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 / README.md

DJLougen

Update README.md

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	---
	base_model: GestaltLabs/Ornstein-3.6-27B-RYS
	language:
	- en
	license: apache-2.0
	pipeline_tag: text-generation
	tags:
	- gguf
	- llama.cpp
	- qwen3.5
	- qwen3.6
	- rys
	- text-generation
	- quantized
	- canada
	- sovereign-ai
	---

	[![Ornstein-3.6-27B-RYS]( Ornstein3.6-27B-RYS.png)

	# Ornstein-3.6-27B-RYS-GGUF

	GGUF quantizations of [GestaltLabs/Ornstein-3.6-27B-RYS](https://huggingface.co/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](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](https://ko-fi.com/djlougen)

	* * *

	## 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

	```bash
	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

	```bash
	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
	```

	## License

	Apache 2.0