# Long Context Support

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-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.

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.