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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Update README.md

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	---
	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)

	```bash
	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