Instructions to use AliAbdelrasheed/maqa_llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AliAbdelrasheed/maqa_llama with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("AliAbdelrasheed/maqa_llama")
model = AutoModelForMultimodalLM.from_pretrained("AliAbdelrasheed/maqa_llama")
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 AliAbdelrasheed/maqa_llama with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AliAbdelrasheed/maqa_llama

SGLang

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

Unsloth Studio

How to use AliAbdelrasheed/maqa_llama 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 AliAbdelrasheed/maqa_llama 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 AliAbdelrasheed/maqa_llama to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AliAbdelrasheed/maqa_llama to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="AliAbdelrasheed/maqa_llama",
    max_seq_length=2048,
)

Docker Model Runner
How to use AliAbdelrasheed/maqa_llama with Docker Model Runner:
```
docker model run hf.co/AliAbdelrasheed/maqa_llama
```

MAQA-LLaMA — Arabic Medical Question Answering (Base Model)

⚠️ Disclaimer: This model is intended for research and informational purposes only. It is not a substitute for professional medical advice, diagnosis, or treatment. It cannot and should not be used to prescribe or recommend medications.

Model Summary

maqa_llama is a Meta Llama 3 8B Instruct model fine-tuned on the MAQA dataset (Medical Arabic Questions & Answers) — 430,000 real doctor-patient interactions across 20 medical specialisations, sourced from Arabic medical platforms.

This is the full-precision base model (BF16 / 16-bit), best suited for research and further fine-tuning. For deployment on consumer hardware, see the quantised variants below.

Property	Value
Base model	`unsloth/llama-3-8b-Instruct-bnb-4bit` (Meta Llama 3 8B Instruct)
Fine-tuning method	QLoRA (via Unsloth)
Precision	BF16 (merged 16-bit)
Model size	8B parameters
Language	Arabic 🇸🇦
License	Apache 2.0
Developed by	Ali Abdelrasheed

Model Family

Model	Format	Size	Best for
`maqa_llama` ← this model	BF16 SafeTensors	~16 GB	Research / further fine-tuning
`maqa_llama_4bit`	4-bit (bitsandbytes)	~5 GB	GPU inference
`maqa_llama_4bit_GGUF`	GGUF q4_k_m	4.92 GB	CPU / local deployment

Dataset — MAQA

The model was trained on MAQA (Medical Arabic Questions & Answers), the largest Arabic medical Q&A dataset available for NLP research.

Property	Value
Total records	430,000 question-answer pairs
Columns	`question` (patient) · `answer` (doctor diagnosis + treatment notes)
Medical specialisations	20 (cardiology, dermatology, neurology, gastroenterology, paediatrics, and more)
Sources	altibbi.com · tbeeb.net · cura.healthcare
Language	Arabic (Modern Standard Arabic)
Quality	All questions unique and cleaned (not stemmed)
Training split	70% train / 30% evaluation

Dataset reference: "Deep learning for Arabic healthcare: MedicalBot" — Social Network Analysis and Mining, Springer (2023) Available on Harvard Dataverse

Training Details

LoRA Configuration

Hyperparameter	Value
LoRA rank (r)	16
LoRA alpha	32
LoRA dropout	0 (optimised)
Bias	none (optimised)
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Gradient checkpointing	unsloth (30% less VRAM)
Random state	3407

Training Arguments

Hyperparameter	Value
Epochs	1
Batch size (per device)	52
Gradient accumulation steps	1
Learning rate	2e-4
LR scheduler	Linear
Warmup steps	200
Optimiser	AdamW 8-bit
Max sequence length	2048
Evaluation strategy	Every 500 steps
Training environment	Google Colab Pro
Framework	Unsloth + HuggingFace TRL (SFTTrainer)

Chat Template & System Prompt

The model was fine-tuned using the Llama-3 chat template with a custom Arabic medical system prompt:

أنت طبيب محترف ولديك خبرة في كل مجالات الطب.
يجيب على أسئلة المرضى حول الأمراض، باستخدام لهجة رسمية وودية،
وإجابات موجزة ومفيدة يسهل على الجميع فهمها.

"You are a professional doctor with expertise in all fields of medicine. Answer patients' questions about diseases using a formal and friendly tone, with concise and helpful answers that everyone can understand."

Special tokens <|question|> and <|answer|> were added to the tokenizer to clearly demarcate patient input and doctor response during training.

Training Journey

The development of this model went through multiple iterations:

RAG approach (initial attempt): Explored Retrieval-Augmented Generation using LangChain and FAISS as a vector store. While technically functional, this approach had limitations in generalisation and was not suitable for a fine-tuned conversational model.
Manual LoRA fine-tuning: Moved to direct supervised fine-tuning using LoRA for full control over the model weights on the MAQA dataset.
Unsloth optimisation (final): Adopted the Unsloth framework to maximise training efficiency on Colab Pro's GPU resources — achieving 2x faster training with significantly reduced VRAM usage. This produced the current published models.

Quick Start

from unsloth import FastLanguageModel
from unsloth.chat_templates import get_chat_template
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "AliAbdelrasheed/maqa_llama",
    max_seq_length = 2048,
    dtype = None,
    load_in_4bit = False,  # Full precision
)

tokenizer = get_chat_template(tokenizer, chat_template="llama-3")
FastLanguageModel.for_inference(model)

messages = [
    {"from": "system", "value": "أنت طبيب محترف ولديك خبرة في كل مجالات الطب. يجيب على أسئلة المرضى حول الأمراض، باستخدام لهجة رسمية وودية، وإجابات موجزة ومفيدة يسهل على الجميع فهمها."},
    {"from": "human", "value": "ما هي أعراض مرض السكري من النوع الثاني؟"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to("cuda")

from transformers import TextStreamer
streamer = TextStreamer(tokenizer)
_ = model.generate(input_ids=inputs, streamer=streamer, max_new_tokens=256, use_cache=True)

Limitations

Not a substitute for professional medical advice or clinical diagnosis
Cannot prescribe, recommend, or endorse specific medications
Trained on a sampled subset of MAQA — performance may vary across all 20 specialisations
Optimised for Modern Standard Arabic; dialectal Arabic (Egyptian, Levantine, etc.) performance may vary
Web-scraped training data may contain noise or outdated medical information
Scarcity of high-quality Arabic medical data remains an open challenge in the field

Future Work

Fine-tuning on the full 430,000-row MAQA dataset (current model trained on a sampled subset)
Multi-turn conversational memory for sustained patient-doctor dialogue
Dialect-specific fine-tuning (Egyptian Arabic, Gulf Arabic)
Multimodal input support (e.g. dermatology image input)
Integration with electronic medical records (EMR) systems
Real-time knowledge updates from medical literature

Citation

If you use this model in your research, please cite the MAQA dataset:

@article{maqa2023,
  title={Deep learning for Arabic healthcare: MedicalBot},
  journal={Social Network Analysis and Mining},
  publisher={Springer},
  year={2023}
}

Developed By

Ali Abdelrasheed — Graduation Project Nile University · B.Sc. Information Technology – Big Data · Class of 2024 🤗 HuggingFace Profile

Downloads last month: 1

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for AliAbdelrasheed/maqa_llama

Base model

meta-llama/Llama-3.1-8B

Quantized

unsloth/Llama-3.1-8B-bnb-4bit

Finetuned

(1)

this model