Instructions to use tokenaii/Horus-Cyber-Nano-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tokenaii/Horus-Cyber-Nano-1.0 with Transformers:

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

pipe = pipeline("image-text-to-text", model="tokenaii/Horus-Cyber-Nano-1.0", trust_remote_code=True)
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 AutoModel
model = AutoModel.from_pretrained("tokenaii/Horus-Cyber-Nano-1.0", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tokenaii/Horus-Cyber-Nano-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tokenaii/Horus-Cyber-Nano-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tokenaii/Horus-Cyber-Nano-1.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/tokenaii/Horus-Cyber-Nano-1.0

SGLang

How to use tokenaii/Horus-Cyber-Nano-1.0 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 "tokenaii/Horus-Cyber-Nano-1.0" \
    --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": "tokenaii/Horus-Cyber-Nano-1.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "tokenaii/Horus-Cyber-Nano-1.0" \
        --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": "tokenaii/Horus-Cyber-Nano-1.0",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use tokenaii/Horus-Cyber-Nano-1.0 with Docker Model Runner:
```
docker model run hf.co/tokenaii/Horus-Cyber-Nano-1.0
```

Horus Cyber Nano 1.0

Official model page: Horus Cyber Nano 1.0

Horus Cyber Nano 1.0 is TokenAI's compact open-weights multimodal cybersecurity model for secure code review, vulnerability analysis, authorized red-team reasoning, terminal workflows, tool-use reasoning, and image-grounded technical understanding.

Overview

Horus Cyber Nano 1.0 is built for practical defensive security work. The model accepts text and images as input and produces text output, making it useful for code review, terminal-centric investigation, screenshot analysis, and long-context technical reasoning.

Developed by TokenAI and founded by Assem Sabry, Horus Cyber Nano 1.0 is part of the Horus model family and is positioned as a specialized model for security-heavy engineering workflows rather than a general-purpose assistant.

Model Links

Main model: tokenaii/Horus-Cyber-Nano-1.0
GGUF repo: tokenaii/Horus-Cyber-Nano-1.0-GGUF
Technical page: tokenai.llc/models/horus-cyber-nano1.0

What It Can Do

1. Secure Code Review

Analyze source code for security weaknesses, explain root causes, map issues to secure engineering patterns, and suggest safer remediation paths.

2. Red Team Task Reasoning

Break down authorized security-testing tasks into structured steps, hypotheses, and evidence-oriented analysis suitable for controlled environments.

3. Terminal and Tool Workflows

Understand command-line tasks, propose useful commands, interpret outputs, and support multi-step technical workflows involving tools and system state.

4. Cybersecurity Vision Understanding

Read screenshots, code images, and terminal captures to extract technical details and convert them into actionable text analysis.

Technical Details

The following specifications are transcribed from the official Horus Cyber Nano 1.0 model page.

Item	Official value
Architecture	Native multimodal vision-language model with a Mixture-of-Experts causal language decoder
Release	September 2026
License	TokenAI Custom License
Parameter count	16B parameters
Tensor types	BF16 and F32
Input and output	Text, images, and video in; text out
Maximum image resolution	3.2 million total pixels per image
Transformer layers	27
Hidden size	2,048
Attention heads	16
KV heads	16
MoE	Yes
Experts per MoE layer	64 routed experts plus 2 shared experts
Expert parallel size	1
Active experts per token	6 routed experts
MoE layer frequency	Every layer
Activation	SiLU
RoPE theta	800,000
Vision encoder layers	27
Vision hidden size	1,152
Vision attention heads	16
Vision patch size	14

Official Benchmarks

Results and comparison values below are taken from the official model page. They are presented as published; evaluation protocols may differ across providers unless a protocol is explicitly stated.

