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Model: rahimdzx/AraCode-7B-Full Source: Original Platform
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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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54
chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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62
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"torch_dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
"pad_token_id": 151665,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 1000000.0,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"unsloth_fixed": true,
|
||||
"unsloth_version": "2026.4.2",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 152064
|
||||
}
|
||||
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||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
|
||||
size 11422356
|
||||
17
tokenizer_config.json
Normal file
17
tokenizer_config.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": [],
|
||||
"is_local": false,
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|PAD_TOKEN|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
|
||||
}
|
||||
Reference in New Issue
Block a user