Instructions to use GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-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-9Bv2.5-NSC-ACE-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-9Bv2.5-NSC-ACE-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-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-9Bv2.5-NSC-ACE-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-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-9Bv2.5-NSC-ACE-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-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-9Bv2.5-NSC-ACE-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-GGUF:Q4_K_M

Use Docker

docker model run hf.co/GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-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-9Bv2.5-NSC-ACE-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-GGUF:Q4_K_M

Ollama
How to use GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-GGUF with Ollama:
```
ollama run hf.co/GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-GGUF:Q4_K_M
```
Unsloth Desktop

How to use GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-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-9Bv2.5-NSC-ACE-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-9Bv2.5-NSC-ACE-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-GGUF with Docker Model Runner:
```
docker model run hf.co/GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-GGUF:Q4_K_M
```

Lemonade

How to use GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornstein-9Bv2.5-NSC-ACE-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-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-9Bv2.5-NSC-ACE-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-9Bv2.5-NSC-ACE-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-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-9Bv2.5-NSC-ACE-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-9Bv2.5-NSC-ACE-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 9B v2.5 — NSC-ACE · GGUF

llama.cpp GGUF builds of DJLougen/Ornstein-9Bv2.5-NSC-ACE (text decoder). The MTP draft head is embedded in every file (blk.32.nextn.*) — enable speculative decoding with --spec-type draft-mtp (llama.cpp b9940+), no separate draft model needed.

File	Quant	Size	Notes
`Ornstein-9Bv2.5-NSC-ACE-Q4_K_M.gguf`	Q4_K_M	5.8 GB	smallest, good default
`Ornstein-9Bv2.5-NSC-ACE-Q5_K_M.gguf`	Q5_K_M	6.6 GB	balanced
`Ornstein-9Bv2.5-NSC-ACE-Q6_K.gguf`	Q6_K	7.6 GB	high quality
`Ornstein-9Bv2.5-NSC-ACE-Q8_0.gguf`	Q8_0	9.8 GB	near-lossless, behaviorally verified
`Ornstein-9Bv2.5-NSC-ACE-F16.gguf`	F16	18.4 GB	full precision / re-quant source

llama-server -hf DJLougen/Ornstein-9Bv2.5-NSC-ACE-GGUF:Q8_0 -ngl 99 --spec-type draft-mtp

Measured on an RTX 3090 at Q8_0 with --spec-type draft-mtp: 67.6 → 100.7 tok/s on code (+49%), 90.6 → 110.9 tok/s on prose (+22%) vs. non-speculative on the same build.

Sample with temperature=0.6, top_p=0.95, top_k=20 (Qwen3.5 reasoning defaults). Hermes-style <tools> tool calling works via the embedded chat template. See the main repo for recipe, evaluation, and caveats.

Downloads last month: 53

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for GestaltLabs/Ornstein-9Bv2.5-NSC-ACE-GGUF

Base model

DJLougen/Ornstein-3.5-9B-V2.5

Finetuned

GestaltLabs/Ornstein-9Bv2.5-NSC-ACE

Quantized

(1)

this model