Instructions to use BinSaqban/Hayula-Rushd-Q-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BinSaqban/Hayula-Rushd-Q-v1 with Transformers:

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

pipe = pipeline("text-generation", model="BinSaqban/Hayula-Rushd-Q-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("BinSaqban/Hayula-Rushd-Q-v1", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BinSaqban/Hayula-Rushd-Q-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/BinSaqban/Hayula-Rushd-Q-v1

SGLang

How to use BinSaqban/Hayula-Rushd-Q-v1 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 "BinSaqban/Hayula-Rushd-Q-v1" \
    --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": "BinSaqban/Hayula-Rushd-Q-v1",
		"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 "BinSaqban/Hayula-Rushd-Q-v1" \
        --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": "BinSaqban/Hayula-Rushd-Q-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BinSaqban/Hayula-Rushd-Q-v1 with Docker Model Runner:
```
docker model run hf.co/BinSaqban/Hayula-Rushd-Q-v1
```

BinSaqban/Hayula-Rushd-Q-v1

Hayula Rushd Q v1 - Arabic reasoning model based on Qwen2.5-7B

License

This model is licensed under CC-BY-4.0 (Creative Commons Attribution 4.0 International).

You must attribute the original creator when using, sharing, or adapting this model:

"Based on BinSaqban/Hayula-Rushd-Q-v1 by Hayula Lab (https://huggingface.co/BinSaqban)"

Citation

@misc{binsaqban-hayula_rushd_q_v1,
  author = {Hayula Lab},
  title = {BinSaqban/Hayula-Rushd-Q-v1},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/BinSaqban/Hayula-Rushd-Q-v1}
}

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Safetensors

Model size

1B params

Tensor type

BF16

U32