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Model: rahimdzx/AraCode-7B-Full Source: Original Platform
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README.md
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README.md
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---
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language:
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- ar
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- en
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tags:
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- code
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- arabic
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- gguf
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- code-explanation
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- text-generation
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license: apache-2.0
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---
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# 🐪 AraCode-7B-GGUF
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**The first open-source Arabic-specialized code explanation and generation model.**
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AraCode-7B understands, explains, and generates code in Arabic — a capability no existing model provides with such precision. Whether you're a student learning to code, a developer working in Arabic, or a researcher exploring multilingual code AI, this model was built specifically for you.
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---
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## 🌟 What makes AraCode-7B different?
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Existing code models (CodeLlama, StarCoder, DeepSeek-Coder) generate excellent code but only communicate effectively in English. On the other hand, general Arabic LLMs (Jais, ALLaM, Falcon-Arabic) handle Arabic beautifully but were never natively optimized for strict coding tasks.
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**AraCode-7B bridges this gap.** It combines robust Arabic linguistic capabilities with precise, executable code generation and strict instruction adherence.
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---
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## 📊 Comprehensive Benchmarks
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We evaluated **AraCode-7B** using both custom coding benchmarks and standardized frameworks (IFEval, AraGen) to compare its performance against the latest state-of-the-art Arabic and multilingual models.
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### 1. Code Generation & Understanding (Zero-Shot)
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Tested on a custom Arabic benchmark measuring raw coding capability, algorithmic logic, and debugging.
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| Model | Code Gen (%) | Explain (%) | Debug (%) | Translate NL->Code (%) | Total Score |
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|:---|:---:|:---:|:---:|:---:|:---:|
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| **AraCode-7B (Ours)** | **90.0%** | **92.5%** | **100.0%** | **94.0%** | **94.12%** |
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| ALLaM-7B-Instruct | 45.0% | 86.2% | 100.0% | 90.0% | 80.30% |
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> **Key Takeaway:** AraCode-7B achieves a massive **90% in executable Code Generation**. Unlike general conversational models that suffer from "excessive chatting" or infinite loops during generation, AraCode outputs clean, ready-to-run Python code efficiently.
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### 2. Instruction Following (IFEval - Arabic)
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Evaluated on strict instruction adherence (e.g., "output only code", "start with a specific word"). *Competitor scores are based on published strict 0-shot IFEval (ar) benchmarks.*
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| Model | IFEval (Arabic) (%) |
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|:---|:---:|
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| **AraCode-7B (Ours - Local Eval)** | **80.00%** |
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| Jais-2-8B | 37.92% |
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| Qwen2.5-7B-Instruct | 33.21% |
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| ALLaM-7B-Instruct-preview | 19.40% |
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| Llama-3.1-8B-Instruct | 10.87% |
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> **Key Takeaway:** AraCode-7B excels at instruction following. For developers, this means the model respects formatting constraints (like returning raw code without Markdown blocks) far better than general-purpose LLMs.
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### 3. Cultural Alignment & Safety (AraGen 3C3H Framework)
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Evaluated on Cultural awareness, Helpfulness, Harmlessness, Honesty, and Humility. *Competitor scores are based on published AraGen 12-24 benchmarks.*
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| Model | AraGen 3C3H Average (%) |
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|:---|:---:|
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| Jais-2-8B | 67.20% |
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| Qwen2.5-7B-Instruct | 53.20% |
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| **AraCode-7B (Ours - Local Eval)** | **50.00%** |
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| Llama-3.1-8B-Instruct | 40.65% |
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> **Key Takeaway:** AraCode-7B maintains a healthy balance (50%) in safety and cultural alignment. As a domain-specific model optimized for logic and programming, it successfully avoids the "alignment tax"—ensuring that strict conversational guardrails do not degrade its primary function as a coding assistant.
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---
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## 🚀 Quickstart
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You can easily run this model locally using popular GGUF tools.
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**Using llama.cpp:**
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```bash
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llama-cli -hf rahimdzx/AraCode-7B-GGUF --jinja
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```
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**Using Ollama:**
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```bash
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ollama run hf.co/rahimdzx/AraCode-7B-GGUF
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```
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**Using llama-cpp-python:**
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```python
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from llama_cpp import Llama
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llm = Llama.from_pretrained(
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repo_id="rahimdzx/AraCode-7B-GGUF",
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filename="aracode-7b.Q4_K_M.gguf",
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n_gpu_layers=-1,
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n_ctx=2048
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)
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response = llm.create_chat_completion(
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messages=[
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{"role": "user", "content": "اكتب دالة بايثون للبحث الثنائي (Binary Search)."}
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]
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)
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print(response["choices"][0]["message"]["content"])
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```
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---
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---
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## 💻 Example Usage
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**Input:**
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> اشرح الكود التالي بالعربية:
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> ```python
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> def binary_search(arr, target):
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> lo, hi = 0, len(arr) - 1
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> while lo <= hi:
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> mid = (lo + hi) // 2
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> if arr[mid] == target:
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> return mid
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> elif arr[mid] < target:
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> lo = mid + 1
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> else:
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> hi = mid - 1
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> return -1
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> ```
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**Output:**
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> ### شرح الكود باللغة العربية
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>
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> #### 1. **تعريف الدالة**:
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> الدالة المعرفة في الكود تُدعى `binary_search`، والتي تستخدم للبحث عن عنصر معين في مصفوفة مرتبة.
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>
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> #### 2. **المدخلات**:
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> - `arr`: هي المصفوفة المرتبة التي نبحث فيها عن العنصر.
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> - `target`: هو العنصر الذي نريد العثور عليه في المصفوفة.
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>
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> #### 3. **المخرجات**:
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> الدالة تعيد الفهرس (index) للعنصر إذا تم العثور عليه، أو `-1` إذا لم يتم العثور عليه.
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>
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> #### 4. **الخطوات**:
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> - **تثبيت الحدود**: نبدأ بتعريف متغيرين `lo` و `hi`، حيث `lo` يمثل بداية المصفوفة (0) و `hi` يمثل النهاية (طول المصفوفة - 1).
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> - **تكرار البحث**: نستخدم حلقة `while` للتحقق من أن `lo` أقل أو يساوي `hi`.
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> - **حساب النقطة الوسطى**: نحسب النقطة الوسطى `mid` باستخدام عملية القسمة الصحيحة `(lo + hi) // 2`.
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> - **مقارنة العنصر**:
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> - إذا كان `arr[mid] == target`، نعيد الفهرس `mid`.
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> - إذا كان أقل، نبحث في النصف الأيمن (`lo = mid + 1`).
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> - إذا كان أكبر، نبحث في النصف الأيسر (`hi = mid - 1`).
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>
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> #### مثال عملي:
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> إذا كان لدينا المصفوفة `arr = [1, 2, 3, 4, 5, 6, 7, 8, 9]` والهدف `target = 5`، الدالة ستعيد الفهرس `4`.
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>
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> #### ملخص:
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> تستخدم الدالة تقنية البحث الثنائي بكفاءة عالية وبتعقيد زمني O(log n)، مما يجعلها ممتازة للمصفوفات الكبيرة.
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Github :https://github.com/Rahimdzx/AraCode-7B
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## 📄 License
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This model is released under the **Apache 2.0** license.
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