Instructions to use sphaela/Ornith-1.0-9B-AutoRound-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 sphaela/Ornith-1.0-9B-AutoRound-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 sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf sphaela/Ornith-1.0-9B-AutoRound-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 sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf sphaela/Ornith-1.0-9B-AutoRound-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 sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
Use Docker
docker model run hf.co/sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use sphaela/Ornith-1.0-9B-AutoRound-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sphaela/Ornith-1.0-9B-AutoRound-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": "sphaela/Ornith-1.0-9B-AutoRound-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
- Ollama
How to use sphaela/Ornith-1.0-9B-AutoRound-GGUF with Ollama:
ollama run hf.co/sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use sphaela/Ornith-1.0-9B-AutoRound-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
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": "sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use sphaela/Ornith-1.0-9B-AutoRound-GGUF with Docker Model Runner:
docker model run hf.co/sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
- Lemonade
How to use sphaela/Ornith-1.0-9B-AutoRound-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Ornith-1.0-9B-AutoRound-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use sphaela/Ornith-1.0-9B-AutoRound-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 sphaela/Ornith-1.0-9B-AutoRound-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 sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use sphaela/Ornith-1.0-9B-AutoRound-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M
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 "sphaela/Ornith-1.0-9B-AutoRound-GGUF:Q4_K_M" \ --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"
Download comparison.csv from sphaela/Ornith-1.0-9B-AutoRound-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 786 Bytes
-
https://huggingface.co/sphaela/Ornith-1.0-9B-AutoRound-GGUF/resolve/main/comparison.csv
- Command line
-
hf download hf://sphaela/Ornith-1.0-9B-AutoRound-GGUF/comparison.csv
-
curl -L -o comparison.csv https://huggingface.co/sphaela/Ornith-1.0-9B-AutoRound-GGUF/resolve/main/comparison.csv
786 Bytes
| scheme,size_gb,bpw,ppl,dppl_pct,kld_mean,kld_q99,top1_agree_pct | |
| Q2_K_S,3.697238496,3.303390424817804,9.893337,21.737732477248326,0.295998,2.746646,76.829 | |
| Q3_K_S,4.259406304,3.8056733465436126,8.527342,4.929133530779726,0.121556,1.086482,84.995 | |
| Q3_K_M,4.516569568,4.035442311902912,8.569239,5.44467705038278,0.121405,1.068216,84.866 | |
| Q3_K_L,4.925514208,4.400824152841248,8.600858,5.8337495507128745,0.120064,1.056818,84.898 | |
| Q4_K_S,5.35162928,4.781547346716418,8.047454,-0.9758990141585313,0.042472,0.359782,91.014 | |
| Q4_K_M,5.629108704,5.029468294558231,8.075174,-0.6348037958040598,0.040609,0.348605,91.209 | |
| Q6_K,7.359259104,6.575314544681959,8.150005,0.28599332846300257,0.00402,0.025164,96.639 | |
| Q8_0,9.52750128,8.51258487512821,8.136132,0.11528575399577318,0.002044,0.012462,97.51 | |