# Code Repair

Qwen2.5-Coder represents the latest series of the Qwen large language models, focusing on code generation, reasoning, and repair. Built on the robust foundations of Qwen2.5, with a training corpus expanded to 5.5 trillion tokens that includes source code, textual code bases, and synthetic data, Qwen2.5-Coder-32B has emerged as the leading open-source code LLM, matching the coding capabilities of GPT-4o. This model is a 4-bit instruction-tuned version of the 0.5B parameters, featuring characteristics such as causal language modeling, pre-training and fine-tuning, as well as a transformer architecture.

Qwen2.5-Coder is the latest series of the Qwen large language models, focusing on code generation, reasoning, and repair. Built on the powerful Qwen2.5, it has been trained on a dataset of 550 trillion tokens including source code, code-grounded texts, and synthetic data. Qwen2.5-Coder-32B has emerged as the most advanced open-source code large language model, matching the coding capabilities of GPT-4o. In practical applications, it provides a more comprehensive foundation, such as a code agent, enhancing coding prowess while retaining advantages in math and general abilities.

Qwen2.5-Coder is a series of large language models specifically designed for coding, ranging from 0.5 billion to 3.2 billion parameters to meet various developer needs. The model shows significant improvements in code generation, reasoning, and repair, leveraging a powerful Qwen2.5 framework trained on 5.5 trillion tokens, including source code, textual code bases, and synthetic data. Qwen2.5-Coder-32B is currently the most advanced open-source large language model for code generation, matching the coding capabilities of GPT-4o. Additionally, the model supports long contexts of up to 128K tokens and employs AWQ 4-bit quantization technology to enhance its efficiency and performance.

The Qwen2.5-Coder-32B-Instruct-GPTQ-Int4 is a large language model based on Qwen2.5, featuring 3.25 billion parameters and supporting long text processing with a maximum of 128K tokens. This model has shown significant improvements in code generation, code inference, and code repair, making it a leader among current open-source code language models. It not only enhances coding capabilities but also maintains strengths in mathematics and general reasoning.

Qwen2.5-Coder is a model specifically designed for code generation, significantly improving capabilities in this area, with a variety of parameter sizes and support for quantization. It is free and enhances efficiency and quality for developers.

Qwen2.5-Coder is the latest series of the Qwen large language models, focusing on code generation, code reasoning, and code repair. Built upon the powerful Qwen2.5 model, this series significantly enhances coding capabilities by adding a training token volume of 5.5 trillion, which includes source code, text code bases, and synthetic data. Qwen2.5-Coder-32B has become the current state-of-the-art open-source large language model for code, comparable in coding ability to GPT-4o. Moreover, Qwen2.5-Coder provides a more comprehensive foundation for practical applications like code agents, enhancing coding abilities while maintaining strengths in mathematics and general capabilities.

Qwen2.5-Coder is the latest series of the Qwen large language model, focused on code generation, reasoning, and repair. Based on the powerful Qwen2.5, this model series significantly enhances code generation, reasoning, and repair capabilities by increasing training tokens to 5.5 trillion, including source code, text grounding, synthetic data, and more. The Qwen2.5-Coder-3B model contains 3.09B parameters, 36 layers, 16 attention heads (Q), and 2 attention heads (KV), with a total context length of 32,768 tokens. It stands out among open-source code LLMs, matching the coding capabilities of GPT-4o, and provides developers with a powerful code assistance tool.

The Qwen2.5-Coder series consists of code-specific models based on the Qwen2.5 architecture, including Qwen2.5-Coder-1.5B and Qwen2.5-Coder-7B. These models continue to be pre-trained on a massive corpus of over 5.5 trillion tokens, showcasing impressive code generation capabilities while maintaining generality through meticulous data cleaning, scalable synthetic data generation, and balanced data mixing. Qwen2.5-Coder has achieved state-of-the-art performance in over ten benchmark tests across various code-related tasks, including code generation, completion, reasoning, and repair, consistently outperforming larger models of comparable size. The release of this series not only pushes the boundaries of intelligent coding research but also encourages developers to adopt it for real-world applications through its licensing.

