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

DJLougen commited on 29 days ago

Commit

2054ac6

verified ·

1 Parent(s): 37390e6

Upload README.md with huggingface_hub

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README.md +10 -0

README.md CHANGED Viewed

@@ -19,6 +19,8 @@ language:
 - en
 ---
 # Ornstein-3.6-27B-RYS-GGUF
 GGUF quantizations for **Ornstein-3.6-27B-RYS** — the RYS-modified variant of Ornstein-3.6-27B with 66 layers (layers 22 & 23 duplicated, zero weight change).
@@ -115,6 +117,14 @@ Base architecture: [unsloth/Qwen3.6-27B](https://huggingface.co/unsloth/Qwen3.6-
 ---
 ## License
 Apache 2.0 — inherited from Qwen 3.6 and Ornstein-3.6-27B.

 - en
 ---
+![Ornstein-3.6-27B-RYS](ChatGPT%20Image%20Apr%2024,%202026,%2006_52_24%20PM(1).png)
 # Ornstein-3.6-27B-RYS-GGUF
 GGUF quantizations for **Ornstein-3.6-27B-RYS** — the RYS-modified variant of Ornstein-3.6-27B with 66 layers (layers 22 & 23 duplicated, zero weight change).
 ---
+## 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](https://ko-fi.com/djlougen)**
+---
 ## License
 Apache 2.0 — inherited from Qwen 3.6 and Ornstein-3.6-27B.