Instructions to use rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF",
	filename="Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-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 rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-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 rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Use Docker

docker model run hf.co/rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-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": "rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Ollama
How to use rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF with Ollama:
```
ollama run hf.co/rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M
```

Unsloth Studio

How to use rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-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 rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-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 rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF to start chatting

How to use rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-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": "rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-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 rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF with Docker Model Runner:
```
docker model run hf.co/rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M
```

Lemonade

How to use rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF / README.md

rico03

Update README.md

d893bb8 verified about 2 months ago

5.43 kB

language:
  - en
  - multilingual
license: apache-2.0
base_model: Qwen/Qwen3.6-27B
tags:
  - qwen3.6
  - reasoning
  - distillation
  - claude-opus
  - gguf
  - llama-cpp
  - ollama
  - fine-tuned
pipeline_tag: text-generation

Qwen3.6-27B — Claude Opus Reasoning Distilled · GGUF

GGUF quantized versions of rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled for use with llama.cpp, Ollama, LM Studio, and any GGUF-compatible runtime.

🙏 This model was trained following the methodology by Jackrong, adapted for Qwen3.6-27B.

🎯 What Is This?

Qwen3.6-27B fine-tuned on ~14k Claude 4.6 Opus reasoning traces. The model adopts a structured, efficient thinking style — concise on simple tasks, deep on hard ones — while fully preserving the base model's exceptional coding and math capabilities.

Key improvement over base Qwen3.6-27B: reduced verbose reasoning loops, replaced with Claude-style structured step-by-step decomposition.

📦 Available Quantizations

Choose based on your available VRAM/RAM:

File	Size	Min VRAM	Quality	Recommended For
`Q2_K`	~10GB	12GB	⭐⭐	Very limited hardware
`Q3_K_M`	~13GB	16GB	⭐⭐⭐	Budget setups
`Q4_K_S`	~16GB	20GB	⭐⭐⭐⭐	Good balance
`Q4_K_M`	16.5GB	20GB	⭐⭐⭐⭐ ✅ Best choice	Most users
`Q5_K_S`	~19GB	24GB	⭐⭐⭐⭐⭐	High quality
`Q5_K_M`	~20GB	24GB	⭐⭐⭐⭐⭐	High quality
`Q6_K`	~23GB	28GB	⭐⭐⭐⭐⭐	Near-lossless
`Q8_0`	28.6GB	36GB	⭐⭐⭐⭐⭐	Maximum quality

Q4_K_M is recommended for most users — best quality-to-size ratio, runs on a 24GB GPU with headroom.

🚀 Quick Start

llama.cpp

# Download
huggingface-cli download rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF \
  --include "*Q4_K_M*" --local-dir ./model

# Run CLI
./llama-cli \
  -m ./model/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-Q4_K_M.gguf \
  --temp 0.6 \
  --top-p 0.95 \
  --top-k 20 \
  --presence-penalty 1.5 \
  --ctx-size 8192 \
  -p "Implement a red-black tree in Python with insert and delete."

# Run as server (OpenAI-compatible API)
./llama-server \
  -m ./model/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-Q4_K_M.gguf \
  --temp 0.6 \
  --top-p 0.95 \
  --top-k 20 \
  --ctx-size 8192 \
  --port 8080

Ollama

# Create Modelfile
cat > Modelfile << 'EOF'
FROM rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF:Q4_K_M
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER top_k 20
PARAMETER num_ctx 8192
EOF

ollama create qwen36-opus -f Modelfile
ollama run qwen36-opus

LM Studio

Search for rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled-GGUF in the model browser and download your preferred quantization.

OpenAI-compatible API (llama-server)

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8080/v1", api_key="none")

response = client.chat.completions.create(
    model="qwen3.6-27b-opus",
    messages=[{"role": "user", "content": "Write a merge sort implementation in Python."}],
    max_tokens=4096,
    temperature=0.6,
    top_p=0.95,
)
print(response.choices[0].message.content)

⚙️ Recommended Sampling Parameters

Mode	temperature	top_p	top_k	presence_penalty
Thinking (general)	1.0	0.95	20	0.0
Thinking (coding)	0.6	0.95	20	0.0
Non-thinking	0.7	0.80	20	1.5

🧠 Example Output Style

The model always reasons before answering:

<think>
Let me analyze this request carefully:

1. Identify the core objective...
2. Break the task into subcomponents...
3. Evaluate constraints and edge cases...
4. Formulate a step-by-step solution...
</think>

[Final Answer]

📊 Base Model Performance

Benchmark	Qwen3.6-27B	Claude 4.5 Opus	Qwen3.5-397B
SWE-bench Verified	77.2	80.9	76.2
SWE-bench Pro	53.5	57.1	50.9
Terminal-Bench 2.0	59.3	59.3	52.5
AIME 2026	94.1	95.1	93.3
GPQA Diamond	87.8	87.0	88.4
MMLU-Pro	86.2	89.5	87.8

Source: Qwen3.6-27B official release

📖 Citation

@misc{rico03-qwen36-opus-reasoning,
  title  = {Qwen3.6-27B Claude Opus Reasoning Distilled},
  author = {rico03},
  year   = {2026},
  url    = {https://huggingface.co/rico03/Qwen3.6-27B-Claude-Opus-Reasoning-Distilled}
}

🙏 Acknowledgements

Jackrong — pipeline guide
Unsloth — GGUF export tooling
Qwen Team — Apache 2.0 base model

Released for research and personal use.