Instructions to use Vionex-digital/Hakeem-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vionex-digital/Hakeem-7B with Transformers:

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

pipe = pipeline("text-generation", model="Vionex-digital/Hakeem-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vionex-digital/Hakeem-7B")
model = AutoModelForCausalLM.from_pretrained("Vionex-digital/Hakeem-7B", device_map="auto")
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 Vionex-digital/Hakeem-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vionex-digital/Hakeem-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vionex-digital/Hakeem-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vionex-digital/Hakeem-7B

SGLang

How to use Vionex-digital/Hakeem-7B 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 "Vionex-digital/Hakeem-7B" \
    --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": "Vionex-digital/Hakeem-7B",
		"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 "Vionex-digital/Hakeem-7B" \
        --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": "Vionex-digital/Hakeem-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vionex-digital/Hakeem-7B with Docker Model Runner:
```
docker model run hf.co/Vionex-digital/Hakeem-7B
```

this will be used for clinical, diagnostic, triage, or treatment decisions

by dummy9996 - opened Jun 9

Discussion

dummy9996

Jun 9

Yousef9Zaghloul

Vionex Digital Solutions org Jun 9

•

edited Jun 9

Hi @dummy9996 👋 — thanks for reaching out, and welcome to the Hakeem-7B community!

It looks like your message came through without any details (the body was empty), so please feel free to share your question and we'll be glad to help.

Regarding the point in your title, to be clear about intended use:

Hakeem-7B is a research and educational model for Arabic medical reasoning. It is not a medical device and must not be relied upon to make real clinical, diagnostic, triage, or treatment decisions, and it is not a substitute for a qualified healthcare professional. Any clinical-facing use must keep a licensed clinician in the loop and undergo appropriate validation, safety testing, and regulatory review for that specific setting.

The intended use and limitations are documented on the model card. We're glad to answer anything about the model's capabilities, benchmarks, or how to run it.

— The Vionex team

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