Instructions to use GestaltLabs/Ornstein-3.6-27B-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 GestaltLabs/Ornstein-3.6-27B-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 GestaltLabs/Ornstein-3.6-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GestaltLabs/Ornstein-3.6-27B-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 GestaltLabs/Ornstein-3.6-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GestaltLabs/Ornstein-3.6-27B-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-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GestaltLabs/Ornstein-3.6-27B-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-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GestaltLabs/Ornstein-3.6-27B-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use GestaltLabs/Ornstein-3.6-27B-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-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-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

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

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use GestaltLabs/Ornstein-3.6-27B-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 GestaltLabs/Ornstein-3.6-27B-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-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Start the llama.cpp server

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

Ornstein-3.6-27B-GGUF

GGUF quantizations of GestaltLabs/Ornstein-3.6-27B — a Qwen 3.6 27B dense multimodal fine-tune with hybrid linear + full attention.

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

Model info

Architecture: Qwen3_5ForConditionalGeneration (linear + full attention interleaved, Gated Delta Net; text path extracted for GGUF)
Parameters: ~27 B dense
Context: 262,144 tokens
Hidden size / layers: 5120 / 64
Attention: 24 heads, 4 KV heads, head_dim 256

These GGUFs expose the text path only. For the multimodal variant use the full safetensors in the base repo.

Quant index

Choose a quant that fits in your RAM/VRAM with room for context. For a dense 27B prefer Q4_K_M or higher on 24 GB cards; go to Q5_K_M or Q6_K if you have headroom.

File	Bits	Notes
`Ornstein-3.6-27B-Q8_0.gguf`	8	Reference, near-lossless
`Ornstein-3.6-27B-Q6_K.gguf`	6.5	Great default for 32 GB+ systems
`Ornstein-3.6-27B-Q5_K_M.gguf`	5.5	Excellent quality/size balance
`Ornstein-3.6-27B-Q5_K_S.gguf`	5.5	Slightly smaller Q5
`Ornstein-3.6-27B-Q5_0.gguf`	5	Legacy 5-bit
`Ornstein-3.6-27B-Q4_K_M.gguf`	4.5	Common 24 GB-card default
`Ornstein-3.6-27B-Q4_K_S.gguf`	4.5	Smaller Q4
`Ornstein-3.6-27B-Q4_0.gguf`	4	Legacy 4-bit
`Ornstein-3.6-27B-IQ4_NL.gguf`	4.25	Non-linear 4-bit I-quant
`Ornstein-3.6-27B-IQ4_XS.gguf`	4.25	Smaller than Q4_K_S, comparable quality
`Ornstein-3.6-27B-Q3_K_L.gguf`	3.5	Largest Q3
`Ornstein-3.6-27B-Q3_K_M.gguf`	3.5	Usable; quality below Q4
`Ornstein-3.6-27B-Q3_K_S.gguf`	3.5	Smaller Q3
`Ornstein-3.6-27B-IQ3_M.gguf`	3.3	Mixed I-quant, beats Q3_K_S at similar size
`Ornstein-3.6-27B-IQ3_S.gguf`	3.1	3-bit I-quant
`Ornstein-3.6-27B-IQ3_XS.gguf`	3.0	Smaller 3-bit I-quant
`Ornstein-3.6-27B-IQ3_XXS.gguf`	3.0	Aggressive 3-bit
`Ornstein-3.6-27B-Q2_K.gguf`	2.6	Lowest K-quant; expect degraded quality

BF16/F16 GGUF is not shipped here — if you want full precision, grab the safetensors from the base repo.

Usage

llama.cpp

# Interactive chat
llama-cli -m Ornstein-3.6-27B-Q4_K_M.gguf -cnv

# Single prompt
llama-cli -m Ornstein-3.6-27B-Q5_K_M.gguf -p "Write a haiku about hybrid attention."

# OpenAI-compatible server
llama-server -m Ornstein-3.6-27B-Q4_K_M.gguf --host 0.0.0.0 --port 8080 -c 8192

Other runners

LM Studio, Ollama (via a Modelfile), koboldcpp, and text-generation-webui all load these GGUFs provided their bundled llama.cpp supports Qwen3_5ForConditionalGeneration with Gated Delta Net.

Reproducing the quants

# 1. Convert safetensors → BF16 GGUF
python llama.cpp/convert_hf_to_gguf.py <model_dir> \
    --outtype bf16 --outfile Ornstein-3.6-27B-BF16.gguf

# 2. Quantize (example)
llama-quantize Ornstein-3.6-27B-BF16.gguf \
    Ornstein-3.6-27B-Q4_K_M.gguf Q4_K_M

License

Apache 2.0 — inherited from the Qwen 3.6 base release.

Downloads last month: 134

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Collection including GestaltLabs/Ornstein-3.6-27B-GGUF

Ornstein Series

Collection

Ornstein post-trained models: base, Hermes, SABER. GGUF + MLX. • 9 items • Updated May 22 • 1