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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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

Hermes Agent

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

OpenClaw

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"

wmeddie commited on Apr 30

Commit

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@@ -143,6 +143,50 @@ reasoning prompts:
 The probe alone suggested rys_33-36 was a moderate win. The **sampled BFCL
 run with thinking enabled confirms it on the harder live categories** (above).
 ---
 ## Hybrid Mamba/attention architecture constraint

 The probe alone suggested rys_33-36 was a moderate win. The **sampled BFCL
 run with thinking enabled confirms it on the harder live categories** (above).
+### Extended evaluation (Ng's protocol)
+After a thoughtful question on the discussion forum about deviations from
+[David Ng's suggested reproduction path](https://github.com/dnhkng/RYS#suggested-reproduction-path),
+we went back and ran the steps we had skipped:
+**Extended probe** — `math_120 + eq_140` from Ng's repo, `--reasoning off` to
+match the protocol's intent (the math probe is designed for intuitive
+guessing, not deliberate computation):
+| Variant | math_120 | eq_140 |
+|---|---|---|
+| base | 0.9986 | 74.53 |
+| **rys_33-36** | 0.9930 | **78.81** |
+On the larger probe rys_33-36 holds its EQ improvement (+4.28 pp). Math is at
+ceiling for both. Note this is the *opposite* direction from our small
+internal probe (where rys_33-36 had lower EQ) — small-probe variance was
+misleading us; the 140-question sample is the trustworthy reading.
+**Depth-2 beam search** — 10 non-overlapping pair-combinations of the top
+single-block configs, each scored on the same probe:
+| Variant | math_120 | eq_140 |
+|---|---|---|
+| **rys_33-36** | 0.9930 | **78.81** |
+| rys_33-36 + 49-52 | 0.9226 | 75.66 |
+| rys_33-36 + 53-56 | 0.9219 | 75.27 |
+| rys_33-36 + 54-57 | 0.9639 | 72.21 |
+| rys_33-36 + 56-59 | 0.9643 | 74.21 |
+| rys_33-36 + 58-61 | 0.9930 | 68.78 |
+| rys_49-52 + 53-56 | 0.8864 | 66.70 |
+| rys_49-52 + 56-59 | 0.9654 | 69.67 |
+| rys_49-52 + 58-61 | 0.9606 | 69.18 |
+| rys_53-56 + 58-61 | 0.9635 | 63.57 |
+| rys_54-57 + 58-61 | 0.9703 | 59.93 |
+No depth-2 combination beats rys_33-36 on EQ_140. Stacking blocks degrades
+math (sometimes catastrophically) without improving EQ. So the shortcut we
+took in candidate selection (no beam search) did not cost us a better
+configuration in this neighborhood. We did **not** train Ng's surrogate
+regressor or run a deeper beam search — those would explore more of the
+configuration space and might find something better.
 ---
 ## Hybrid Mamba/attention architecture constraint