Instructions to use wesam3/qu-llm-assistant-allam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wesam3/qu-llm-assistant-allam with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="wesam3/qu-llm-assistant-allam",
	filename="ALLaM-7B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use wesam3/qu-llm-assistant-allam with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wesam3/qu-llm-assistant-allam:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf wesam3/qu-llm-assistant-allam:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wesam3/qu-llm-assistant-allam:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf wesam3/qu-llm-assistant-allam:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf wesam3/qu-llm-assistant-allam:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf wesam3/qu-llm-assistant-allam:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf wesam3/qu-llm-assistant-allam:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wesam3/qu-llm-assistant-allam:Q4_K_M

Use Docker

docker model run hf.co/wesam3/qu-llm-assistant-allam:Q4_K_M

LM Studio
Jan

vLLM

How to use wesam3/qu-llm-assistant-allam with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wesam3/qu-llm-assistant-allam"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wesam3/qu-llm-assistant-allam",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wesam3/qu-llm-assistant-allam:Q4_K_M

Ollama
How to use wesam3/qu-llm-assistant-allam with Ollama:
```
ollama run hf.co/wesam3/qu-llm-assistant-allam:Q4_K_M
```

Unsloth Studio new

How to use wesam3/qu-llm-assistant-allam 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 wesam3/qu-llm-assistant-allam 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 wesam3/qu-llm-assistant-allam to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for wesam3/qu-llm-assistant-allam to start chatting

Docker Model Runner
How to use wesam3/qu-llm-assistant-allam with Docker Model Runner:
```
docker model run hf.co/wesam3/qu-llm-assistant-allam:Q4_K_M
```

Lemonade

How to use wesam3/qu-llm-assistant-allam with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wesam3/qu-llm-assistant-allam:Q4_K_M

Run and chat with the model

lemonade run user.qu-llm-assistant-allam-Q4_K_M

List all available models

lemonade list

QU-LLM Assistant (ALLaM) — wesam3/qu-llm-assistant-allam

A fine-tuned ALLaM-7B-Instruct model specialised in answering student advisory questions for Qassim University (جامعة القصيم). ALLaM is an Arabic-first large language model developed by SDAIA (Saudi Data and Artificial Intelligence Authority) and is particularly strong on Arabic-language tasks.

The model is distributed as a Q4_K_M GGUF file for efficient local inference with llama.cpp / Ollama.

Model Details

Property	Value
Base model	sdaia/ALLaM-7B-Instruct
Developed by (base)	SDAIA — Saudi Data and Artificial Intelligence Authority
Fine-tuning method	LoRA (MLX framework)
Quantisation	Q4_K_M (GGUF)
File size	~4.3 GB
Language	Arabic-native, English-secondary
License	Apache-2.0
Domain	University student advisory (academic rules, registration, scholarships, etc.)

Training Data

Dataset: 12,320 curated Q&A pairs filtered exclusively from 2024–2026 Qassim University documents — the most current bylaws, handbooks, and policy circulars.
Source: Publicly available PDFs and web pages from qu.edu.sa.
Language: Predominantly Arabic.
Rationale for filtering: Focusing on 2024-2026 documents ensures the model reflects the latest university regulations and avoids outdated policies.

Usage

Ollama (recommended)

ollama run wesamhamad/qu-llm-assistant-allam

Or pull the GGUF manually and create a local Modelfile:

FROM ./ALLaM-7B-Q4_K_M.gguf
SYSTEM "أنت مساعد ذكاء اصطناعي متخصص في الإجابة على استفسارات طلاب جامعة القصيم."

ollama create qu-llm-assistant-allam -f Modelfile
ollama run qu-llm-assistant-allam

llama.cpp

llama-cli -m ALLaM-7B-Q4_K_M.gguf \
  -p "ما هي متطلبات التسجيل في الفصل الدراسي الأول؟"

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(model_path="ALLaM-7B-Q4_K_M.gguf")
response = llm.create_chat_completion(messages=[
    {"role": "system",  "content": "أنت مساعد أكاديمي لجامعة القصيم."},
    {"role": "user",    "content": "ما هي شروط الحصول على المنحة الدراسية؟"},
])
print(response["choices"][0]["message"]["content"])

Why ALLaM?

ALLaM was pre-trained on an extensive Arabic corpus by SDAIA, giving it superior morphological understanding and dialectal coverage compared with multilingual models of similar size. Fine-tuning ALLaM on Qassim University data yields a model that is both culturally aware and domain-specific.

Intended Use & Limitations

Intended use: Helping students navigate university regulations, registration procedures, scholarship requirements, and academic policies at Qassim University — using the most up-to-date (2024-2026) policy documents.
Out-of-scope: General-purpose chat, medical/legal advice, topics unrelated to the university.
Hallucinations: Like all LLMs the model may occasionally produce incorrect information. Always verify critical details with the official university portal.

Citation

@misc{qu-llm-assistant-allam-2025,
  author    = {Wesam Hamad},
  title     = {QU-LLM Assistant (ALLaM): Fine-tuned ALLaM-7B for Qassim University Advisory},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/wesam3/qu-llm-assistant-allam}
}

License

Apache-2.0 — see LICENSE.

Downloads last month: 28

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit