Instructions to use GestaltLabs/Ornstein-3.6-27B-RYS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GestaltLabs/Ornstein-3.6-27B-RYS-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="GestaltLabs/Ornstein-3.6-27B-RYS-GGUF",
	filename="ornstein-3.6-27b-rys-q2_k.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use GestaltLabs/Ornstein-3.6-27B-RYS-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf GestaltLabs/Ornstein-3.6-27B-RYS-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf GestaltLabs/Ornstein-3.6-27B-RYS-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 GestaltLabs/Ornstein-3.6-27B-RYS-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf GestaltLabs/Ornstein-3.6-27B-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 GestaltLabs/Ornstein-3.6-27B-RYS-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GestaltLabs/Ornstein-3.6-27B-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 GestaltLabs/Ornstein-3.6-27B-RYS-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GestaltLabs/Ornstein-3.6-27B-RYS-GGUF:Q4_K_M

Use Docker

docker model run hf.co/GestaltLabs/Ornstein-3.6-27B-RYS-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use GestaltLabs/Ornstein-3.6-27B-RYS-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GestaltLabs/Ornstein-3.6-27B-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": "GestaltLabs/Ornstein-3.6-27B-RYS-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GestaltLabs/Ornstein-3.6-27B-RYS-GGUF:Q4_K_M

Ollama
How to use GestaltLabs/Ornstein-3.6-27B-RYS-GGUF with Ollama:
```
ollama run hf.co/GestaltLabs/Ornstein-3.6-27B-RYS-GGUF:Q4_K_M
```

Unsloth Studio new

How to use GestaltLabs/Ornstein-3.6-27B-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 GestaltLabs/Ornstein-3.6-27B-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 GestaltLabs/Ornstein-3.6-27B-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 GestaltLabs/Ornstein-3.6-27B-RYS-GGUF to start chatting

Pi new

How to use GestaltLabs/Ornstein-3.6-27B-RYS-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf GestaltLabs/Ornstein-3.6-27B-RYS-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": "GestaltLabs/Ornstein-3.6-27B-RYS-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use GestaltLabs/Ornstein-3.6-27B-RYS-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf GestaltLabs/Ornstein-3.6-27B-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 GestaltLabs/Ornstein-3.6-27B-RYS-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use GestaltLabs/Ornstein-3.6-27B-RYS-GGUF with Docker Model Runner:
```
docker model run hf.co/GestaltLabs/Ornstein-3.6-27B-RYS-GGUF:Q4_K_M
```

Lemonade

How to use GestaltLabs/Ornstein-3.6-27B-RYS-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GestaltLabs/Ornstein-3.6-27B-RYS-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornstein-3.6-27B-RYS-GGUF-Q4_K_M

List all available models

lemonade list

[

Ornstein-3.6-27B-RYS-GGUF

GGUF quantizations of GestaltLabs/Ornstein-3.6-27B-RYS — the RYS-enhanced dense Ornstein model.

About Gestalt Lab

We are a proudly Canadian research collective working to advance sovereign Canadian AI — open-weight models that Canadians (and everyone else) can run locally, study, and build on without dependence on closed foreign APIs. All training, fine-tuning, and quantization is done on local and self-funded compute. By supporting this work, you help keep frontier model development accessible, transparent, and under Canadian stewardship.

Important: requires a patched llama.cpp

RYS duplicates one of the middle layers, which breaks the hardcoded full_attention_interval = 4 assumption in stock llama.cpp's Qwen3.5 loader. These GGUFs are re-converted with per-layer head_count_kv baked in, and you need a llama.cpp that reads that per-layer metadata instead of falling back to the interval formula.

Patched fork: https://github.com/DJLougen/llama.cpp (default branch rys-qwen35, one commit on top of ggml-org/llama.cpp@d00685831, fully backward-compatible).

Stock llama.cpp, Ollama, LM Studio, and any other inference runtime built on stock llama.cpp will currently fail to load these files with a check_tensor_dims error on blk.33 — this is expected until/unless the patch is upstreamed.

Support This Work

Our training compute is entirely self-funded. If this model is useful to you, consider supporting the lab:

Support on Ko-fi

Available Quantizations

File	Quant	Size	Notes
`ornstein-3.6-27b-rys-q8_0.gguf`	Q8_0	~27 GB	Near-lossless, largest
`ornstein-3.6-27b-rys-q6_k.gguf`	Q6_K	~21 GB	Very high quality
`ornstein-3.6-27b-rys-q5_k_m.gguf`	Q5_K_M	~18 GB	Strong quality/size balance
`ornstein-3.6-27b-rys-q4_k_m.gguf`	Q4_K_M	~16 GB	Recommended default
`ornstein-3.6-27b-rys-q3_k_m.gguf`	Q3_K_M	~12 GB	Low-memory option

Model Lineage

Qwen 3.6 27B → Ornstein3.6 (DDM fine-tune) → RYS (layer 33 dup, +49%)

Model Details

Architecture: Qwen3.5 dense
Parameters: ~27B active
Layers: 65 (64 original + 1 RYS-duplicated layer 33)
Context: 131,072 tokens
GGUF metadata: per-layer head_count_kv array encoding the RYS-shifted attention pattern

Usage

Build the patched llama.cpp

git clone https://github.com/DJLougen/llama.cpp.git
cd llama.cpp
git checkout rys-qwen35
cmake -B build -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build -j

Drop -DGGML_CUDA=ON for a CPU-only build. The patch touches the GGUF loader and three model forward files; backend selection is independent.

Download + run

hf download GestaltLabs/Ornstein-3.6-27B-RYS-GGUF \
    ornstein-3.6-27b-rys-q4_k_m.gguf \
    --local-dir .

./build/bin/llama-server \
    -m ornstein-3.6-27b-rys-q4_k_m.gguf \
    --host 0.0.0.0 --port 8080 \
    --n-gpu-layers 99 --ctx-size 131072 \
    --flash-attn on --jinja \
    -ctk q4_0 -ctv q4_0