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
```

assemsabry commited on Jul 6

Commit

eeb5f5a

verified ·

1 Parent(s): e5be06c

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README.md +103 -33

README.md CHANGED Viewed

@@ -1,33 +1,103 @@
----
-tags:
-- hieroglyphics
-- ancient-egyptian
-- tokenai
-- translation
----
-# Horus-Hiero
-Horus-Hiero is a specialized AI model for ancient Egyptian hieroglyphic translation, built on Qwen3.5 architecture.
-## Usage
-Use the following plain text format:
-```
-User: <your prompt>
-Assistant:
-```
-For specialized behavior, use a system prompt:
-```
-System: You are Horus-Hiero, a specialized AI for translating ancient Egyptian hieroglyphics from TokenAI.
-User: <your prompt>
-Assistant:
-```
-## Links
-- **GGUF Quantized:** [tokenaii/Horus-Hiero-9B-GGUF](https://huggingface.co/tokenaii/Horus-Hiero-9B-GGUF) | [tokenaii/Horus-Hiero-Mini-4B-GGUF](https://huggingface.co/tokenaii/Horus-Hiero-Mini-4B-GGUF)
-- **Organization:** [tokenaii](https://huggingface.co/tokenaii)

+---
+pipeline_tag: image-text-to-text
+language:
+- multilingual
+library_name: transformers
+tags:
+- llm
+- image-text-to-text
+- neuralnode
+- multilingual
+- horus
+- tokenai
+---
+<div align="center">
+  <h1><b style="font-size: 1.5em;">Horus Hiero 4B Mini</b></h1>
+</div>
+<p align="center">
+  <img src="media/Horus%20Hiero.png" alt="Horus Hiero 4B Mini" width="600"/>
+</p>
+<p align="center">
+  <b>Horus Hiero 4B Mini</b> is an advanced language model developed by <b>TokenAI</b>, 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 <b>201 languages</b>. To ensure accessibility and efficient deployment across different hardware setups, the model will also be available in multiple GGUF variants.
+</p>
+<br>
+## Model Overview
+**Horus Hiero 4B Mini** is a 4-billion parameter language model built on top of the robust [Qwen/Qwen3.5-4B](https://huggingface.co/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.
+## Model Variants
+The following quantized versions are available for different deployment scenarios:
+| Variant | Format | Size | Best For |
+| :--- | :--- | :--- | :--- |
+| **[Full 16-bit](https://huggingface.co/tokenaii/Horus-Hiero-Mini-4B)** | Safetensors | ~8.0 GB | Maximum quality, GPU inference |
+| **[Q8_0](https://huggingface.co/tokenaii/Horus-Hiero-Mini-4B-GGUF/blob/main/Horus-Hiero-Mini-4B-Q8_0.gguf)** | GGUF | ~4.4 GB | Minimal quality loss |
+| **[Q6_K](https://huggingface.co/tokenaii/Horus-Hiero-Mini-4B-GGUF/blob/main/Horus-Hiero-Mini-4B-Q6_K.gguf)** | GGUF | ~3.4 GB | Near-full quality |
+| **[Q4_K_M](https://huggingface.co/tokenaii/Horus-Hiero-Mini-4B-GGUF/blob/main/Horus-Hiero-Mini-4B-Q4_K_M.gguf)** | GGUF | ~2.6 GB | Balanced quality/size, CPU+GPU |
+| **[Q2_K](https://huggingface.co/tokenaii/Horus-Hiero-Mini-4B-GGUF/blob/main/Horus-Hiero-Mini-4B-Q2_K.gguf)** | 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** | 262,144 tokens natively (Extensible up to 1,010,000 tokens) |
+| **Training** | Multi-Token Prediction (MTP), strong-to-weak distillation |
+| **Thinking Mode** | Enabled (toggleable via `enable_thinking` parameter) |
+## Benchmark Comparison
+Performance comparison between **Horus Hiero 9B** and **Horus Hiero 4B Mini** across various rigorous benchmarks:
+| Benchmark | Horus Hiero 9B | Horus Hiero 4B Mini |
+| :--- | :---: | :---: |
+| **MMLU-Pro** | **82.5** | 79.1 |
+| **GPQA Diamond** | **81.7** | 76.2 |
+| **IFEval** | **91.5** | 89.8 |
+| **LongBench v2** | **55.2** | 50.0 |
+| **LiveCodeBench v6** | **65.6** | 55.8 |
+| **MMMU** | **78.4** | 77.6 |
+| **MathVista (mini)** | **85.7** | 85.1 |
+| **C-Eval** | **88.2** | 85.1 |
+| **MMMU-Pro** | **70.1** | 66.3 |
+| **AI2D_TEST** | **90.2** | 89.6 |
+## Quick Start
+**Using NeuralNode (Recommended)**
+The easiest way to use Horus models is with the NeuralNode framework:
+```python
+import neuralnode as nn
+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": "hi horus im emy"}])
+print(response.content)
+```
+## 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.cloud](https://tokenai.cloud/)
+- **Hugging Face:** [tokenaii](https://huggingface.co/tokenaii)
+- **GitHub:** [tokenaii](https://github.com/tokenaii)
+- **LinkedIn:** [TokenAI](https://www.linkedin.com/company/tokenaii)
+- **Location:** Alexandria, Egypt
+## Citation
+If you use Horus Hiero 4B Mini in your research or applications, please cite it as:
+```bibtex
+@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}
+}
+```