# Code Reasoning

Qwen2.5-Coder is the latest series of the Qwen large language model, focusing on code generation, reasoning, and debugging. Based on the robust Qwen2.5, this series significantly improves code generation, reasoning, and repair capabilities by incorporating 55 trillion training tokens, including source code, text code grounding, and synthetic data. The Qwen2.5-Coder-32B has emerged as the most advanced open-source large language model for code generation, matching the coding capabilities of GPT-4o. Additionally, Qwen2.5-Coder offers a more comprehensive foundation for real-world applications, such as code agents, enhancing coding abilities while maintaining strengths in mathematics and general proficiency.

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 from the Qwen large language model, focusing on code generation, reasoning, and debugging. Built upon the powerful Qwen2.5 framework, this model has been trained on 550 trillion source code, text-code correlations, and synthesized data, making it one of the leading open-source code language models today, rivaling GPT-4 in coding ability. Additionally, Qwen2.5-Coder offers comprehensive real-world application capabilities, such as code agents, enhancing coding proficiency while maintaining strengths in mathematical and general skills.

Qwen2.5-Coder is the latest series of the Qwen large language models, focusing on code generation, reasoning, and debugging. Based on the powerful Qwen2.5 architecture, this model was trained on 550 trillion source codes, text-code associations, synthetic data, and more, making it a leader among current open-source code language models. It not only enhances programming capabilities but also retains advantages in mathematics and general-purpose tasks.

Qwen2.5-Coder is the latest series of the Qwen large language model, specifically designed for code generation, reasoning, and debugging. Built on the powerful Qwen2.5 framework, it has scaled training tokens to 5.5 trillion, incorporating source code, text code bases, synthetic data, among others. Qwen2.5-Coder-32B has emerged as the most advanced open-source large language model for code, matching the coding capabilities of GPT-4o. This model is a 1.5B parameter instruction-tuned version in GGUF format, featuring causal language modeling, pre-training and post-training phases, and a transformers architecture.

Qwen2.5-Coder is the latest series of the Qwen large language model, designed for code generation, reasoning, and fixing. Built on the powerful Qwen2.5, this model has been trained on 550 trillion source codes, text code foundations, and synthetic data, elevating its coding capabilities to the forefront of open-source code LLMs. It not only enhances coding abilities but also maintains strengths in mathematics and general capabilities.

The Qwen2.5-Coder-3B-Instruct-GPTQ-Int8 is a large language model optimized for code generation, reasoning, and debugging, part of the Qwen2.5-Coder series. Based on Qwen2.5, it has been trained on a dataset including source code, code-text associations, and synthetic data, achieving 550 trillion training tokens. The Qwen2.5-Coder-32B has emerged as the leading open-source large language model for code, matching coding capabilities with GPT-4o. This model also provides a comprehensive foundation for real-world applications such as code assistance, augmenting coding capabilities while maintaining strengths in mathematics and general skills.

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.

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 model, focusing on code generation, reasoning, and fixing. Built on the powerful Qwen2.5 with an extended training dataset of 5.5 trillion tokens that includes source code, text code bases, and synthetic data, Qwen2.5-Coder-32B has become the leading open-source code LLM, matching GPT-4o in coding abilities. This model not only enhances coding capabilities but also maintains superiority in mathematics and general abilities, providing a comprehensive foundation for real-world applications like code assistance.

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-1.5B is a large language model in the Qwen2.5-Coder series, focusing on code generation, reasoning, and debugging. Built upon the robust Qwen2.5 architecture, this model has significantly expanded the training tokens to 5.5 trillion, incorporating source code, textual code bases, synthetic data, and more, making it a leader among open-source code LLMs, rivaling GPT-4o's coding capabilities. Moreover, Qwen2.5-Coder-1.5B has enhanced its mathematical and general capabilities, providing a more comprehensive foundation for practical applications such as code agents.

Qwen2.5-Coder is the latest series in the Qwen large language model family, focusing on code generation, code reasoning, and code fixing. Leveraging the powerful capabilities of Qwen2.5, the model was trained on 55 trillion source code, textual code bases, synthetic data, and more, making it a leader among open-source code generation language models, comparable in coding ability to GPT-4o. It not only enhances coding capability but also retains strengths in mathematics and general skills, providing a robust foundation for practical applications such as code agents.

Qwen2.5-Coder-3B is a large language model within the Qwen2.5-Coder series, emphasizing code generation, reasoning, and debugging. Built on the robust Qwen2.5 architecture, this model significantly improves code generation, reasoning, and debugging capabilities by increasing training tokens to 5.5 trillion, incorporating source code, textual code fundamentals, synthetic data, and more. The Qwen2.5-Coder-32B has become the most advanced open-source code language model available, matching the encoding capabilities of GPT-4o. Furthermore, Qwen2.5-Coder-3B provides a comprehensive foundation for real-world applications like code assistants, enhancing coding capabilities while maintaining strengths in mathematics and general comprehension.

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.

Qwen2.5-Coder-7B is a large language model based on Qwen2.5, focusing on code generation, reasoning, and correction. It has been trained on 5.5 trillion tokens, including source code, textual code grounding, synthetic data, etc., representing the latest advancements in open-source code language models. This model not only matches GPT-4o in programming capabilities but also retains advantages in mathematics and general skills, supporting long contexts of up to 128K tokens.

Qwen2.5-Coder-7B-Instruct is a large language model specifically designed for code, part of the Qwen2.5-Coder series which includes six mainstream model sizes: 0.5, 1.5, 3, 7, 14, and 32 billion parameters to meet the diverse needs of developers. This model shows significant improvements in code generation, reasoning, and debugging, trained on an extensive dataset of 5.5 trillion tokens that includes source code, code-related textual data, and synthetic data. The Qwen2.5-Coder-32B represents the latest advancement in open-source code LLMs, matching the coding capabilities of GPT-4o. Moreover, it supports long context lengths of up to 128K tokens, providing a solid foundation for practical applications like code agents.

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-32B is a code generation model based on Qwen2.5, featuring 32 billion parameters, making it one of the largest open-source code language models available today. It shows significant improvements in code generation, reasoning, and fixing, capable of handling long texts up to 128K tokens, which is suitable for practical applications such as code assistants. The model also maintains advantages in mathematical and general capabilities, supporting long text processing, thus serving as a powerful assistant for developers in code development.

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.