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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="XpressAI/Qwen3.6-27B-RYS-GGUF",
	filename="Qwen3.6-27B-DFlash-Q8_0-rys.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 XpressAI/Qwen3.6-27B-RYS-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 XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0

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 XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0

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 XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0

Use Docker

docker model run hf.co/XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use XpressAI/Qwen3.6-27B-RYS-GGUF with Ollama:
```
ollama run hf.co/XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0
```

Unsloth Studio

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

Using HuggingFace Spaces for Unsloth

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

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0

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": "XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use XpressAI/Qwen3.6-27B-RYS-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 XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0

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 XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use XpressAI/Qwen3.6-27B-RYS-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0

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 "XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0" \
  --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 XpressAI/Qwen3.6-27B-RYS-GGUF with Docker Model Runner:
```
docker model run hf.co/XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull XpressAI/Qwen3.6-27B-RYS-GGUF:Q8_0

Run and chat with the model

lemonade run user.Qwen3.6-27B-RYS-GGUF-Q8_0

List all available models

lemonade list

RYS for Qwen3.6 27B MTP ?

by dav3ai - opened 25 days ago

Discussion

dav3ai

25 days ago

Hi XpressAI !

Can you make the same RYS version for the model unsloth/Qwen3.6-27B-MTP-GGUF Q8 please ?

wmeddie

Xpress AI org 25 days ago

Yeah. We have this on our todo list. Coming very soon.

dottreo

18 days ago

We found that MTP doesn't actually work well with the RYS model, but DFlash does.
You can use Qwen3.6-27B-DFlash-Q8_0-rys.gguf with beellama.cpp.

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