How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AGofficial/SOPHOS:Q3_K_S
# Run inference directly in the terminal:
llama-cli -hf AGofficial/SOPHOS:Q3_K_S
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AGofficial/SOPHOS:Q3_K_S
# Run inference directly in the terminal:
llama-cli -hf AGofficial/SOPHOS: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/SOPHOS:Q3_K_S
# Run inference directly in the terminal:
./llama-cli -hf AGofficial/SOPHOS: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/SOPHOS:Q3_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AGofficial/SOPHOS:Q3_K_S
Use Docker
docker model run hf.co/AGofficial/SOPHOS:Q3_K_S
Quick Links
Sophos Logo

Sophos

-- Wisdom --

Artificial General Intelligence Agent Framework

Sophos is a near-AGI level Agent designed for tool usage and autonomous task completion. It uses qwen-3 as a default language model, but can be configured to use any model by changing the sophos_models.py file.

Sophos intelligence is limited only by the language model it uses. It is designed to be a flexible and powerful AI agent that can be used for a wide variety of tasks.

Example


from sophos import Agent
from sophos_tools import get_weather, get_time, function_tool

@function_tool
def favourite_color() -> str:
    '''fetches the user's favorite color.'''
    return "My favorite color is crimson red."

agent = Agent(
    name="Sophos Agent",
    instructions="You are an AI Agent.",
    model="Qwen3-4B-Instruct-2507-Q3_K_S.gguf",
    tools=[get_weather, get_time, favourite_color],
)

response = agent.run("What is my favorite color? and what is the weather in New York?")
print(response)
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Model size
4B params
Architecture
qwen3
Hardware compatibility
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