Instructions to use Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q", filename="Qwopus3.6-27B-Coder-MoQ-4.0-12.6GB.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q 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 Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q # Run inference directly in the terminal: llama cli -hf Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q # Run inference directly in the terminal: llama cli -hf Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q
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 Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q # Run inference directly in the terminal: ./llama-cli -hf Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q
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 Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q # Run inference directly in the terminal: ./build/bin/llama-cli -hf Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q
Use Docker
docker model run hf.co/Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q
- LM Studio
- Jan
- Ollama
How to use Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q with Ollama:
ollama run hf.co/Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q
- Unsloth Studio
How to use Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q to start chatting
- Pi
How to use Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q
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 Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q
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 "Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q" \ --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"
- Docker Model Runner
How to use Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q with Docker Model Runner:
docker model run hf.co/Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q
- Lemonade
How to use Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q
Run and chat with the model
lemonade run user.Qwopus3.6-27B-Coder-GGUF-Predator-Q-{{QUANT_TAG}}List all available models
lemonade list
⚠️ THIS REPO HAS BEEN RENAMED
This repository is deprecated. The contents have been moved to:
Fredred89/Qwopus3.6-27B-Coder-GGUF-kaitchup-MoQ-4.0
Why the rename: The "Predator-Q" branding implied novel work, but the underlying GGUF is a direct application of kaitchup's MoQ recipe (from kaitchup/Qwen3.6-27B-GGUF-MoQ) to the Qwopus3.6-27B-Coder model. The new repo name properly attributes the source.
What we actually did:
- Converted Qwopus3.6-27B-Coder from safetensors → F16 GGUF (~30 min, 53.8 GB)
- Generated an importance matrix via
llama-imatrix(~1 hour) - Applied kaitchup's MoQ recipe at 4.0 BPW via
llama-quantize(~5 min) - Validated with LCB-30 (LiveCodeBench easy subset, 30 problems)
The actual GGUF file is unchanged (same SHA256: 587840e75895199e5ad771bfa7dfd9682f6d85ae295ad00001b78adb485c52c1). It just has a properly attributed name now.
Attribution
- Recipe source:
kaitchup/Qwen3.6-27B-GGUF-MoQby Benjamin Marie (kaitchup) - Methodology: MoQ (Mixture of Quantizations) by Waleed Ahmad (w-ahmad)
- Base model: Jackrong/Qwopus3.6-27B-Coder
- Quantization tool: llama.cpp (open source)
The new repo (Fredred89/Qwopus3.6-27B-Coder-GGUF-kaitchup-MoQ-4.0) contains the same GGUF plus multi-benchmark validation results (HumanEval+ 164, MBPP+ 100, BigCodeBench 50, LCB-30 30).
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We're not able to determine the quantization variants.