Text Generation
GGUF
English
llama.cpp
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
imatrix
conversational
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
- llama-cpp-python
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
File size: 3,013 Bytes
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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 |