Instructions to use BLACK0X80/horus-egy-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BLACK0X80/horus-egy-coder with Transformers:

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

pipe = pipeline("text-generation", model="BLACK0X80/horus-egy-coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BLACK0X80/horus-egy-coder")
model = AutoModelForCausalLM.from_pretrained("BLACK0X80/horus-egy-coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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 BLACK0X80/horus-egy-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BLACK0X80/horus-egy-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BLACK0X80/horus-egy-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BLACK0X80/horus-egy-coder

SGLang

How to use BLACK0X80/horus-egy-coder 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 "BLACK0X80/horus-egy-coder" \
    --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": "BLACK0X80/horus-egy-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "BLACK0X80/horus-egy-coder" \
        --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": "BLACK0X80/horus-egy-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use BLACK0X80/horus-egy-coder with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for BLACK0X80/horus-egy-coder to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for BLACK0X80/horus-egy-coder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BLACK0X80/horus-egy-coder to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="BLACK0X80/horus-egy-coder",
    max_seq_length=2048,
)

Docker Model Runner
How to use BLACK0X80/horus-egy-coder with Docker Model Runner:
```
docker model run hf.co/BLACK0X80/horus-egy-coder
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Horus-Egy-Coder

Egyptian Arabic coding assistant — a fine-tuned Llama 3.1-8B model specialized in helping Arabic and Egyptian developers write, debug, and understand code.

Built in Egypt 🇪🇬 | Powered by Unsloth + TRL

Model Description

horus-egy-coder is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct trained to assist Arabic-speaking developers — with native understanding of Egyptian Arabic technical dialect. It can explain code in Arabic, help debug in conversational Egyptian, and write clean code with Arabic comments.

Property	Value
Base model	meta-llama/Llama-3.1-8B-Instruct
Parameters	1B (quantized BF16)
Training	Unsloth + HuggingFace TRL (2x faster)
License	Apache 2.0
Language	Arabic 🇪🇬 / English
Task	Code generation, explanation, debugging

Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "BLACK0X80/horus-egy-coder"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {
        "role": "system",
        "content": "أنت مساعد برمجة بيتكلم عربي مصري. بتشرح الكود بالعربي وبتساعد المطورين."
    },
    {
        "role": "user", 
        "content": "اكتبلي function بـ Python بتحسب الـ factorial"
    }
]

input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

output = model.generate(
    input_ids,
    max_new_tokens=512,
    temperature=0.7,
    do_sample=True
)

print(tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True))

Example Interactions

Example 1 — Code explanation in Arabic:

User: فسرلي الكود ده
      for i in range(len(arr)-1):
          for j in range(len(arr)-i-1):
              if arr[j] > arr[j+1]:
                  arr[j], arr[j+1] = arr[j+1], arr[j]

Model: ده Bubble Sort — خوارزمية ترتيب بسيطة.
       بتعمل loop على الـ array وبتقارن كل عنصر بالي بعده،
       لو العنصر الأكبر جه الأول بتبدلهم...

Example 2 — Debugging:

User: الكود ده بيطلع IndexError، مش عارف ليه
Model: المشكلة في السطر ده... جرب كذا...

🔢 GGUF Version (للأجهزة المحدودة)

لو جهازك مش عنده GPU كبير، استخدم النسخة المضغوطة:

BLACK0X80/horus-egy-coder-Q4_K_M-GGUF

# تشغيل بـ llama.cpp
./llama-cli -m horus-egy-coder-Q4_K_M.gguf \
  -p "أنت مساعد برمجة عربي مصري." \
  --chat-format llama-3

Training Details

Framework: Unsloth (2x faster training, 60% less memory)
Library: HuggingFace TRL (SFT Trainer)
Base: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
Quantization: BF16

Intended Use

Good for:

شرح الكود بالعربي المصري
كتابة functions وscripts بـ Python
Debugging مع تفسير الأخطاء بالعربي
تعليم البرمجة للمبتدئين العرب

Limitations:

النموذج fine-tuned على بيانات محدودة — ممكن يغلط في كود معقد جداً
مش متخصص في لغات غير Python بنفس الكفاءة
مش مناسب لـ production code بدون مراجعة بشرية

About HORUS-AI

HORUS-AI is an Egyptian AI initiative focused on building Arabic and Egyptian-dialect AI models. Named after the ancient Egyptian god of knowledge, we aim to make AI accessible to Arabic-speaking developers.

GitHub: BLACK0X80
Web: black0x80.vercel.app

Citation

@misc{horus-egy-coder-2025,
  title={Horus-Egy-Coder: Egyptian Arabic Coding Assistant},
  author={BLACK0X80 and HORUS-AI},
  year={2025},
  url={https://huggingface.co/BLACK0X80/horus-egy-coder},
  note={Fine-tuned from meta-llama/Llama-3.1-8B-Instruct}
}

Made with ❤️ in Egypt 🇪🇬

Downloads last month: 88

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for BLACK0X80/horus-egy-coder

Base model

meta-llama/Llama-3.1-8B

Finetuned

(1430)

this model

Quantizations

1 model