Instructions to use DJLougen/Ornstein-27B-SABER-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 DJLougen/Ornstein-27B-SABER-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 DJLougen/Ornstein-27B-SABER-RYS-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DJLougen/Ornstein-27B-SABER-RYS-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 DJLougen/Ornstein-27B-SABER-RYS-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DJLougen/Ornstein-27B-SABER-RYS-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 DJLougen/Ornstein-27B-SABER-RYS-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DJLougen/Ornstein-27B-SABER-RYS-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 DJLougen/Ornstein-27B-SABER-RYS-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DJLougen/Ornstein-27B-SABER-RYS-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DJLougen/Ornstein-27B-SABER-RYS-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DJLougen/Ornstein-27B-SABER-RYS-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DJLougen/Ornstein-27B-SABER-RYS-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": "DJLougen/Ornstein-27B-SABER-RYS-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DJLougen/Ornstein-27B-SABER-RYS-GGUF:Q4_K_M

Ollama
How to use DJLougen/Ornstein-27B-SABER-RYS-GGUF with Ollama:
```
ollama run hf.co/DJLougen/Ornstein-27B-SABER-RYS-GGUF:Q4_K_M
```
Unsloth Desktop

How to use DJLougen/Ornstein-27B-SABER-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 DJLougen/Ornstein-27B-SABER-RYS-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "DJLougen/Ornstein-27B-SABER-RYS-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DJLougen/Ornstein-27B-SABER-RYS-GGUF with Docker Model Runner:
```
docker model run hf.co/DJLougen/Ornstein-27B-SABER-RYS-GGUF:Q4_K_M
```

Lemonade

How to use DJLougen/Ornstein-27B-SABER-RYS-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DJLougen/Ornstein-27B-SABER-RYS-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornstein-27B-SABER-RYS-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use DJLougen/Ornstein-27B-SABER-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 DJLougen/Ornstein-27B-SABER-RYS-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 DJLougen/Ornstein-27B-SABER-RYS-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DJLougen/Ornstein-27B-SABER-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 DJLougen/Ornstein-27B-SABER-RYS-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 "DJLougen/Ornstein-27B-SABER-RYS-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"

DJLougen commited on Apr 15

Commit

4c57d17

verified ·

1 Parent(s): 7a4b0c1

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@@ -214,9 +214,21 @@ This model is released for research purposes. It demonstrates that safety refusa
 ---
 ## References
-- [LLM Neuroanatomy: How I Topped the Leaderboard Without Changing a Single Weight](https://dnhkng.github.io/posts/rys/) — David Noel Ng, RYS Part I
-- [LLM Neuroanatomy II: Modern LLM Hacking and Hints of a Universal Language](https://dnhkng.github.io/posts/rys-ii/) — David Noel Ng, RYS Part II (Qwen3.5-27B sweep)
-- [alainnothere/llm-circuit-finder](https://github.com/alainnothere/llm-circuit-finder) — GGUF surgery tools
 - [XpressAI/Qwen3.5-27B-RYS-UD-Q4_K_XL-GGUF](https://huggingface.co/XpressAI/Qwen3.5-27B-RYS-UD-Q4_K_XL-GGUF) — Reference RYS model with BFCLv4 benchmarks

 ---
+## Acknowledgments
+The **RYS (Repeat Your Self)** layer-duplication method was discovered and developed by **David Noel Ng** ([@dnhkng](https://github.com/dnhkng)). The Pareto-optimal configurations for Qwen3.5-27B, the Math/EQ probes, the XGBoost surrogate pipeline, and the beam search methodology are all from his work. The GGUF surgery tools used to create these models are from [alainnothere/llm-circuit-finder](https://github.com/alainnothere/llm-circuit-finder), an open-source (MIT) implementation of the RYS technique for llama.cpp.
+**If you use these models, please cite David Noel Ng's work:**
+> Ng, David Noel. "LLM Neuroanatomy: How I Topped the Leaderboard Without Changing a Single Weight." [dnhkng.github.io/posts/rys](https://dnhkng.github.io/posts/rys/)
+>
+> Ng, David Noel. "LLM Neuroanatomy II: Modern LLM Hacking and Hints of a Universal Language." [dnhkng.github.io/posts/rys-ii](https://dnhkng.github.io/posts/rys-ii/)
+The **SABER** refusal-ablation method is original to this model.
 ## References
+- [LLM Neuroanatomy — Part I](https://dnhkng.github.io/posts/rys/) — David Noel Ng
+- [LLM Neuroanatomy — Part II](https://dnhkng.github.io/posts/rys-ii/) — David Noel Ng (Qwen3.5-27B sweep)
+- [alainnothere/llm-circuit-finder](https://github.com/alainnothere/llm-circuit-finder) — GGUF surgery tools (MIT)
 - [XpressAI/Qwen3.5-27B-RYS-UD-Q4_K_XL-GGUF](https://huggingface.co/XpressAI/Qwen3.5-27B-RYS-UD-Q4_K_XL-GGUF) — Reference RYS model with BFCLv4 benchmarks