初始化项目，由ModelHub XC社区提供模型

Model: CUAIStudents/Qwen-Ar-GEC
Source: Original Platform

.gitattributes

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README.md

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+---
+library_name: transformers
+tags:
+- llama-factory
+---
+
+# Qwen-Ar-GEC
+
+Qwen-Ar-GEC is a fine-tuned adaptation of the Qwen model for **Arabic Grammatical Error Correction (GEC)**.  
+The goal of this model is to automatically detect and correct grammatical, spelling, and stylistic errors in Arabic text,  
+making it useful for applications such as language learning, academic writing assistance, and automated proofreading.
+
+# Architecture
+
+This model was fine-tuned using the **QLoRA** method on **50,000 samples**, based on the **[Qwen 2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)** architecture.  
+The fine-tuning followed the system instruction below:
+
+```
+صحّح الأخطاء النحوية والإملائية فقط إن وُجدت. أضف التشكيل الكامل على كل الحروف إجباريًا — حتى لو كان النص صحيحًا. لا تُغيّر أي كلمة أو اسم أو رقم أو بنية جملة. إذا لم يكن هناك خطأ نحوي أو إملائي، أعد إنتاج المدخلات كما هي — لكن مع التشكيل الكامل. لا تُضف شروحات. لا تُكرر المدخلات. لا تُعدِل المعنى.
+```
+Training was conducted with **[Llama Factory](https://github.com/hiyouga/LLaMA-Factory)**, using a rank `r = 32`, and `alpha = 64`.
+
+
+# Dataset
+
+This model is train on 50000 sample of **[our dataset](https://huggingface.co/datasets/CUAIStudents/Arabic-Tashkeel)** but with small pre-processing since we are dealing with larger knowledge.
+
+
+# Usage
+
+```python
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "Abdo-Alshoki/qwen-ar-gec-v2"
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+# Recommended system instruction (same as training)
+system_prompt = """صحّح الأخطاء النحوية والإملائية فقط إن وُجدت. أضف التشكيل الكامل على كل الحروف إجباريًا — حتى لو كان النص صحيحًا. لا تُغيّر أي كلمة أو اسم أو رقم أو بنية جملة. إذا لم يكن هناك خطأ نحوي أو إملائي، أعد إنتاج المدخلات كما هي — لكن مع التشكيل الكامل. لا تُضف شروحات. لا تُكرر المدخلات. لا تُعدِل المعنى."""
+
+# Example input
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": "مِنَ الْمُهِمِّ أَنْ لاَ يَسسْقُطُؤأ أَبَدًا، وَلاَ يَبْقَوْا فِي الخَارِجِ طَوِيلاً لأَنَّهُمْ يَحْتَاجُونَ إلَى الرِّطَابِ."}
+]
+
+# Format prompt and tokenize
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+# Generate output
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True)) # مِنَ الْمُهِمِّ أَنْ لاَ يَسْقُطُوا أَبَدًا، وَلاَ يَبْقَوْا فِي الخَارِجِ طَوِيلاً لأَنَّهُمْ يَحْتَاجُونَ إلَى الرِّطَابِ.
+
+```
+
+# limits and improvements
+
+This model achieves promising accuracy on our dataset; however, the dataset contains limited coverage of Modern Standard Arabic (MSA). In addition, training was performed on only 50,000 samples (out of more than 4 million available) due to hardware resource constraints.
+
+
+
+

added_tokens.json

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+  "<tool_call>": 151657,
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+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
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+  "bos_token_id": 151643,
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+  "hidden_act": "silu",
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+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
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+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

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+  "do_sample": true,
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+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.55.0"
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