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
```

ArmadaOS commited on Mar 27

Commit

4ab9ec2

1 Parent(s): 848993d

Add model card with training details and usage instructions

Browse files

Files changed (1) hide show

README.md +98 -0

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	@@ -0,0 +1,98 @@

+---
+license: apache-2.0
+base_model: Qwen/Qwen3.5-VL-7B
+tags:
+  - armadaos
+  - agent
+  - shadow-verification
+  - fine-tuned
+  - qwen3_5
+  - sft
+  - dpo
+language:
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+---
+# 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](https://github.com/kam-ship-it/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
+```python
+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](https://github.com/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}
+}
+```