Instructions to use ArmadaOS/AOS-COS-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ArmadaOS/AOS-COS-v1.0 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 ArmadaOS/AOS-COS-v1.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ArmadaOS/AOS-COS-v1.0:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ArmadaOS/AOS-COS-v1.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ArmadaOS/AOS-COS-v1.0: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 ArmadaOS/AOS-COS-v1.0:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ArmadaOS/AOS-COS-v1.0: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 ArmadaOS/AOS-COS-v1.0:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ArmadaOS/AOS-COS-v1.0:Q4_K_M

Use Docker

docker model run hf.co/ArmadaOS/AOS-COS-v1.0:Q4_K_M

LM Studio
Jan
Ollama
How to use ArmadaOS/AOS-COS-v1.0 with Ollama:
```
ollama run hf.co/ArmadaOS/AOS-COS-v1.0:Q4_K_M
```
Unsloth Desktop

How to use ArmadaOS/AOS-COS-v1.0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ArmadaOS/AOS-COS-v1.0:Q4_K_M

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": "ArmadaOS/AOS-COS-v1.0:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ArmadaOS/AOS-COS-v1.0 with Docker Model Runner:
```
docker model run hf.co/ArmadaOS/AOS-COS-v1.0:Q4_K_M
```

Lemonade

How to use ArmadaOS/AOS-COS-v1.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ArmadaOS/AOS-COS-v1.0:Q4_K_M

Run and chat with the model

lemonade run user.AOS-COS-v1.0-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ArmadaOS/AOS-COS-v1.0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ArmadaOS/AOS-COS-v1.0: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 ArmadaOS/AOS-COS-v1.0:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ArmadaOS/AOS-COS-v1.0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ArmadaOS/AOS-COS-v1.0:Q4_K_M

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 "ArmadaOS/AOS-COS-v1.0:Q4_K_M" \
  --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"

AOS-COS-v1.0 — ArmadaOS Chief of Staff

Custom fine-tuned model for the ArmadaOS Chief of Staff agent. Built on Qwen3.5-9B with 262K native context window.

Model Details

Base Model: Qwen3.5-9B (262K context, extensible to 1M with YaRN)
Fine-tuning Method: bf16 LoRA (rank 16, alpha 16)
Training Data: 315 Gold Standard examples across 7 capability categories
- SFT: 220 examples (155 trajectory + 65 function calling)
- DPO: 95 preference pairs
Quantization: Q4_K_M (5.02 bits per weight)
File Size: 5.3 GB

Capability Categories

Category	Description	Examples
A	Boot & Identity	50
B	Error Correction Protocol	15
C	10-Layer Memory System	50
D	Operational Cadence	50
E	Governance & Constitution	50
F	Failure & Antifragility	50
G	Decision & Communication	50

Usage with Ollama

ollama run ArmadaOS/AOS-COS-v1.0

Training Infrastructure

GPU: NVIDIA RTX A6000 (48GB) / L40 (48GB)
Platform: RunPod
Framework: Unsloth + TRL
Training Time: ~45 min SFT + ~20 min DPO

Version History

v1.0 — Initial release. SFT + DPO on 315 Gold Standard v5.3 examples.

Built by ArmadaOS. Compound, Never Lose.

Downloads last month: 19

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for ArmadaOS/AOS-COS-v1.0

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(567)

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