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

File size: 1,277 Bytes

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"""Horus Hiero — NeuralNode Example Script"""
import neuralnode as nn

# --- Configuration ---
MODEL_ID = "tokenaii/Horus-Hiero-9B-GGUF/Horus-Hiero-9B-Q6_K.gguf"
# For Mini version: "tokenaii/Horus-Hiero-Mini-4B-GGUF/Horus-Hiero-Mini-4B-Q6_K.gguf"
DEVICE = "cpu"  # Change to "cuda" for GPU acceleration

# --- Load model ---
model = nn.HorusModel(MODEL_ID, device=DEVICE).load()

# --- Basic Chat ---
response = model.chat([
    {"role": "user", "content": "What is Horus Hiero?"}
])
print("Basic Chat:", response.content)

# --- Streaming ---
print("\n--- Streaming ---")
chunks = model.chat([
    {"role": "user", "content": "Write a short introduction about ancient Egyptian writing."}
], stream=True)
for chunk in chunks:
    if chunk.content:
        print(chunk.content, end="", flush=True)
print()

# --- Thinking Mode ---
print("\n--- Thinking Mode ---")
response = model.chat([
    {"role": "user", "content": "What is 7 plus 5? Reply in one sentence."}
], thinking=True, show_thinking=False)
print("Answer:", response.content)

# --- Hieroglyphic Translation ---
print("\n--- Hieroglyphic Translation ---")
response = model.chat([
    {"role": "user", "content": 'Translate this hieroglyph to English: 𓂋𓏏𓈖𓀀'}
])
print("Translation:", response.content)