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Model: NightPrince/Qwen3-4B-Islamic-Arabic Source: Original Platform
2026-05-26 01:34:18 +08:00
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+---
+language:
+  - ar
+license: apache-2.0
+library_name: transformers
+base_model: Qwen/Qwen3-4B
+datasets:
+  - NightPrince/islamic-arabic-qa
+tags:
+  - arabic
+  - islamic
+  - fiqh
+  - fatwa
+  - qlora
+  - peft
+  - qwen3
+  - instruction-tuning
+  - conversational
+pipeline_tag: text-generation
+inference:
+  parameters:
+    max_new_tokens: 512
+    temperature: 0.3
+    do_sample: true
+widget:
+  - text: "ما حكم زكاة الفطر وما مقدارها؟"
+    example_title: "زكاة الفطر"
+  - text: "ما الفرق بين الفرض والواجب عند الحنفية؟"
+    example_title: "الفرض والواجب"
+  - text: "ما حكم بيع العينة في الفقه الإسلامي؟"
+    example_title: "بيع العينة"
+  - text: "ما شروط صحة الصلاة؟"
+    example_title: "شروط الصلاة"
+model-index:
+  - name: Qwen3-4B-Islamic-Arabic
+    results:
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: Islamic Arabic Q&A
+          type: NightPrince/islamic-arabic-qa
+          split: validation
+        metrics:
+          - name: Validation Loss
+            type: loss
+            value: 2.4094
+            verified: false
+---
+
+# Qwen3-4B-Islamic-Arabic
+
+**Qwen3-4B fine-tuned on Islamic Arabic Q&A via QLoRA — merged FP16, ready for direct inference.**
+
+This is the canonical, fully merged version of a Qwen3-4B model fine-tuned on 17,944 high-quality Islamic Arabic question-answer pairs spanning Fiqh, Fatwa, Aqeedah, Quran Sciences, and Islamic Finance. The LoRA adapter has been merged into the base weights and saved in FP16; no additional adapter loading is required.
+
+Trained by **[Yahya Alnwsany (NightPrince)](https://huggingface.co/NightPrince)** — 2026-05-05.
+
+---
+
+## Model Variants
+
+| Variant | Repo | Description |
+|---|---|---|
+| **Merged FP16** (this model) | [NightPrince/Qwen3-4B-Islamic-Arabic](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/NightPrince/Qwen3-4B-Islamic-Arabic-mlx-4Bit) | Apple Silicon / MLX — native Mac inference, 4-bit quantized |
+| **GGUF** | [NightPrince/Qwen3-4B-Islamic-Arabic-GGUF](https://huggingface.co/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](https://huggingface.co/datasets/NightPrince/islamic-arabic-qa) | 17,944 train / 2,101 val / 1,042 test — Islamic Arabic Q&A pairs |
+
+---
+
+## Training Metrics
+
+### Loss Curve
+
+| Checkpoint | Train Loss | Eval Loss |
+|---|---|---|
+| Step 0 (init) | — | — |
+| Step 843 (final) | **1.8918** | **2.4094** (best) |
+
+### Token Accuracy
+
+| 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.
+
+---
+
+## Usage
+
+### Transformers Inference
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import 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)
+```
+
+### vLLM Serving
+
+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 needed
+pip install vllm
+
+# Serve with tensor parallelism across 2 GPUs
+vllm serve NightPrince/Qwen3-4B-Islamic-Arabic \
+    --dtype float16 \
+    --tensor-parallel-size 2 \
+    --max-model-len 4096 \
+    --port 8000
+```
+
+Query 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](https://huggingface.co/NightPrince/Qwen3-4B-Islamic-Arabic-INT4) (2.5 GB) for vLLM or the [GGUF variant](https://huggingface.co/NightPrince/Qwen3-4B-Islamic-Arabic-GGUF) for llama.cpp / Ollama.
+
+---
+
+## Training Details
+
+### Dataset
+
+| Property | Value |
+|---|---|
+| Dataset | [NightPrince/islamic-arabic-qa](https://huggingface.co/datasets/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) |
+
+### Hyperparameters
+
+| 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`) |
+
+### LoRA Configuration
+
+| 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 |
+
+### Results
+
+| 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) |
+
+### Hardware
+
+| 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 |
+
+### Software Environment
+
+| 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 |
+
+---
+
+## Limitations
+
+- **Domain scope**: The model is optimized for Islamic Arabic Q&A. General Arabic tasks or non-Islamic domains may show degraded quality compared to the base Qwen3-4B.
+- **Source attribution**: While the model is trained to cite sources, citations should be independently verified — the model can produce plausible-sounding but incorrect references.
+- **Classical vs. contemporary Fiqh**: The training data emphasizes classical scholarship. Contemporary jurisprudential debates, especially minority or regional opinions, may be underrepresented.
+- **Language**: The model performs best in Arabic (Modern Standard and Classical). Responses in other languages are not guaranteed to be accurate or fluent.
+
+---
+
+## Citation
+
+```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.}
+}
+```
+
+---
+
+## License
+
+This model is released under the **Apache 2.0** license, consistent with the base model [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B). See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for details.