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

Libraries

How to use DJLougen/Ornstein3.6-35B-A3B-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-GGUF",
	filename="Ornstein3.6-35B-A3B-IQ2_M.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 DJLougen/Ornstein3.6-35B-A3B-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-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-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-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-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-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-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-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

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

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

Unsloth Studio

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

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Run Hermes

hermes

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Ornstein3.6-35B-A3B-GGUF

GGUF quantizations of DJLougen/Ornstein3.6-35B-A3B — a Qwen 3.6 MoE fine-tune (35B total, ~3B active).

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

Image/video sidecars: This repository now includes the restored Qwen3.6 multimodal config, processor/preprocessor files, tokenizer/chat template, safetensors index, and model-vision-from-qwen3.6-base.safetensors visual tower sidecar. The existing .gguf binaries were not rewritten in this metadata-copy pass.

Model info

Architecture: Qwen3_5MoeForCausalLM (linear + full attention interleaved, Gated Delta Net)
Parameters: 34.66 B total / ~3 B active (256 experts, 8 active per token)
Context: 262,144 tokens
Hidden size / layers: 2048 / 40
Vocab: 248,320 tokens

All sub-8-bit quants were produced with an importance matrix (imatrix) computed from a mixed-domain multilingual calibration corpus (eaddario/imatrix-calibration → combined_all_medium): 200 chunks × 512 tokens = 102,400 calibration tokens.

Quant index

Choose a quant that fits in your RAM/VRAM with room for context. For MoE models quality degrades more sharply at low bit widths than for dense models of similar size — prefer Q4_K_M or higher if you have the memory.

File	Bits	Size	imatrix	Notes
`Ornstein3.6-35B-A3B-Q8_0.gguf`	8	36.9 GB	—	Reference, near-lossless
`Ornstein3.6-35B-A3B-Q6_K.gguf`	6.5	28.5 GB	—	Great default for 32 GB+ systems
`Ornstein3.6-35B-A3B-Q5_K_M.gguf`	5.5	24.7 GB	✓	Excellent quality/size balance
`Ornstein3.6-35B-A3B-Q5_K_S.gguf`	5.5	24.0 GB	✓	Slightly smaller Q5
`Ornstein3.6-35B-A3B-Q4_K_M.gguf`	4.5	21.2 GB	✓	Common 24 GB-card default
`Ornstein3.6-35B-A3B-Q4_K_S.gguf`	4.5	19.9 GB	✓	Smaller Q4
`Ornstein3.6-35B-A3B-IQ4_XS.gguf`	4.25	~18 GB	✓	Smaller than Q4_K_S, comparable quality with imatrix
`Ornstein3.6-35B-A3B-Q3_K_M.gguf`	3.5	16.8 GB	✓	Usable; quality below Q4
`Ornstein3.6-35B-A3B-Q3_K_S.gguf`	3.5	15.2 GB	✓	Smaller Q3
`Ornstein3.6-35B-A3B-IQ3_M.gguf`	3.3	~15 GB	✓	Mixed I-quant, beats Q3_K_S at similar size
`Ornstein3.6-35B-A3B-IQ3_XXS.gguf`	3.0	~13 GB	✓	Aggressive 3-bit
`Ornstein3.6-35B-A3B-Q2_K.gguf`	2.6	12.9 GB	✓	Lowest K-quant; expect degraded quality
`Ornstein3.6-35B-A3B-IQ2_M.gguf`	2.7	~12 GB	✓	Aggressive I-quant 2-bit
`imatrix.dat`	—	192 MB	—	Importance matrix (GGUF format)

Usage

llama.cpp

# Interactive chat
llama-cli -m Ornstein3.6-35B-A3B-Q4_K_M.gguf -cnv

# Single prompt
llama-cli -m Ornstein3.6-35B-A3B-Q5_K_M.gguf -p "Write a haiku about MoE routing."

# OpenAI-compatible server
llama-server -m Ornstein3.6-35B-A3B-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_5MoeForCausalLM 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 Ornstein3.6-35B-A3B-BF16.gguf

# 2. Importance matrix
llama-imatrix \
    -m Ornstein3.6-35B-A3B-BF16.gguf \
    -f calibration.txt \
    -o imatrix.dat \
    --chunks 200 -c 512 -b 512 -ngl 99

# 3. Quantize (example)
llama-quantize --imatrix imatrix.dat \
    Ornstein3.6-35B-A3B-BF16.gguf \
    Ornstein3.6-35B-A3B-Q4_K_M.gguf Q4_K_M

License

Apache 2.0 — inherited from the Qwen 3.6 base release.

Downloads last month: 1,422

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-GGUF

Base model

DJLougen/Ornstein3.6-35B-A3B

Quantized

(4)

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Collection including DJLougen/Ornstein3.6-35B-A3B-GGUF

Ornstein GGUF

Collection

All Ornstein GGUF quantizations for llama.cpp and ollama. • 6 items • Updated 19 days ago