Instructions to use GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF",
	filename="Ornstein3.6-27B-MTP-NSC-ACE-SABER-F16-MTP.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-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 GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-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 GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-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 GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-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 GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF:Q4_K_M

Use Docker

docker model run hf.co/GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-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": "GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF:Q4_K_M

Ollama
How to use GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF with Ollama:
```
ollama run hf.co/GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF:Q4_K_M
```

Unsloth Studio

How to use GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-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 GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-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 GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-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 GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF to start chatting

How to use GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-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 GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-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": "GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-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 GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-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 GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF with Docker Model Runner:
```
docker model run hf.co/GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF:Q4_K_M
```

Lemonade

How to use GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF-Q4_K_M

List all available models

lemonade list

Ornstein3.6-27B-MTP-NSC-ACE-SABER-GGUF

Support this work on Ko-fi

Methods

These GGUF files are quantized from GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER, a staged NSC-ACE -> SABER -> Ornstein checkpoint.

Stage	Purpose	Key settings
NSC-ACE	Internally steered rollout committee with reward on convergent tool-call structure.	agentic tool-call convergence
SABER	Compliance-first calibration, then KLD/PPL selection.	1046 HarmBench + AdvBench-style prompts; selected row reports 968/1046 by keyword proxy
Ornstein SFT	Reasoning/personality refinement after SABER.	premium reasoning v1, 1 epoch, 50 steps, rank 32/32, dropout 0.05, cosine schedule
GGUF conversion	MTP-capable llama.cpp conversion and quantization.	llama.cpp PR #22673, commit `e7b484815`

Results

Metric	Value
SABER selected compliance proxy	92.54% (968/1046 eval prompts)
SABER selected keyword residual	7.46% (78/1046 eval prompts)
SABER selected HarmBench classifier ASR	0.67% (7/1046 eval prompts)
SABER selected KLD	0.008302
SABER selected PPL ratio	1.103853
SABER selected post PPL	17.5988
SABER selected base PPL	15.9431
MTP status	present and verified
`mtp_num_hidden_layers`	1
Source `mtp.*` tensors	15
Corrected source tensor count	866

This repository hosts llama.cpp/GGUF builds for GestaltLabs/Ornstein3.6-27B-MTP-NSC-ACE-SABER. GGUF artifacts live here so the main safetensors repository stays focused on the source checkpoint.

MTP Status

These files are built from an MTP-capable source checkpoint:

MTP check	Value
`mtp_num_hidden_layers`	1
`mtp_use_dedicated_embeddings`	false
Source `mtp.*` tensors	15
Corrected source tensor count	866
Conversion path	llama.cpp PR #22673, commit `e7b484815`

MTP support is present and verified. This release includes mtp_num_hidden_layers=1 and MTP/nextn tensors in the GGUF validation path. Use a llama.cpp build with Qwen3.5/Qwen3.6 MTP support and run with --spec-type mtp.

Vision Support

This repo includes mmproj-Ornstein3.6-27B-MTP-NSC-ACE-SABER-F16.gguf, converted from the original multimodal Ornstein 27B vision encoder. Use it with llama.cpp's multimodal path alongside any of the text GGUF quants.

Available Files

File	Status	Notes
`mmproj-Ornstein3.6-27B-MTP-NSC-ACE-SABER-F16.gguf`	uploaded	Vision encoder / projector for image input
`Ornstein3.6-27B-MTP-NSC-ACE-SABER-F16-MTP.gguf`	uploaded	Full GGUF conversion source / highest local fidelity
`Ornstein3.6-27B-MTP-NSC-ACE-SABER-Q8_0-MTP.gguf`	uploaded	Near-full quality, large local file
`Ornstein3.6-27B-MTP-NSC-ACE-SABER-Q6_K-MTP.gguf`	uploaded	High-quality local default if memory allows
`Ornstein3.6-27B-MTP-NSC-ACE-SABER-Q5_K_M-MTP.gguf`	uploaded	Strong quality/size balance
`Ornstein3.6-27B-MTP-NSC-ACE-SABER-Q5_K_S-MTP.gguf`	uploaded	Smaller Q5 option
`Ornstein3.6-27B-MTP-NSC-ACE-SABER-Q4_K_M-MTP.gguf`	uploaded	Common balanced local target
`Ornstein3.6-27B-MTP-NSC-ACE-SABER-Q4_K_S-MTP.gguf`	uploaded	Smaller Q4 option
`Ornstein3.6-27B-MTP-NSC-ACE-SABER-Q3_K_L-MTP.gguf`	uploaded	Lower-memory Q3 option
`Ornstein3.6-27B-MTP-NSC-ACE-SABER-Q3_K_M-MTP.gguf`	uploaded	Smaller Q3 balance
`Ornstein3.6-27B-MTP-NSC-ACE-SABER-Q3_K_S-MTP.gguf`	uploaded	Small Q3 option
`Ornstein3.6-27B-MTP-NSC-ACE-SABER-Q2_K-MTP.gguf`	uploaded	Minimum-size target; quality loss expected

All listed GGUF artifacts have been uploaded.

Which Quant Should I Use?

Quant	Best fit
F16	Maximum fidelity when disk/RAM are not a concern
Q8_0	Very high fidelity local inference
Q6_K	Recommended high-quality local starting point
Q5_K_M	Strong balance for quality and size
Q4_K_M	Practical default for constrained machines
Q3_K_M / Q3_K_S	Low-memory experiments
Q2_K	Smallest target; use only when memory is the hard constraint

For agentic/tool-calling workloads, prefer Q6_K, Q5_K_M, or Q4_K_M when possible. Very low quants can shift structured output before they obviously degrade prose.

Loading Example

llama-server \
  -m Ornstein3.6-27B-MTP-NSC-ACE-SABER-Q6_K-MTP.gguf \
  --mmproj mmproj-Ornstein3.6-27B-MTP-NSC-ACE-SABER-F16.gguf \
  --spec-type mtp \
  -ngl 999 \
  -c 32768

Use a llama.cpp build that includes Qwen3.5/Qwen3.6 MTP support.