Instructions to use DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF",
	filename="Ornstein3.6-35B-A3B-SABER-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 DJLougen/Ornstein3.6-35B-A3B-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 DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-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 DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-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 DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-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 DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DJLougen/Ornstein3.6-35B-A3B-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": "DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF:Q4_K_M

Ollama
How to use DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF with Ollama:
```
ollama run hf.co/DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF:Q4_K_M
```

Unsloth Studio new

How to use DJLougen/Ornstein3.6-35B-A3B-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 DJLougen/Ornstein3.6-35B-A3B-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 DJLougen/Ornstein3.6-35B-A3B-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 DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF to start chatting

Pi new

How to use DJLougen/Ornstein3.6-35B-A3B-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 DJLougen/Ornstein3.6-35B-A3B-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": "DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DJLougen/Ornstein3.6-35B-A3B-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 DJLougen/Ornstein3.6-35B-A3B-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 DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF with Docker Model Runner:
```
docker model run hf.co/DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF:Q4_K_M
```

Lemonade

How to use DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ornstein3.6-35B-A3B-SABER-GGUF-Q4_K_M

List all available models

lemonade list

Ornstein3.6-35B-A3B-SABER — GGUF

GGUF quantizations of DJLougen/Ornstein3.6-35B-A3B-SABER for use with llama.cpp, ollama, LM Studio, and compatible runtimes.

Source model is the SABER-ablated variant of Ornstein3.6-35B-A3B (Qwen3.5 MoE, 35B total / ~3B active). See the source model card for a description of SABER.

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

Quantization suite (8-bit and under)

All variants derived from the bf16 SABER safetensors via llama.cpp convert_hf_to_gguf.py → llama-quantize. Non-Q8_0 K-quants are derived from the Q8_0 file with --allow-requantize.

File	Bits	Size (approx)	Notes
`…-Q8_0.gguf`	8.5	~36 GB	Highest fidelity, near-lossless
`…-Q6_K.gguf`	6.6	~29 GB	Very close to Q8_0 quality
`…-Q5_K_M.gguf`	5.7	~25 GB	Recommended for high-quality inference
`…-Q5_K_S.gguf`	5.5	~24 GB
`…-Q4_K_M.gguf`	4.8	~22 GB	Recommended default
`…-Q4_K_S.gguf`	4.6	~20 GB
`…-Q3_K_M.gguf`	3.9	~17 GB	Fits most 24 GB VRAM setups
`…-Q3_K_S.gguf`	3.5	~15 GB
`…-Q2_K.gguf`	~3	~13 GB	Emergency size — expect quality loss

Active parameters per token are ~3B regardless of file size; the table reflects total weights on disk.

Usage (llama.cpp)

./llama-cli -m Ornstein3.6-35B-A3B-SABER-Q4_K_M.gguf \
    -p "You are a helpful assistant." \
    -cnv --temp 0.7 --top-p 0.9

Intended use

Research and red-teaming. The SABER-ablated model complies with requests its parent model refused. Deploy behind your own policy/logging layer.

License

Apache 2.0, inherited from the base model.

Downloads last month: 1,384

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for DJLougen/Ornstein3.6-35B-A3B-SABER-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

unsloth/Qwen3.6-35B-A3B

Finetuned

DJLougen/Ornstein3.6-35B-A3B

Finetuned

DJLougen/Ornstein3.6-35B-A3B-SABER

Quantized

(6)

this model