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

Libraries

How to use LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF",
	filename="Qwen3.6-27B-RYS-XL.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 LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF
# Run inference directly in the terminal:
llama-cli -hf LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF
# Run inference directly in the terminal:
llama-cli -hf LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF

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 LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF
# Run inference directly in the terminal:
./llama-cli -hf LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF

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 LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF

Use Docker

docker model run hf.co/LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF

LM Studio
Jan
Ollama
How to use LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF with Ollama:
```
ollama run hf.co/LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF
```

Unsloth Studio

How to use LogicBombaklot/Qwen3.6-27B-RYS-XL-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 LogicBombaklot/Qwen3.6-27B-RYS-XL-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 LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF

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": "LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use LogicBombaklot/Qwen3.6-27B-RYS-XL-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 LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF

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 LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF with Docker Model Runner:
```
docker model run hf.co/LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF
```

Lemonade

How to use LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LogicBombaklot/Qwen3.6-27B-RYS-XL-GGUF

Run and chat with the model

lemonade run user.Qwen3.6-27B-RYS-XL-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Qwen 3.6 27B RYS XL (GGUF)

This is a modified version of the Qwen 3.6 27B model, utilizing the RYS (Repeat Your Self) technique.

What is RYS?

The RYS (Repeat Your Self) technique, discovered by David Ng, enhances the reasoning capabilities of Large Language Models by duplicating specific "reasoning" layers in the middle of the transformer stack. This increases the depth of the model's computation for semantic and logic-heavy tasks without requiring additional training.

Model Details

Architecture: Qwen 3.6 27B
RYS Configuration: Physical duplication of layers (26, 34).
Variant: XL (8 additional layers, bringing the total depth to 72 layers).
Format: GGUF (Quantized to Q8_0).
Tokenizer: Full Qwen 3.5/3.6 201-language support.

Performance

By repeating layers 26 through 34, the model spends more time processing the internal semantic representation of a prompt. This is particularly effective for:

Mathematical reasoning
Complex logic puzzles
Large-scale coding tasks

Usage

This GGUF model is compatible with any tool that uses llama.cpp, such as:

Prompt Format

This model uses the standard Qwen Chat template:

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant


CREDITS
Base Model: The Qwen Team at Alibaba Cloud.
RYS Technique: David Ng (dnhkng).
Quantization: Processed on a Mac Studio

Downloads last month: 51

GGUF

Model size

30B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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