MMLU

Model	Score
Horus Cyber Nano 1.0	82.0%
OpenAI o1	91.8%
GPT-4.5	90.8%
GPT-4.1	90.2%
Grok-2	87.5%
DeepSeek V3.2 Thinking	85.0%

GPQA Diamond

Model	Score
Horus Cyber Nano 1.0	45.2%
Mistral 3.1 24B	45.96%
GPT-4.1	66.3%
gpt-oss-120b	67.1%
DeepSeek-V3.2	82.4%

Terminal-Bench 2.0

Model	Score
Horus Cyber Nano 1.0	46.2%
GPT-5.5	82.7%
GPT-5.4	75.1%
Gemini 3.1 Pro Thinking	68.5%
GPT-5.2 Thinking	62.2%
Claude Sonnet 4.6	59.1%

AIME 2025

Model	Score
Horus Cyber Nano 1.0	76.4%
GPT-5.2 Thinking	100.0%
Claude Sonnet 4.6	95.6%
GPT-5	94.6%
Gemini 3 Flash	95.2%
DeepSeek-R1-0528	87.5%

SWE-Bench Verified

Model	Score
Horus Cyber Nano 1.0	61.7%
Gemini 3.1 Pro Thinking	80.6%
GPT-5.2 Thinking	80.0%
Claude Sonnet 4.6	79.6%
GPT-5	74.9%
Claude Opus 4	72.5%

Multimodal benchmarks

Benchmark	Horus Cyber Nano 1.0	GPT-4o	Qwen2.5-VL-72B	Qwen2.5-VL-32B	Gemma 3 27B
MATH-Vision	56.9%	30.4%	38.1%	38.4%	35.4%
MathVista MINI	80.1%	63.8%	74.8%	74.7%	59.8%
MMBench-EN-v1.1	84.4%	83.1%	88.3%	-	78.9%
MMStar	70.4%	64.7%	70.8%	69.5%	63.1%
VideoMMMU	65.2%	61.2%	60.2%	-	61.8%

GGUF Variants

GGUF variants are available in the companion repository: tokenaii/Horus-Cyber-Nano-1.0-GGUF

Variant	Approx. combined RAM + VRAM	Download
F16	59.71 GiB	GGUF repo
Q8_0	32.18 GiB	GGUF repo
Q6_K	27.24 GiB	GGUF repo
Q5_K_M	23.11 GiB	GGUF repo
Q4_K_M	20.84 GiB	GGUF repo

Note: Combined RAM and VRAM usage was measured approximately for each variant under a fixed local test configuration. Actual requirements may vary with context length, generation length, batching, runtime, and hardware.

Quick Start

Using NeuralNode

import neuralnode as nn

model = nn.Provider.horus(
    model="horus-cyber-nano-1.0",
    device="cuda"
)

agent = nn.Agent(
    role="Security Engineer",
    model=model,
    system_prompt="You analyze code, terminal output, and screenshots for defensive cybersecurity work."
)

result = agent.execute(
    task="Review this authentication flow for insecure patterns and propose a safer patch."
)

print(result)

You will also be able to use Horus Cyber Nano 1.0 through Ollama and LM Studio, and fine-tune it with Unsloth. Direct runtime and fine-tuning links will be added with the public release materials. Microsoft Foundry support is planned.

Intended Use

Secure code review
Vulnerability triage and remediation guidance
Terminal-centric engineering workflows
Tool-augmented reasoning for defensive security analysis
Screenshot and code-image understanding

Safety Note

Horus Cyber Nano 1.0 is intended for authorized security analysis and defensive workflows. Public benchmarking, documentation, and deployment guidance should be interpreted within that scope.

Note on Identity Responses

As with any open-weight model, occasional hallucinations or inconsistent answers about the model's name and identity may occur. This can result from training techniques, decoding settings, and differences among training-data sources; users should verify identity claims against the official model page and release artifacts.

Contact & Community

Citation

@misc{tokenai_horus_cyber_nano_1_0_2026,
  title        = {Horus Cyber Nano 1.0},
  author       = {TokenAI},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/tokenaii/Horus-Cyber-Nano-1.0}},
  note         = {Multimodal MoE model for secure coding, terminal workflows, and cybersecurity reasoning}
}

License

Horus Cyber Nano 1.0 is released under the TokenAI Custom License. Final license text and release usage terms should be consulted alongside the public model release materials.

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Safetensors

Model size

16B params

Tensor type

BF16

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Horus Cyber Nano 1.0

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