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NightPrince
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Model Details
Model Provider
NightPrince
Model Tree
Qwen/Qwen3-4B
Input Modalities
Output Modalities
Supported Functionality
| Variant | Repo | Description |
|---|---|---|
| Merged FP16 (this model) | 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 | 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 |
| Checkpoint | Train Loss | Eval Loss |
|---|---|---|
| Step 0 (init) | — | — |
| Step 843 (final) | 1.8918 | 2.4094 (best) |
| Phase | Token Accuracy |
|---|---|
| Early training | ~50% |
| End of training | ~60% |
MCQ evaluation coming soon — a multiple-choice benchmark (Islamics domain) is prepared but requires serving the model via vLLM. Results will be posted here once available.
python
from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "NightPrince/Qwen3-4B-Islamic-Arabic" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto",) 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.") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": "ما حكم الزكاة على المال المدخر؟"},] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True,)inputs = tokenizer(text, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.7, top_p=0.9, do_sample=True, ) response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(response)The merged FP16 model is ~7.6 GB. Use at least tensor_parallel_size=2 on 11 GB GPUs (e.g., RTX 2080 Ti), or a single 24 GB+ GPU.
bash
# Install vLLM if neededpip install vllm # Serve with tensor parallelism across 2 GPUsvllm serve NightPrince/Qwen3-4B-Islamic-Arabic \ --dtype float16 \ --tensor-parallel-size 2 \ --max-model-len 4096 \ --port 8000Query the running server:
python
from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="token-abc123") 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.") response = client.chat.completions.create( model="NightPrince/Qwen3-4B-Islamic-Arabic", messages=[ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": "ما حكم الزكاة على المال المدخر؟"}, ], max_tokens=512, temperature=0.7,)print(response.choices[0].message.content)Prefer lower memory? Use the INT4 quantized variant (2.5 GB) for vLLM or the GGUF variant for llama.cpp / Ollama.
| Property | Value |
|---|---|
| Dataset | NightPrince/islamic-arabic-qa |
| Train split | 17,944 samples |
| Validation split | 2,101 samples |
| Test split | 1,042 samples |
| Language | Arabic (Modern Standard + Classical) |
| Domains | Fiqh, Fatwa, Aqeedah, Quran Sciences, Islamic Finance |
| Quality filter | Applied — deduplication, length filtering, domain relevance scoring |
| Format | Instruction-following (system / user / assistant) |
| Hyperparameter | Value |
|---|---|
| Epochs | 3 |
| Per-device batch size | 1 |
| Gradient accumulation steps | 16 |
| Effective batch size | 64 |
| Learning rate | 2e-4 |
| LR scheduler | Cosine with warmup |
| Warmup ratio | 0.05 |
| Max sequence length | 1,024 tokens |
| Optimizer | AdamW (paged, 8-bit) |
| Precision | QLoRA (4-bit base + BF16 adapters) |
| Gradient checkpointing | Enabled |
| Loss masking | Assistant turns only (assistant_only_loss=True) |
| Parameter | Value |
|---|---|
| Rank (r) | 64 |
| Alpha (α) | 128 |
| Dropout | 0.05 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Trainable parameters | 132,120,576 |
| % of total parameters | 5.65% of 4.15B |
| Metric | Value |
|---|---|
| Final train loss | 1.8918 |
| Best eval loss | 2.4094 |
| Total training steps | 843 |
| Training duration | 7.59 hours |
| Token accuracy (start → end) | ~50% → ~60% |
| MCQ benchmark | Coming soon (requires vLLM serving) |
| Component | Specification |
|---|---|
| GPUs | 4× NVIDIA GeForce RTX 2080 Ti (11 GB VRAM each, 44 GB total) |
| CUDA version | 13.0 |
| Training framework | DDP via Hugging Face Accelerate |
| Library | Version |
|---|---|
| Python | 3.11.15 |
| PyTorch | 2.11.0+cu130 |
| Transformers | 4.57.6 |
| PEFT | 0.18.1 |
| TRL | 1.3.0 |
| BitsAndBytes | 0.49.2 |
| Accelerate | 1.13.0 |
bibtex
@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.}}This model is released under the Apache 2.0 license, consistent with the base model Qwen/Qwen3-4B. See LICENSE for details.
from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "NightPrince/Qwen3-4B-Islamic-Arabic" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto",) 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.") messages = [ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": "ما حكم الزكاة على المال المدخر؟"},] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True,)inputs = tokenizer(text, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.7, top_p=0.9, do_sample=True, ) response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(response)# Install vLLM if neededpip install vllm # Serve with tensor parallelism across 2 GPUsvllm serve NightPrince/Qwen3-4B-Islamic-Arabic \ --dtype float16 \ --tensor-parallel-size 2 \ --max-model-len 4096 \ --port 8000from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="token-abc123") 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.") response = client.chat.completions.create( model="NightPrince/Qwen3-4B-Islamic-Arabic", messages=[ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": "ما حكم الزكاة على المال المدخر؟"}, ], max_tokens=512, temperature=0.7,)print(response.choices[0].message.content)@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.}}