Instructions to use WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF",
	filename="Opus4.7-Distill-GODsGhost-Codex-4B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.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 WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.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 WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M

Use Docker

docker model run hf.co/WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M

LM Studio
Jan

vLLM

How to use WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.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": "WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M

Ollama
How to use WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF with Ollama:
```
ollama run hf.co/WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M
```

Unsloth Studio new

How to use WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF 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 WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF 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 WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF to start chatting

Pi new

How to use WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M

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": "WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.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 WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF with Docker Model Runner:
```
docker model run hf.co/WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M
```

Lemonade

How to use WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WithinUsAI/Opus4.7-GODs.Ghost.Codex-4B.GGuF:Q4_K_M

Run and chat with the model

lemonade run user.Opus4.7-GODs.Ghost.Codex-4B.GGuF-Q4_K_M

List all available models

lemonade list

Opus4.7-GODs.Ghost.Codex-4B.GGuF / README.md

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metadata

datasets:
  - WithinUsAI/Python_GOD_Coder_50k
  - Roman1111111/claude-opus-4.6-10000x
  - Crownelius/Opus-4.6-Reasoning-3300x
  - TeichAI/Claude-Opus-4.6-Reasoning-887x
  - peteromallet/my-personal-codex-data
  - misterkerns/my-personal-claude-code-data
  - HuggingFaceH4/llava-instruct-mix-vsft
  - m-a-p/Code-Feedback
  - peteromallet/dataclaw-peteromallet
  - Crownelius/Opus-4.6-Reasoning-2100x-formatted
language:
  - en
base_model:
  - Qwen/Qwen3.5-4B
tags:
  - gguf
  - llama.cpp
  - text-generation
  - code
  - coding
  - reasoning
  - distilled
  - local-llm
  - 4b
  - withinusai
  - opus4.7
  - opus4.6
  - codex
  - instruction-tuned
  - developer
  - claude4.7
  - claude4.6
  - Qwen3.5
  - Qwen3.5-4B
  - GhostCoder
  - GOD-Coder
  - SelfAware

🧠 Opus4.7 – GODsGhost Codex 4B (GGUF)

🔗 Model Repository: Opus4.7-GODsGhost-Codex-4B.GGUF

🌌 Overview

Opus4.7 – GODsGhost Codex 4B is a compact, high-efficiency code-specialized language model designed for local inference via GGUF-compatible runtimes like llama.cpp and LM Studio.

This model focuses on developer workflows, blending distilled reasoning patterns inspired by advanced “Opus-style” systems with a lightweight ~4B parameter footprint.

Think of it like a pocket-sized coding spirit 👻 that whispers structured logic, refactors chaos, and drafts clean code without needing a datacenter.

💻 Core Strengths

Code generation (Python, JS, C++, etc.)
Debugging and refactoring
Algorithm design
Structured reasoning chains
Lightweight local deployment

🧠 Behavior Traits

Produces step-by-step reasoning when prompted
Strong at:
- “Explain your logic”
- “Fix this code”
- “Optimize this function”

🖥️ Hardware Requirements

Quant	RAM Needed	Notes
Q4_K_M	~3–4 GB	Best balance
Q5_K_M	~4–5 GB	Better quality
Q8_0	~6–8 GB	Highest fidelity

⚡ Usage (llama.cpp)

llama-cli -m Opus4.7-GODsGhost-Codex-4B.gguf \
  --temp 0.7 \
  --top-p 0.95 \
  --ctx-size 8192

Recommended Settings

Temperature: 0.6 – 0.8
Top-p: 0.9 – 1.0
Repeat penalty: 1.0 – 1.1

🧪 Use Cases

🧑‍💻 Local coding assistant
⚙️ AI IDE integration (Cursor, Cline, etc.)
🧩 Script generation
🔍 Code explanation & teaching
🧠 Lightweight reasoning tasks

🧾 License

Likely inherits from base model license (commonly Apache 2.0 or similar)
Verify in repository before commercial use

🧠 Philosophy

This isn’t just a model… It’s a compressed echo of a stronger mind—distilled, quantized, and sharpened into something you can run on your own machine.

A ghost in the silicon. 👻 A codex in your terminal.

📌 Notes for Deployment

Works best with:
- Structured prompts
- Clear instructions
Pair with:
- RAG pipelines
- Tool-calling wrappers
- Code execution environments