4_K_M

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": "chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-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 chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-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 chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-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 chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-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 "chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-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"

Docker Model Runner
How to use chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF:Q4_K_M
```

Lemonade

How to use chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.6-27B-Claude-Mythos-Distilled

🚀 Try our ecosystem:

Free AI Chat (no login): freeaichat.chatqaq.com
AI Atlas (Insights & Analysis): ai-atlas-a.chatqaq.com

Model Overview

Qwen3.6-27B-Claude-Mythos-Distilled is a high-performance community fine-tune based on the Qwen3.6-27B architecture. It has been specifically optimized using a high-signal synthetic instruction dataset of 25K samples to excel in advanced technical reasoning, complex coding tasks, cybersecurity analysis, and autonomous agentic workflows.

The model is designed to bridge the gap between general-purpose LLMs and specialized technical assistants by distilling complex reasoning patterns into a 27B parameter scale.

📖 Detailed Analysis & Deep Dive

For a comprehensive breakdown of the training process, evaluation, and use-case analysis, please visit our detailed posts:

Chinese Analysis: Read on AI Atlas (ZH)
English Analysis: Read on AI Atlas (EN)

🛠 Training Methodology

Base Model: Qwen/Qwen3.6-27B
Training Method: QLoRA (4-bit NF4) parameter-efficient fine-tuning.
Dataset: 25,000 high-quality synthetic SFT samples focused on Claude Mythos-style reasoning distributions.
Optimization Goal: Enhancing multi-step problem decomposition and technical precision.
Export Formats: Available in GGUF (Q8/Q4) for seamless local deployment.

✨ Key Capabilities

🧠 Advanced Technical Reasoning

The model exhibits strong structured reasoning behavior, allowing it to break down complex, multi-layered problems into actionable steps with a clear logical chain.

💻 Engineering-Grade Coding

Optimized for software engineering tasks, the model provides production-oriented code, excels in debugging, and strictly adheres to complex architectural specifications.

🤖 Agentic Workflow Optimization

Designed as a "brain" for AI agents, it shows superior performance in long-horizon planning and the ability to execute multi-step autonomous tasks.

🛡 Cybersecurity Analysis

Enhanced capability in defensive-oriented security analysis, including vulnerability assessment and source code auditing.

System Prompt

Training system prompt:

"You are a distilled mirror of Claude Mythos..."

Used to reinforce:

structured reasoning
technical depth
agentic behavior
security-first analysis

⚠️ Limitations

Synthetic Data Origin: Trained entirely on synthetic data; may exhibit hallucinations or over-generalize in highly niche real-world scenarios.
Validation: Not safety-certified for mission-critical or production-critical systems. Expert human validation is required for high-stakes deployments.

📜 License

This model follows the Qwen3.6-27B base model license. Please ensure compliance with the original upstream licensing terms.

Downloads last month: 10,946

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

Model tree for chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF

Base model

Qwen/Qwen3.6-27B

Quantized

(647)

this model

Finetunes

1 model

chatqaq
/

Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF

Qwen3.6-27B-Claude-Mythos-Distilled

Model Overview

📖 Detailed Analysis & Deep Dive

🛠 Training Methodology

✨ Key Capabilities

🧠 Advanced Technical Reasoning

💻 Engineering-Grade Coding

🤖 Agentic Workflow Optimization

🛡 Cybersecurity Analysis

System Prompt

⚠️ Limitations

📜 License

Model tree for chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF

Dataset used to train chatqaq/Qwen3.6-27B-Claude-Mythos-Distilled-MTP-GGUF