language, license, library_name, base_model, datasets, tags, pipeline_tag

language	license	library_name	base_model	datasets	tags	pipeline_tag
ar	apache-2.0	gguf	NightPrince/Qwen3-4B-Islamic-Arabic	NightPrince/islamic-arabic-qa	arabic islamic fiqh fatwa qwen3 gguf llama-cpp ollama quantized instruction-tuning	text-generation

Qwen3-4B-Islamic-Arabic-GGUF

GGUF quantized versions of Qwen3-4B-Islamic-Arabic for llama.cpp, Ollama, and LM Studio.

This repository contains three GGUF files at different quantization levels, converted from NightPrince/Qwen3-4B-Islamic-Arabic (the merged FP16 model). All standard GGUF-compatible runtimes are supported: llama.cpp, Ollama, LM Studio, Jan, and others.

Trained and converted by Yahya Alnwsany (NightPrince) — 2026-05-05.

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Files

File	Size	Recommended for
qwen3-4b-islamic-q4_k_m.gguf	2.3 GB	Most users — best quality/size balance
qwen3-4b-islamic-q8_0.gguf	4.0 GB	High quality, more RAM available
qwen3-4b-islamic-f16.gguf	7.5 GB	Reference / re-quantization source

Recommendation: Start with q4_k_m. If you have 6+ GB of RAM headroom and want noticeably sharper Arabic output, use q8_0. The f16 file is the lossless reference and is best used as a source for producing custom quantizations with llama.cpp's llama-quantize.

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Model Variants

Variant	Repo	Description
Merged FP16	NightPrince/Qwen3-4B-Islamic-Arabic	Canonical merged model, FP16, ~7.6 GB — drop-in for transformers or vLLM
LoRA Adapter	NightPrince/Qwen3-4B-Islamic-Arabic-LoRA	PEFT adapter only, 264 MB — apply on top of Qwen/Qwen3-4B
INT4 Quantized	NightPrince/Qwen3-4B-Islamic-Arabic-INT4	W4A16 compressed-tensors for fast vLLM serving, 2.5 GB
MLX 4-bit	NightPrince/Qwen3-4B-Islamic-Arabic-mlx-4Bit	Apple Silicon / MLX — native Mac inference, 4-bit quantized
GGUF (this model)	NightPrince/Qwen3-4B-Islamic-Arabic-GGUF	llama.cpp / Ollama / LM Studio — Q4_K_M (2.3 GB), Q8_0 (4.0 GB), F16 (7.5 GB)
Dataset	NightPrince/islamic-arabic-qa	17,944 train / 2,101 val / 1,042 test — Islamic Arabic Q&A pairs

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Usage

Ollama

Step 1: Create a Modelfile

Save the following as Modelfile (no extension) in any directory:

FROM ./qwen3-4b-islamic-q4_k_m.gguf

SYSTEM """أنت مساعد عالم إسلامي متخصص. أجب على الأسئلة بدقة استناداً إلى القرآن الكريم والسنة النبوية والفقه الإسلامي الكلاسيكي. استشهد بالمصادر حيثما أمكن. كن موجزاً لكن شاملاً."""

PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER num_ctx 4096

Important: The SYSTEM field above contains the exact system prompt the model was fine-tuned with. Using it will produce the best results.

Step 2: Download the GGUF file

# Using huggingface-cli
pip install huggingface_hub
huggingface-cli download NightPrince/Qwen3-4B-Islamic-Arabic-GGUF \
    qwen3-4b-islamic-q4_k_m.gguf \
    --local-dir .

Step 3: Build and run

# Create the Ollama model
ollama create qwen3-islamic -f Modelfile

# Run interactively
ollama run qwen3-islamic

# Or query via API
curl http://localhost:11434/api/generate -d '{
  "model": "qwen3-islamic",
  "prompt": "ما حكم الاحتفال بالمولد النبوي الشريف؟",
  "stream": false
}'

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llama.cpp

Build llama.cpp (if not already installed):

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make -j$(nproc)            # CPU
# For CUDA: make GGML_CUDA=1 -j$(nproc)

Download a GGUF file:

huggingface-cli download NightPrince/Qwen3-4B-Islamic-Arabic-GGUF \
    qwen3-4b-islamic-q4_k_m.gguf \
    --local-dir ./models

Run the llama.cpp HTTP server:

./llama-server \
    --model ./models/qwen3-4b-islamic-q4_k_m.gguf \
    --ctx-size 4096 \
    --n-gpu-layers 99 \
    --host 0.0.0.0 \
    --port 8080 \
    --system-prompt "أنت مساعد عالم إسلامي متخصص. أجب على الأسئلة بدقة استناداً إلى القرآن الكريم والسنة النبوية والفقه الإسلامي الكلاسيكي. استشهد بالمصادر حيثما أمكن. كن موجزاً لكن شاملاً."

CLI inference:

./llama-cli \
    --model ./models/qwen3-4b-islamic-q4_k_m.gguf \
    --ctx-size 4096 \
    --n-gpu-layers 99 \
    --chat-template qwen3 \
    --system-prompt "أنت مساعد عالم إسلامي متخصص. أجب على الأسئلة بدقة استناداً إلى القرآن الكريم والسنة النبوية والفقه الإسلامي الكلاسيكي. استشهد بالمصادر حيثما أمكن. كن موجزاً لكن شاملاً." \
    --prompt "ما هي أركان الإسلام الخمسة؟" \
    --n-predict 512

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LM Studio

1. Open LM Studio and go to the Search tab.
2. Search for NightPrince/Qwen3-4B-Islamic-Arabic-GGUF.
3. Download qwen3-4b-islamic-q4_k_m.gguf (recommended) from the file list.
4. Load the model and open the Chat tab.
5. In System Prompt, paste:

   أنت مساعد عالم إسلامي متخصص. أجب على الأسئلة بدقة استناداً إلى القرآن الكريم والسنة النبوية والفقه الإسلامي الكلاسيكي. استشهد بالمصادر حيثما أمكن. كن موجزاً لكن شاملاً.
   

6. Set Temperature to 0.7 and Context Length to 4096 for best results.

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Hardware Requirements

File	Min RAM (CPU)	Min VRAM (GPU offload)
q4_k_m (2.3 GB)	4 GB	3–4 GB
q8_0 (4.0 GB)	6 GB	5–6 GB
f16 (7.5 GB)	10 GB	8–10 GB

Use --n-gpu-layers 99 in llama.cpp to offload all layers to GPU. Reduce the value if you run out of VRAM.

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Citation

@misc{alnwsany2026qwen3islamicarbic,
  author       = {Yahya Alnwsany},
  title        = {Qwen3-4B-Islamic-Arabic: QLoRA Fine-Tuning of Qwen3-4B on Islamic Arabic Q\&A},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/NightPrince/Qwen3-4B-Islamic-Arabic}},
  note         = {Base model: Qwen/Qwen3-4B. Dataset: NightPrince/islamic-arabic-qa.}
}

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License

Apache 2.0 — consistent with the base model Qwen/Qwen3-4B.