Instructions to use YTan2000/Ornith-1.0-35B-TQ3_4S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use YTan2000/Ornith-1.0-35B-TQ3_4S 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 YTan2000/Ornith-1.0-35B-TQ3_4S:F16 # Run inference directly in the terminal: llama cli -hf YTan2000/Ornith-1.0-35B-TQ3_4S:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf YTan2000/Ornith-1.0-35B-TQ3_4S:F16 # Run inference directly in the terminal: llama cli -hf YTan2000/Ornith-1.0-35B-TQ3_4S: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 YTan2000/Ornith-1.0-35B-TQ3_4S:F16 # Run inference directly in the terminal: ./llama-cli -hf YTan2000/Ornith-1.0-35B-TQ3_4S: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 YTan2000/Ornith-1.0-35B-TQ3_4S:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf YTan2000/Ornith-1.0-35B-TQ3_4S:F16
Use Docker
docker model run hf.co/YTan2000/Ornith-1.0-35B-TQ3_4S:F16
- LM Studio
- Jan
- vLLM
How to use YTan2000/Ornith-1.0-35B-TQ3_4S with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "YTan2000/Ornith-1.0-35B-TQ3_4S" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "YTan2000/Ornith-1.0-35B-TQ3_4S", "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/YTan2000/Ornith-1.0-35B-TQ3_4S:F16
- Ollama
How to use YTan2000/Ornith-1.0-35B-TQ3_4S with Ollama:
ollama run hf.co/YTan2000/Ornith-1.0-35B-TQ3_4S:F16
- Unsloth Desktop
- Pi
How to use YTan2000/Ornith-1.0-35B-TQ3_4S with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf YTan2000/Ornith-1.0-35B-TQ3_4S:F16
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": "YTan2000/Ornith-1.0-35B-TQ3_4S:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use YTan2000/Ornith-1.0-35B-TQ3_4S with Docker Model Runner:
docker model run hf.co/YTan2000/Ornith-1.0-35B-TQ3_4S:F16
- Lemonade
How to use YTan2000/Ornith-1.0-35B-TQ3_4S with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull YTan2000/Ornith-1.0-35B-TQ3_4S:F16
Run and chat with the model
lemonade run user.Ornith-1.0-35B-TQ3_4S-F16
List all available models
lemonade list
- Hermes Agent
How to use YTan2000/Ornith-1.0-35B-TQ3_4S with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf YTan2000/Ornith-1.0-35B-TQ3_4S: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 YTan2000/Ornith-1.0-35B-TQ3_4S:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use YTan2000/Ornith-1.0-35B-TQ3_4S with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf YTan2000/Ornith-1.0-35B-TQ3_4S:F16
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 "YTan2000/Ornith-1.0-35B-TQ3_4S:F16" \ --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"
Will this work with 16 GB VRAM?
Will this work with the RTX 4070 Ti S (16 GB) graphics card? If it does, what is the maximum context size it can handle?
This is actually beat model for 16gb. It is moe and only 3b active. Remember to compile the turbo-tan/llama.cpp-tq3 main branch
I'm using the YTan2000 base Qwen3.6 model (the model this is based on) with a laptop 3060 - 6GB VRAM and 65k context and it's FAST - like 40 t/s with low context and 30 t/s when context is full. It's close to 50% faster than the Unsloth IQ4_XS. I am downloading this now and I'm so excited.
Yes