Instructions to use DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use DJLougen/Ornstein3.6-35B-A3B-RYS-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-RYS-GGUF", filename="Ornstein3.6-35B-A3B-RYS-F16.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-RYS-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-RYS-GGUF:F16 # Run inference directly in the terminal: llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF:F16
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-RYS-GGUF:F16 # Run inference directly in the terminal: llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF:F16
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-RYS-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF:F16
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-RYS-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF:F16
Use Docker
docker model run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF:F16
- LM Studio
- Jan
- vLLM
How to use DJLougen/Ornstein3.6-35B-A3B-RYS-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-RYS-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-RYS-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF:F16
- Ollama
How to use DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF with Ollama:
ollama run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF:F16
- Unsloth Studio new
How to use DJLougen/Ornstein3.6-35B-A3B-RYS-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-RYS-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-RYS-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-RYS-GGUF to start chatting
- Pi new
How to use DJLougen/Ornstein3.6-35B-A3B-RYS-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-RYS-GGUF:F16
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-RYS-GGUF:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use DJLougen/Ornstein3.6-35B-A3B-RYS-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-RYS-GGUF:F16
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-RYS-GGUF:F16
Run Hermes
hermes
- Docker Model Runner
How to use DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF with Docker Model Runner:
docker model run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF:F16
- Lemonade
How to use DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF:F16
Run and chat with the model
lemonade run user.Ornstein3.6-35B-A3B-RYS-GGUF-F16
List all available models
lemonade list
Ornstein3.6-35B-A3B-RYS-GGUF
GGUF quantizations of DJLougen/Ornstein3.6-35B-A3B-RYS — the RYS-enhanced Ornstein fine-tune with layer 10 duplicated for a +49% reasoning improvement.
Full-precision model: DJLougen/Ornstein3.6-35B-A3B-RYS | Uncensored version: DJLougen/Ornstein3.6-35B-A3B-RYS-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.
Available Quantizations
| Quantization | Use Case |
|---|---|
| Q8_0 | Best quality, highest memory |
| Q6_K | Near-lossless, good for 48GB+ VRAM |
| Q5_K_M | Excellent quality/size balance |
| Q5_K_S | Slightly smaller Q5 |
| Q5_0 | Legacy Q5 format |
| Q4_K_M | Recommended default for 24GB VRAM |
| Q4_K_S | Smaller Q4 variant |
| Q4_0 | Legacy Q4 format |
| Q3_K_L | Low memory, acceptable quality |
| Q3_K_M | Lower memory |
| Q3_K_S | Aggressive 3-bit |
| Q2_K | Minimum viable quality |
Model Details
- Architecture: Qwen 3.6 MoE (35B total, ~3B active per token)
- Layers: 41 (40 original + 1 RYS-duplicated layer 10)
- Context: 262,144 tokens
- RYS improvement: +139% math, +7.2% instruction following, +49% combined
Usage
llama.cpp
llama-cli -m Ornstein3.6-35B-A3B-RYS-Q4_K_M.gguf -p "Your prompt here" -ngl 99
Ollama
ollama run hf.co/DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF:Q4_K_M
License
Apache 2.0
- Downloads last month
- 91
6-bit
8-bit
16-bit
Model tree for DJLougen/Ornstein3.6-35B-A3B-RYS-GGUF
Base model
Qwen/Qwen3.6-35B-A3B