Instructions to use mudler/Ornith-1.0-35B-APEX-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 mudler/Ornith-1.0-35B-APEX-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 mudler/Ornith-1.0-35B-APEX-GGUF # Run inference directly in the terminal: llama cli -hf mudler/Ornith-1.0-35B-APEX-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mudler/Ornith-1.0-35B-APEX-GGUF # Run inference directly in the terminal: llama cli -hf mudler/Ornith-1.0-35B-APEX-GGUF
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 mudler/Ornith-1.0-35B-APEX-GGUF # Run inference directly in the terminal: ./llama-cli -hf mudler/Ornith-1.0-35B-APEX-GGUF
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 mudler/Ornith-1.0-35B-APEX-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf mudler/Ornith-1.0-35B-APEX-GGUF
Use Docker
docker model run hf.co/mudler/Ornith-1.0-35B-APEX-GGUF
- LM Studio
- Jan
- Ollama
How to use mudler/Ornith-1.0-35B-APEX-GGUF with Ollama:
ollama run hf.co/mudler/Ornith-1.0-35B-APEX-GGUF
- Unsloth Desktop
- Pi
How to use mudler/Ornith-1.0-35B-APEX-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mudler/Ornith-1.0-35B-APEX-GGUF
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": "mudler/Ornith-1.0-35B-APEX-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use mudler/Ornith-1.0-35B-APEX-GGUF with Docker Model Runner:
docker model run hf.co/mudler/Ornith-1.0-35B-APEX-GGUF
- Lemonade
How to use mudler/Ornith-1.0-35B-APEX-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mudler/Ornith-1.0-35B-APEX-GGUF
Run and chat with the model
lemonade run user.Ornith-1.0-35B-APEX-GGUF-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use mudler/Ornith-1.0-35B-APEX-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 mudler/Ornith-1.0-35B-APEX-GGUF
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 mudler/Ornith-1.0-35B-APEX-GGUF
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use mudler/Ornith-1.0-35B-APEX-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mudler/Ornith-1.0-35B-APEX-GGUF
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 "mudler/Ornith-1.0-35B-APEX-GGUF" \ --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"
Amazing
Worked about 11 million tokens on two projects, consistency is crazy for it's size. This is the best of it's variants among I've tried. Using with Turbo Quant3 and a private context-governor that manages to save up to 60-80% for my context window (76K only,but I'm running 1-2 hours without compaction with the governor)
All we need now is APEX I-Balance 0-Refusal version.
Including original, agentsworld and Darwin (and its' father and mother models).
This is by far the best squeeze.
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λμμ΄ λμμ΅λλ€ κ°μ¬ν©λλ€!
λμμ΄ λμμ΅λλ€ κ°μ¬ν©λλ€!
Try this too https://huggingface.co/mudler/KAT-Coder-V2.5-Dev-APEX-GGUF
This is based on Qwen 3.6 35B-A3B (newer base model) and is amazing for coding tasks.