Instructions to use tokenaii/Horus-Hiero-Mini-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tokenaii/Horus-Hiero-Mini-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="tokenaii/Horus-Hiero-Mini-4B")
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 AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tokenaii/Horus-Hiero-Mini-4B")
model = AutoModelForCausalLM.from_pretrained("tokenaii/Horus-Hiero-Mini-4B", device_map="auto")
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 = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tokenaii/Horus-Hiero-Mini-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tokenaii/Horus-Hiero-Mini-4B"
# 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-Hiero-Mini-4B",
		"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-Hiero-Mini-4B

SGLang

How to use tokenaii/Horus-Hiero-Mini-4B 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-Hiero-Mini-4B" \
    --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-Hiero-Mini-4B",
		"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-Hiero-Mini-4B" \
        --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-Hiero-Mini-4B",
		"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-Hiero-Mini-4B with Docker Model Runner:
```
docker model run hf.co/tokenaii/Horus-Hiero-Mini-4B
```

Horus-Hiero-Mini-4B / README.md

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metadata

pipeline_tag: image-text-to-text
language:
  - multilingual
library_name: transformers
tags:
  - llm
  - image-text-to-text
  - neuralnode
  - multilingual
  - horus
  - tokenai

Horus Hiero 4B Mini

Horus Hiero 4B Mini is an advanced language model developed by TokenAI, an Egyptian AI startup founded by Assem Sabry. The model specializes in understanding and translating ancient Egyptian Hieroglyphs into various modern human languages. In addition, it is highly multilingual and masters 150 Languages + Hieroglyphic. To ensure accessibility and efficient deployment across different hardware setups, the model is also available in multiple GGUF variants.

Model Overview

Horus Hiero 4B Mini is a 4-billion parameter language model built on top of the robust Qwen/Qwen3.5-4B architecture (or its GGUF equivalent). It is designed to handle extremely long contexts and leverages a sophisticated hybrid attention mechanism to deliver high performance and efficiency.

Leading Model in the Middle East

150 Languages + Hieroglyphic

Example: Hieroglyph Translation

Model Variants

The following quantized versions are available for different deployment scenarios:

Variant	Format	Size	Best For
Full 16-bit	Safetensors	~8.0 GB	Maximum quality, GPU inference
GGUF Repo	GGUF	Various	All quantized variants (Q2_K through Q8_0)
Q8_0	GGUF	~4.4 GB	Minimal quality loss
Q6_K	GGUF	~3.4 GB	Near-full quality
Q4_K_M	GGUF	~2.6 GB	Balanced quality/size, CPU+GPU
Q2_K	GGUF	~1.8 GB	Maximum compression, lower RAM usage

Technical Specifications

Feature	Specification
Number of Parameters	4 Billion
Input Modalities	Text, Image, Video
Context Length	524,288 tokens (512K) natively
Training	Multi-Token Prediction (MTP), strong-to-weak distillation
Thinking Mode	Enabled (toggleable via `enable_thinking` parameter)

Benchmark Results

Benchmark	Score
MMLU-Pro	74.2%
GPQA Diamond	72.3%
HumanEval	68.2%
LiveCodeBench	51.4%
MATH	45.8%

Quick Start

Using NeuralNode (Recommended)
The easiest way to use Horus models is with the NeuralNode framework:

import neuralnode as nn

# For GGUF models (recommended):
MODEL_ID = "tokenaii/Horus-Hiero-Mini-4B-GGUF/Horus-Hiero-Mini-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": "Translate this hieroglyph: 𓂋𓏏𓈖𓀀"}
])
print(response.content)

Create your own code to use Horus Hiero →

About TokenAI & The Horus Family

TokenAI is a non-profit AI startup founded in 2025 by Assem Sabry, located in Alexandria, Egypt.

The Horus family is our line of advanced language models. The series began with Horus 1.0 4B, which achieved remarkable success as the very first language model to be fully trained from scratch in Egypt. Building on that foundation, Horus Hiero brings specialized capabilities in ancient languages while retaining powerful modern multilingual performance.

Contact & Community

Website: tokenai.llc
Hugging Face: tokenaii
GitHub: tokenaii
LinkedIn: TokenAI
Location: Alexandria, Egypt

Citation

If you use Horus Hiero 4B Mini in your research or applications, please cite it as:

@misc{tokenai_horus_hiero_4b_mini,
    title={Horus Hiero 4B Mini: Advanced Multilingual and Hieroglyphs Language Model},
    author={Assem Sabry and TokenAI},
    year={2026},
    url={https://huggingface.co/tokenaii/Horus-Hiero-Mini-4B}
}