Instructions to use offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF 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 offmonreal/Ornith-1.5-35B-MaxQuality-MTP-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 offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX # Run inference directly in the terminal: llama cli -hf offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX # Run inference directly in the terminal: llama cli -hf offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX
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 offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX # Run inference directly in the terminal: ./llama-cli -hf offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX
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 offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX # Run inference directly in the terminal: ./build/bin/llama-cli -hf offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX
Use Docker
docker model run hf.co/offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX
- LM Studio
- Jan
- Ollama
How to use offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF with Ollama:
ollama run hf.co/offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX
- Unsloth Desktop
- Pi
How to use offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX
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": "offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF with Docker Model Runner:
docker model run hf.co/offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX
- Lemonade
How to use offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX
Run and chat with the model
lemonade run user.Ornith-1.5-35B-MaxQuality-MTP-GGUF-Q4_K_M_IMATRIX
List all available models
lemonade list
- Hermes Agent
How to use offmonreal/Ornith-1.5-35B-MaxQuality-MTP-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 offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX
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 offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX
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 "offmonreal/Ornith-1.5-35B-MaxQuality-MTP-GGUF:Q4_K_M_IMATRIX" \ --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"
Bigger quants
Hi, thanks for your work!
Im not as smart so reading the card didn't help me - but I still can't understand why there are no quants > Q4? Q6_K_L or Q8?
Hi! I’m not planning to make higher quants. Anything above Q4 is outside the scope of what I’m targeting.
I’m focusing specifically on consumer GPUs with 12–24 GB of VRAM, so my goal is to get the best possible quality while maintaining the highest possible inference speed on typical home hardware. That’s why my variants don’t go above Q4.