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--- |
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license: mit |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-Coder-7B-Instruct |
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pipeline_tag: text-generation |
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--- |
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## Model Description |
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This model is a fine-tuned variant of Qwen2.5-Coder-7B-Instruct, optimized for code-related tasks by training on a specialized code dataset. Fine-tuning was performed using the Hugging Face Transformers library, Low-Rank Adaptation(LoRA) and Parameter-Efficient Fine-Tuning (PEFT) techniques,. |
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- Intended Use: |
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The provided fine-tuned weights are ready for immediate use, making this model particularly suitable for developers and researchers working on code comprehension, generation, and related applications. |
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- Performance: |
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Users can anticipate enhanced performance on coding-specific tasks compared to the base model. The actual improvement varies based on the task and input data specifics. |
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## Use |
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The base model Qwen2.5-Coder-7B-Instruct needs to be downloaded and loaded in advance, then provide the following information: "## Problem Description: {} ## Test Cases: {} ## Error Code: {}" |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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from peft import PeftModel |
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model_path = 'Qwen/Qwen2.5-Coder-7B-Instruct' |
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lora_path = 'Code-AiHelper' |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto",torch_dtype=torch.bfloat16) |
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model = PeftModel.from_pretrained(model, model_id=lora_path) |
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system_prompt = '你是一位经验丰富的Python编程专家和技术顾问，擅长分析Python题目和学生编写的代码。你的任务是理解题目要求和测试样例，分析学生代码，找出潜在的语法或逻辑错误，提供具体的错误位置和修复建议，并用专业且易懂的方式帮助学生改进代码。请以markdown格式返回你的答案。' |
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user_prompt = '''## 题目描述：{} ## 测试样例：{} ## 错误代码：{}''' |
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messages = [ |
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{"role": "system", "content": system_prompt}, |
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{"role": "user", "content": user_prompt} |
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] |
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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model_inputs = tokenizer([text], return_tensors="pt").to('cuda') |
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generated_ids = model.generate( |
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model_inputs.input_ids, |
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max_new_tokens=1024 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
