Instructions to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF",
	filename="Ornstein3.6-35B-A3B-RYS-SABER-Q3_K_M.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 DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-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 DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-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/Ornstein3.6-35B-A3B-RYS-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-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/Ornstein3.6-35B-A3B-RYS-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-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/Ornstein3.6-35B-A3B-RYS-SABER-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF:Q4_K_M

Ollama
How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF with Ollama:
```
ollama run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF:Q4_K_M
```

Unsloth Studio

How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-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 DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-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 DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF to start chatting

How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-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": "DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-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 DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-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/Ornstein3.6-35B-A3B-RYS-SABER-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF with Docker Model Runner:
```
docker model run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF:Q4_K_M
```

Lemonade

How to use DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornstein3.6-35B-A3B-RYS-SABER-GGUF-Q4_K_M

List all available models

lemonade list

Ornstein3.6-35B-A3B-RYS-SABER-GGUF / README.md

DJLougen

Upload README.md with huggingface_hub

7762e32 verified about 2 months ago

2.79 kB

base_model: DJLougen/Ornstein3.6-35B-A3B-RYS-SABER
tags:
  - gguf
  - qwen3_5_moe
  - mixture-of-experts
  - text-generation
  - qwen3.6
  - rys
  - saber
  - refusal-ablation
  - uncensored
  - quantized
language:
  - en
license: apache-2.0
pipeline_tag: text-generation

Ornstein3.6-35B-A3B-RYS-SABER-GGUF

GGUF quantizations of DJLougen/Ornstein3.6-35B-A3B-RYS-SABER — the fully uncensored, RYS-enhanced Ornstein fine-tune with SABER refusal ablation applied.

Full-precision model: DJLougen/Ornstein3.6-35B-A3B-RYS-SABER | Censored version: DJLougen/Ornstein3.6-35B-A3B-RYS

Support This Work

I'm a PhD student in visual neuroscience at the University of Toronto who also happens to spend way too much time fine-tuning, merging, and quantizing open-weight models on rented H100s and a local DGX Spark. All training compute is self-funded — balancing GPU costs against a student budget. If my uploads have been useful to you, consider buying a PhD student a coffee. It goes a long way toward keeping these experiments running.

Support on Ko-fi

Available Quantizations

Quantization	Use Case
Q8_0	Best quality, highest memory
Q6_K	Near-lossless, good for 48GB+ VRAM
Q5_K_M	Excellent quality/size balance
Q5_K_S	Slightly smaller Q5
Q5_0	Legacy Q5 format
Q4_K_M	Recommended default for 24GB VRAM
Q4_K_S	Smaller Q4 variant
Q4_0	Legacy Q4 format
Q3_K_L	Low memory, acceptable quality
Q3_K_M	Lower memory
Q3_K_S	Aggressive 3-bit
Q2_K	Minimum viable quality

Model Lineage

Qwen 3.6 35B-A3B → Ornstein3.6 (DDM fine-tune) → RYS (layer 10 dup, +49%) → SABER (refusal ablated)

Model Details

Architecture: Qwen 3.6 MoE (35B total, ~3B active per token)
Layers: 41 (40 original + 1 RYS-duplicated layer 10)
Context: 262,144 tokens
SABER: 54 refusal directions ablated across layers 24-32, 100% capability preserved

Usage

llama.cpp

llama-cli -m Ornstein3.6-35B-A3B-RYS-SABER-Q4_K_M.gguf -p "Your prompt here" -ngl 99

Ollama

ollama run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-SABER-GGUF:Q4_K_M

Disclaimer

This model has had its refusal training removed. It will comply with requests that the base model would refuse. The user assumes full responsibility for how this model is used. This release is intended for research, creative, and educational purposes.

License

Apache 2.0