Instructions to use GestaltLabs/Ornstein-3.5-9B-V2-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-3.5-9B-V2-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-3.5-9B-V2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GestaltLabs/Ornstein-3.5-9B-V2-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-3.5-9B-V2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GestaltLabs/Ornstein-3.5-9B-V2-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-3.5-9B-V2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GestaltLabs/Ornstein-3.5-9B-V2-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-3.5-9B-V2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GestaltLabs/Ornstein-3.5-9B-V2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/GestaltLabs/Ornstein-3.5-9B-V2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use GestaltLabs/Ornstein-3.5-9B-V2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GestaltLabs/Ornstein-3.5-9B-V2-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-3.5-9B-V2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GestaltLabs/Ornstein-3.5-9B-V2-GGUF:Q4_K_M

Ollama
How to use GestaltLabs/Ornstein-3.5-9B-V2-GGUF with Ollama:
```
ollama run hf.co/GestaltLabs/Ornstein-3.5-9B-V2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use GestaltLabs/Ornstein-3.5-9B-V2-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-3.5-9B-V2-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-3.5-9B-V2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use GestaltLabs/Ornstein-3.5-9B-V2-GGUF with Docker Model Runner:
```
docker model run hf.co/GestaltLabs/Ornstein-3.5-9B-V2-GGUF:Q4_K_M
```

Lemonade

How to use GestaltLabs/Ornstein-3.5-9B-V2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GestaltLabs/Ornstein-3.5-9B-V2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornstein-3.5-9B-V2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use GestaltLabs/Ornstein-3.5-9B-V2-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-3.5-9B-V2-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-3.5-9B-V2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use GestaltLabs/Ornstein-3.5-9B-V2-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-3.5-9B-V2-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-3.5-9B-V2-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-3.5-9B-V2-GGUF / README.md

DJLougen

Document embedded MTP draft head / --spec-type draft-mtp

4c57e2b verified 3 months ago

preview code

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metadata

base_model: GestaltLabs/Ornstein-3.5-9B-V2
base_model_relation: quantized
language:
  - en
license: apache-2.0
library_name: gguf
tags:
  - gguf
  - llama.cpp
  - quantized
  - reasoning
  - ai-research
  - qwen3.5
  - multimodal
  - mtmd
pipeline_tag: text-generation

Ornstein 3.5 9B — V2 · GGUF

GGUF quantizations of GestaltLabs/Ornstein-3.5-9B-V2 — the reinforcement-learning post-training (V2) of Ornstein 3.5 9B. Each quant has a sibling .sha256 checksum, and a separate vision projector (mmproj) ships the multimodal tower for image/video input.

Files

File	Notes
`ornstein-v2-Q4_K_M.gguf`	recommended default
`ornstein-v2-Q5_K_M.gguf`	high quality
`ornstein-v2-Q6_K.gguf`	very high quality
`ornstein-v2-Q8_0.gguf`	near-lossless
`ornstein-v2-f16.gguf`	full F16
`mmproj-ornstein-v2-f16.gguf`	vision encoder — pair with any quant for image/video input

Usage (llama.cpp)

Text:

llama-cli -m ornstein-v2-Q4_K_M.gguf -p "Derive the variance of a sum of two correlated random variables."

Multimodal (image/video) — add the vision projector:

llama-mtmd-cli -m ornstein-v2-Q4_K_M.gguf \
  --mmproj mmproj-ornstein-v2-f16.gguf \
  --image picture.jpg -p "Describe this image."

Quality and speed scale with quant size; Q4_K_M is a strong default for ~8 GB of VRAM/RAM.

Speculative decoding (MTP)

Every quant embeds the model's native multi-token-prediction (MTP) draft head (GGUF block_count 33), so self-speculative decoding runs from a single file — no separate draft model needed:

llama-cli -m ornstein-v2-Q4_K_M.gguf --spec-type draft-mtp \
  -p "Write a Python function is_prime(n)."

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

License

Apache 2.0 — inherited from the Qwen 3.5 9B base release.