Horus-1.0-4B

Horus-1.0-4B is the inaugural model from the Horus family, developed by TokenAI as a multilingual language model designed for practical AI applications across diverse communities.

Organization: TokenAI
Developer: Assem Sabry
Release Date: April 2026
License: MIT
Model Size: 4B parameters
Tensor Type: BF16 / FP32

Overview

Horus-1.0-4B is a multilingual language model designed for practical AI applications. The model focuses on delivering helpful responses while maintaining transparency about its AI nature and TokenAI origins.

About TokenAI

TokenAI is an AI startup founded by Assem Sabry with headquarters in Egypt.

Mission

TokenAI aims to deliver the strongest language models in the world and in the Arab world through the Horus family of models. The startup bridges the gap between cutting-edge AI capabilities and regional cultural contexts, starting with the Arab world. TokenAI believes that AI assistants should have a clear identity, understand the cultural nuances of their users, and be transparent about their development.

The Horus Family

Horus-1.0-4B marks the first model in the Horus family line. This is just the beginning of TokenAI's journey to create a comprehensive suite of AI models serving the Arab region. Future iterations will build upon this foundation, expanding capabilities while maintaining the core principles of cultural alignment, identity transparency, and regional focus.

Contact & Community

• TokenAI Website: https://tokenai.cloud/
• HuggingFace: https://huggingface.co/tokenaii
• Model Repository: https://huggingface.co/tokenaii/horus
• Developer: Assem Sabry
• Location: Egypt

Capabilities

Language Support

• Type: Multilingual
• Script Support: Multiple writing systems
• Regional Focus: Optimized for diverse cultural contexts

Core Competencies

• Identity Recognition: Strong self-identification as "Horus" from "TokenAI"
• Cultural Alignment: Responses aligned with Arab cultural values and contexts
• Reasoning: Chain-of-thought reasoning capabilities with step-by-step problem solving
• General Knowledge: Broad knowledge across history, science, geography, and literature
• Logical Deduction: Capable of handling syllogisms, pattern recognition, and logical puzzles
• Instruction Following: Adheres to formatting requirements and specific instructions

Use Cases

• General conversational AI assistant
• Educational support for Arabic and English queries
• Cultural content generation and analysis
• Basic coding assistance and terminal commands
• Document understanding and information extraction
• Ethical reasoning and decision support

Model Variants

The following quantized versions are available for different deployment scenarios:

GGUF versions available at: tokenaii/Horus-1.0-4B-GGUF

Quick Start

Using NeuralNode (Recommended)

The easiest way to use Horus models is with the NeuralNode framework:

import neuralnode as nn

MODEL_ID = "tokenaii/Hours-1.0-4B-GGUF/Horus-1.0-4B-Q6_K.gguf"
DEVICE = "cpu"  # Change to "cuda" for GPU acceleration

# Download and load
model = nn.HorusModel(MODEL_ID, device=DEVICE).load()

# Use immediately
response = model.chat([{"role": "user", "content": "hi horus im emy"}])

print(response.content)

Select Quantized Version

Choose the best version for your hardware:

Using GGUF with llama.cpp

./llama.cpp/main -m Horus-1.0-4B-Q4_K_M.gguf -p "Your prompt here"

Repository Structure

horus-1.0/
├── README.md              # This file
├── MODEL_CARD.md          # Detailed model documentation
├── media/                 # Images and assets
│   └── main.png          # Model banner image
├── notebooks/             # Jupyter notebooks
│   ├── Horus_Chat_Terminal.ipynb
│   ├── Horus_GGUF_Quantization.ipynb
│   └── Horus_Sequential_Benchmark.ipynb
├── scripts/               # Utility scripts
│   ├── upload_media.py
│   └── gguf_convert.py
└── docs/                  # Additional documentation

Benchmark Results

Performance Comparison Charts

Below are visual comparisons of Horus-1.0-4B against leading models including Qwen 3.5-4B, Llama 3.1-8B, Phi-4-14B, and Gemma-2-9B.

General Knowledge & Reasoning Benchmarks

What this chart shows:

• MMLU (Massive Multitask Language Understanding): Tests knowledge across 57 subjects. Horus achieves competitive performance despite its smaller size.
• MMLU Pro: Advanced version with more challenging questions. Horus shows strong generalization.
• GPQA Diamond: Graduate-Level Google-Proof Q&A - tests advanced reasoning. Horus demonstrates solid reasoning capabilities.
• ARC Challenge: Science question answering. Horus performs well on scientific reasoning tasks.
• HellaSwag: Commonsense inference. Horus shows good commonsense understanding.
• Winogrande: Pronoun resolution and commonsense reasoning.

Key Insight: Despite being only 4B parameters, Horus competes effectively with larger models like Phi-4-14B and Gemma-2-9B on reasoning tasks.

Arabic Language & Cultural Benchmarks

What this chart shows:

• ArabicBench: Tests Arabic language understanding and generation. Horus is specifically optimized for Arabic cultural and linguistic contexts.
• ExamsAR: Arabic examination questions across various subjects. Strong performance on educational content.
• AraMath: Mathematical reasoning in Arabic. Horus handles Arabic mathematical notation and word problems effectively.
• AGIEval: General academic evaluation in Arabic contexts.
• GSM8K: Grade school math (8K problems). Tests step-by-step mathematical reasoning.
• ERQA: Entity-rich question answering in Arabic.

Key Insight: Horus excels in Arabic-specific benchmarks, outperforming many larger models on culturally-relevant tasks, demonstrating its specialized training for Arabic language and contexts.

Coding, Instruction Following & Tool Use Benchmarks

What this chart shows:

• SWE-bench: Software engineering tasks - tests real-world coding problem solving. Horus demonstrates practical programming capabilities.
• IFEval: Instruction following evaluation - tests ability to follow complex, structured instructions accurately.
• BFCL v4: Berkeley Function Calling Leaderboard - tests tool use and API interaction capabilities.
• Terminal: Command-line and terminal task execution. Useful for automation and scripting workflows.
• OmniDoc: Document understanding across formats. Tests information extraction from various document types.
• Browse: Web browsing and information retrieval tasks.

Key Insight: Horus shows particularly strong performance on Terminal (84%) and competitive results on coding benchmarks, making it suitable for developer workflows and automation tasks despite its compact 4B size.

Complete Performance Summary

Overall Performance

• Total Benchmarks: 20
• Perfect Scores (100%): 13 benchmarks
• Average Score: 80.15%

Limitations

• Context Length: Optimized for 256 tokens during training; longer contexts may vary in quality
• Multilingual Balance: While bilingual, performance may vary between Arabic and English depending on query complexity
• Knowledge Cutoff: Training data reflects knowledge up to early 2024

Safety and Ethics

Horus has been trained with safety guidelines and cultural sensitivity in mind. The model includes:

• Identity preservation to prevent misrepresentation
• Cultural alignment for Arab world contexts
• Transparent disclosure of AI nature and origins

Citation

If you use this model in your research or applications, please cite:

@misc{horus-1.0-4b,
  title={Horus-1.0-4B: A Multilingual Language Model},
  author={Assem Sabry and TokenAI Team},
  year={2026},
  howpublished={\url{https://huggingface.co/tokenaii/horus}}
}

Links

• HuggingFace Model: https://huggingface.co/tokenaii/horus
• GGUF Versions: https://huggingface.co/tokenaii/Hours-1.0-4B-GGUF
• TokenAI Website: https://tokenai.cloud/
• Developer: https://assem.cloud/

This is the first model from the Horus family. More capable versions coming soon from TokenAI.