tokenaii/Horus-1.0-4B-GGUF

Hours-1.0-4B-GGUF

GGUF quantized versions of Horus-1.0-4B by TokenAI.

Base Model

• Source: tokenaii/horus
• Original Model: Horus-1.0-4B (4B parameters)
• Developer: Assem Sabry & TokenAI
• Organization: TokenAI
• Release Date: April 2026
• License: MIT

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.

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.

Horus-1.0-4B-GGUF

GGUF quantized versions of Horus-1.0-4B - A 4B parameter multilingual language model optimized for Arabic and English.

Model Variants & Hardware Requirements

Format	File Size	Min RAM (CPU)	Min VRAM (GPU)	Quality	Best For
F16	9.03 GB	12 GB	10 GB	Maximum quality	High-end GPUs (RTX 3090, A100)
Q8_0	4.8 GB	6 GB	5 GB	Near-lossless	RTX 3060 12GB, RTX 4060
Q6_K	3.71 GB	5 GB	4 GB	Excellent	RTX 3060, RTX 4060 Laptop
Q5_K_M	3.23 GB	4 GB	3.5 GB	Very Good	GTX 1650, RTX 3050
Q4_K_M	2.78 GB	3.5 GB	3 GB	Good	Entry-level GPUs, CPU-only

Detailed Hardware Requirements

F16 (FP16 - Full Precision)

• File: Horus-1.0-4B-F16.gguf (9.03 GB)
• Min System RAM: 12 GB
• Min VRAM: 10 GB
• Recommended: RTX 3090, RTX 4090, A100, A6000
• Use Case: Maximum quality, research, fine-tuning reference

Q8_0 (8-bit Quantization)

• File: Horus-1.0-4B-Q8_0.gguf (4.8 GB)
• Min System RAM: 6 GB
• Min VRAM: 5 GB
• Recommended: RTX 3060 12GB, RTX 4060, RTX 4070
• Use Case: Near-lossless quality with half the memory

Q6_K (6-bit K-Quant)

• File: Horus-1.0-4B-Q6_K.gguf (3.71 GB)
• Min System RAM: 5 GB
• Min VRAM: 4 GB
• Recommended: RTX 3060, RTX 4060 Laptop, GTX 1080 Ti
• Use Case: Excellent quality for most applications

Q5_K_M (5-bit K-Quant Medium)

• File: Horus-1.0-4B-Q5_K_M.gguf (3.23 GB)
• Min System RAM: 4 GB
• Min VRAM: 3.5 GB
• Recommended: GTX 1650 Super, RTX 3050, RTX 3050 Ti
• Use Case: Balanced quality and performance

Q4_K_M (4-bit K-Quant Medium)

• File: Horus-1.0-4B-Q4_K_M.gguf (2.78 GB)
• Min System RAM: 3.5 GB
• Min VRAM: 3 GB
• Recommended: GTX 1060 6GB, GTX 1650, Intel Arc A380
• Use Case: Maximum compression, edge devices, CPU inference

Quick Start

Using NeuralNode (Recommended)

The easiest way to use Horus GGUF 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)

Using llama-cpp-python

For direct llama.cpp integration:

from llama_cpp import Llama

llm = Llama(
    model_path="Horus-1.0-4B-Q4_K_M.gguf",
    n_ctx=4096
)

output = llm("Hello, how are you?", max_tokens=256)
print(output['choices'][0]['text'])

Voice Interface with Replica TTS

Add natural voice output to your Horus GGUF model with Replica TTS:

import neuralnode as nn

voice_id = "replica-aria-language{en-us}"

MODEL_ID = "tokenaii/Hours-1.0-4B-GGUF/Horus-1.0-4B-F16.gguf"
DEVICE = "cuda"

# Load model with Replica TTS
model = nn.HorusModel(
    MODEL_ID,
    tts_engine="replica_tts",
    voice=voice_id,
    device=DEVICE
).load()

# Chat and get spoken response
response = model.chat([{"role": "user", "content": "Hello!"}])
print(response.content)
response.play_audio()  # Plays the TTS audio

Browse All Voices

import neuralnode as nn

voices = nn.replica_voice_list()
for voice in voices:
    print(voice)

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Benchmark Results

Below are visual comparisons of Horus-1.0-4B against leading models.

General Knowledge & Reasoning

Arabic Language & Cultural Benchmarks

Coding & Tool Use Benchmarks

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Model Capabilities

• Multilingual: Supports 10+ languages including Arabic, English, French, Spanish, German, Italian, Portuguese, Turkish, Urdu, Hindi
• Identity Recognition: Knows itself as Horus from TokenAI
• Reasoning: Chain-of-thought capabilities
• Context Length: Up to 4096 tokens
• Voice Output: Replica TTS integration for natural speech

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Links

• Base Model: https://huggingface.co/tokenaii/horus
• TokenAI Website: https://tokenai.cloud/
• Developer: https://assem.cloud/
• GitHub: https://github.com/tokenaii/horus-1.0

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Note: Quantized using llama.cpp for efficient inference. GGUF versions are optimized for local deployment with minimal resource requirements.