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.

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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-MoQ by 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).

Downloads last month: 37

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for Fredred89/Qwopus3.6-27B-Coder-GGUF-Predator-Q

Base model

Jackrong/Qwopus3.6-27B-v2

Adapter

Jackrong/Qwopus3.6-27B-Coder

Quantized

(26)

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