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

Libraries

How to use ArmadaOS/AOS-Shadow-v1.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ArmadaOS/AOS-Shadow-v1.0")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("ArmadaOS/AOS-Shadow-v1.0")
model = AutoModelForImageTextToText.from_pretrained("ArmadaOS/AOS-Shadow-v1.0")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ArmadaOS/AOS-Shadow-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArmadaOS/AOS-Shadow-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArmadaOS/AOS-Shadow-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ArmadaOS/AOS-Shadow-v1.0

SGLang

How to use ArmadaOS/AOS-Shadow-v1.0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ArmadaOS/AOS-Shadow-v1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArmadaOS/AOS-Shadow-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ArmadaOS/AOS-Shadow-v1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArmadaOS/AOS-Shadow-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ArmadaOS/AOS-Shadow-v1.0 with Docker Model Runner:
```
docker model run hf.co/ArmadaOS/AOS-Shadow-v1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

AOS-Shadow-v1.0

ArmadaOS Shadow Verification Agent — First Custom Model

AOS-Shadow-v1.0 is a specialized AI agent model fine-tuned from Qwen 3.5 9B for the Shadow Verification role within the ArmadaOS agent operating system. This is the first custom model ever trained by ArmadaOS, serving as a proof-of-concept for the strategy of training specialized agent models from open-source base models.

Model Description

The Shadow agent is ArmadaOS's independent verification and quality assurance system. It operates as a multi-model red-team pipeline that stress-tests work before delivery, running independent assessor, cross-reference, and adversarial red-team roles.

Key Capabilities

Independent Verification: Reviews and validates outputs from other agents
Red-Team Analysis: Identifies weaknesses, gaps, and failure modes in plans and deliverables
Cross-Reference Checking: Validates claims against multiple sources
Quality Scoring: Provides structured assessment with confidence levels (high/medium/low)
Engine-Native Tool Use: Uses ArmadaOS Engine tools ([tool: shell], [tool: read], [tool: memory_store], etc.)

Training Details

Parameter	Value
Base Model	Qwen 3.5 9B (Vision-Language)
Training Method	SFT + DPO (Supervised Fine-Tuning → Direct Preference Optimization)
Framework	Unsloth + TRL with bf16 LoRA
Trainable Parameters	0.31% (LoRA)
SFT Examples	226 trajectory examples
DPO Pairs	130 preference pairs
SFT Final Loss	1.6885
DPO Final Loss	0.4744
Training Time	~18 minutes on RTX A6000 48GB
Output Format	16-bit safetensors (4 shards, ~18GB)
Training Data Version	v4.1 Gold Standard (100% validated)

Training Data

Training data was generated using the AOSP-315 v4.1 Teacher Agent Directive, a comprehensive specification for producing Engine-native training examples. All 226 SFT examples and 130 DPO pairs were validated through multiple Shadow v1.2 verification rounds:

100% JSON valid
0% Manus contamination (pure ArmadaOS Engine-native format)
Uses <think> blocks for reasoning
ChatML wrapping via pipeline
70/15/15 split: identity reinforcement / no-prompt / mixed

Intended Use

This model is designed to run within the ArmadaOS Engine as the Shadow Verification agent. It is a proof-of-concept demonstrating that specialized agent-level models can be trained from open-source base models for specific organizational roles.

Limitations

This is a v1.0 proof-of-concept; production deployment requires further evaluation
Trained on a relatively small dataset (226 SFT + 130 DPO examples)
Optimized specifically for the ArmadaOS Engine tool format and may not generalize to other agent frameworks
Vision capabilities from the base model have not been specifically fine-tuned

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ArmadaOS/AOS-Shadow-v1.0")
tokenizer = AutoTokenizer.from_pretrained("ArmadaOS/AOS-Shadow-v1.0")

About ArmadaOS

ArmadaOS is an agent operating system that orchestrates specialized AI agents for organizational operations. Each agent has a distinct role (Chief of Staff, Shadow, Oracle, etc.) and operates within the ArmadaOS Engine using a defined set of tools, memory systems, and delegation protocols.

GitHub: kam-ship-it/ArmadaOS
Organization: ArmadaOS

Citation

@misc{armadaos-shadow-v1,
  title={AOS-Shadow-v1.0: ArmadaOS Shadow Verification Agent},
  author={ArmadaOS},
  year={2026},
  url={https://huggingface.co/ArmadaOS/AOS-Shadow-v1.0}
}

Downloads last month: 23

Safetensors

Model size

10B params

Tensor type

BF16

F32

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

Quantizations

2 models