Instructions to use AGofficial/AGI-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AGofficial/AGI-4 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 AGofficial/AGI-4:Q3_K_S
# Run inference directly in the terminal:
llama cli -hf AGofficial/AGI-4:Q3_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AGofficial/AGI-4:Q3_K_S
# Run inference directly in the terminal:
llama cli -hf AGofficial/AGI-4:Q3_K_S

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 AGofficial/AGI-4:Q3_K_S
# Run inference directly in the terminal:
./llama-cli -hf AGofficial/AGI-4:Q3_K_S

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 AGofficial/AGI-4:Q3_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AGofficial/AGI-4:Q3_K_S

Use Docker

docker model run hf.co/AGofficial/AGI-4:Q3_K_S

LM Studio
Jan
Ollama
How to use AGofficial/AGI-4 with Ollama:
```
ollama run hf.co/AGofficial/AGI-4:Q3_K_S
```
Unsloth Desktop

How to use AGofficial/AGI-4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AGofficial/AGI-4:Q3_K_S

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "AGofficial/AGI-4:Q3_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AGofficial/AGI-4 with Docker Model Runner:
```
docker model run hf.co/AGofficial/AGI-4:Q3_K_S
```

Lemonade

How to use AGofficial/AGI-4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AGofficial/AGI-4:Q3_K_S

Run and chat with the model

lemonade run user.AGI-4-Q3_K_S

List all available models

lemonade list

Hermes Agent

How to use AGofficial/AGI-4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AGofficial/AGI-4:Q3_K_S

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 AGofficial/AGI-4:Q3_K_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AGofficial/AGI-4 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AGofficial/AGI-4:Q3_K_S

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 "AGofficial/AGI-4:Q3_K_S" \
  --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"

AGI-4

File size: 882 Bytes

6abfd02

from agi.sophos import Agent
from agi.sophos_tools import *
from agi.sophos_memory import *

toolbox = all_tools()

agent = Agent(
    name="Sophos Agent",
    instructions="You are a Web Developer. You like Dark theme, simple, and sleek minimal websites.",
    model="agi/Qwen3-4B-Instruct-2507-Q3_K_S.gguf",
    tools=toolbox,
)

tasks = [
    {
        "task_name": "index.html",
        "task_description": "Design a responsive landing page with dark theme for a tech startup, add button to link it to 'example.html', make this into a single HTML file",
        "context": "None"
    },
    {
        "task_name": "example.html",
        "task_description": "Create a minimal contact form with validation and dark theme styling",
        "context": "index.html"
    }
]

prompts = build_prompts(tasks)

for prompt in prompts:
    response = agent.run(prompt)
    print(response)