Instructions to use orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-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 orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-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 orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF # Run inference directly in the terminal: llama cli -hf orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF # Run inference directly in the terminal: llama cli -hf orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-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 orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF # Run inference directly in the terminal: ./llama-cli -hf orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-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 orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF
Use Docker
docker model run hf.co/orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF
- LM Studio
- Jan
- vLLM
How to use orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-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": "orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF
- Ollama
How to use orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF with Ollama:
ollama run hf.co/orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF
- Unsloth Desktop
- Pi
How to use orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-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": "orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF with Docker Model Runner:
docker model run hf.co/orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF
- Lemonade
How to use orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF
Run and chat with the model
lemonade run user.OrcaSAQ-2-Cyber-27B-Uncensored-GGUF-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-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 orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-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 orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-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 "orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-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"
OrcaSAQ2 Cyber 27B reached 68 tok/s and 240K context on 2× RTX 3060 12GB
I tested orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF on two RTX 3060 12GB cards using llamAmpere (so good).
Final configuration:
Context: 245,760 tokens (240K)
KV cache: Q4_0
MTP depth: 4
Batch/ubatch: 384 / 96
Tensor split: 1.25,1
Backend: llamAmpere, CUDA SM86
Driver: 580.178.04
Results:
Short code generation: 68.10 tok/s
12K-token prefill: 490 tok/s
Long-context test: 219,155 input tokens
Long-context prefill: 221.6 tok/s
Long-context total time: 990 seconds
Sentinel retrieval: Correct
Truncation: None
Remaining VRAM: 361 / 377 MiB
For comparison, my Qwen3.8 27B UD-Q5_K_M profile reached 49.61 tok/s with llamAmpere at 96K context.
Qwen Q5 size: 19.76 GB
OrcaSAQ2 size: 15.68 GB
Qwen MTP acceptance: 87.9%
Orca MTP acceptance: 93.0%
That makes Orca roughly 37% faster in my short-prompt tests while using a much larger configured context. The same two deterministic coding prompts were used, although output lengths differed, so this is not a perfect scientific comparison.
256K also loaded after reducing the batch to 128/32, but prefill dropped to about 320 tok/s and minimum free VRAM
fell to 255 MiB. I therefore kept 240K as the best speed/context compromise.
This is the best local 27B result I have measured so far.
Thanks for your details test result!
was meant to comment but i created a new discussion if its of interest to read SpaceMo0 https://huggingface.co/orcarouter/OrcaSAQ-2-Cyber-27B-Uncensored-GGUF/discussions/6