Qwen2.5-Coder-14B is a large language model in the Qwen series focused on code, encompassing various model sizes ranging from 0.5 to 32 billion parameters to meet diverse developer needs. The model shows significant improvements in code generation, reasoning, and repair, built upon the powerful Qwen2.5, with a training token expansion to 5.5 trillion, including source code, grounded text code, and synthetic data. Qwen2.5-Coder-32B has become the leading open-source code LLM, matching the coding capacity of GPT-4o. Additionally, it provides a comprehensive foundation for real-world applications such as code agents, enhancing coding abilities while maintaining advantages in mathematics and general tasks. It supports long contexts of up to 128K tokens.

Qwen2.5-Coder-14B-Instruct is a large language model in the Qwen2.5-Coder series, focusing on code generation, reasoning, and repair. Built upon the powerful Qwen2.5, this model is trained on 5.5 trillion tokens, including source code and synthesized data, making it a leading open-source code LLM. It not only enhances coding capabilities but also maintains strengths in mathematics and general abilities while supporting long contexts of up to 128K tokens.

Qwen2.5-Coder represents a series of large language models designed specifically for code generation, featuring six mainstream model sizes with 0.5, 1.5, 3, 7, 14, and 32 billion parameters to meet diverse developers' needs. This model has made significant improvements in code generation, reasoning, and repair, built upon the robust Qwen2.5, trained on a token count expanding to 5.5 trillion, including source code, text code basics, synthetic data, and more. The Qwen2.5-Coder-32B is currently the most advanced open-source code generation large language model, rivaling the encoding capabilities of GPT-4o. It not only enhances coding abilities but also retains advantages in mathematics and general understanding, supporting long contexts of up to 128K tokens.

Aide is an open-source AI-native integrated development environment (IDE) that runs on Swebench-lite using the latest agentic framework. It can provide code repair suggestions or inquire about potentially missing files. Aide achieves this by iterating through linter errors and leveraging LSP tools (such as 'Go to references') to pull relevant context. The key advantages of Aide include developer control, the experience of pairing with real engineers, quick invocation, and local-first intelligent processing. Aide aims to address maintainability and accuracy issues in AI editing within large codebases, resolving 43% of problems through SWE-Bench Lite testing, thus becoming the current best solution.

WaveCoder is a large language model for code developed by Microsoft Research Asia. It enhances the versatility and functionality of code language models through instruction fine-tuning. The model demonstrates exceptional performance across various programming tasks, including code summarization, generation, translation, and repair. Its innovation lies in the data synthesis framework and two-stage instruction data generation strategy, ensuring high quality and diversity of data. The model's open-source nature provides developers with a powerful coding assistance tool, contributing to increased development efficiency and code quality.

Qwen2.5-Coder is part of the Qwen2.5 open-source family, focusing on tasks like code generation, inference, and repair. By leveraging large-scale code training data, it improves code capability while maintaining mathematical and general skills. The model supports 92 programming languages and has shown significant advancements in code-related tasks. Qwen2.5-Coder is licensed under Apache 2.0 to accelerate the application of coding intelligence.

ProMind.AI is a content generation tool based on the OpenAI GPT-3 and GPT-4 models. It can generate tweets, blog posts, LinkedIn posts, YouTube scripts, and more. This tool can help you fix code errors, generate code, and save time. ProMind.AI has powerful features that can help you improve your writing efficiency.

ProMindGPT is a content generation tool based on the OpenAI GPT-3 and GPT-4 models. It can help users generate a variety of text content, including tweets, blog posts, LinkedIn posts, and YouTube scripts. ProMindGPT also assists in fixing code errors and generating code snippets, saving users time and effort.

MagickPen is a ChatGPT-powered AI writing assistant that can quickly generate articles, essays, reports, stories, advertisements, and even jokes. It also includes translation, grammar checking, and code repair features to enhance your writing abilities. MagickPen offers a free trial and paid plans, including pay-as-you-go and subscription options.

ProMind.AI is a content generation tool powered by OpenAI's GPT-3 and GPT-4 models. It can help you generate tweets, blog posts, LinkedIn posts, YouTube scripts, and more. It can also assist in fixing code errors and generating code, saving you valuable time. ProMind.AI boasts powerful features and a user-friendly interface to cater to all your writing needs. Pricing varies based on usage; for detailed information, please visit our official website.