# Code Inference

Qwen2.5-Coder is the latest series in the Qwen large language model lineup, focusing on code generation, inference, and debugging. Built upon the powerful Qwen2.5 framework, this model was trained on 550 trillion sources, including source code, text code foundations, and synthetic data, making it the most advanced open-source code language model available today. It not only matches the programming capabilities of GPT-4o but also maintains an edge in mathematics and general competencies. The Qwen2.5-Coder-0.5B-Instruct-GPTQ-Int4 model features a 4-bit quantized instruction-tuned architecture, incorporating causal language modeling, pre-training and fine-tuning, as well as a transformers architecture.

Qwen2.5-Coder is the latest series of the Qwen large language model, focusing on code generation, inference, and debugging. Built on the powerful Qwen2.5, the Qwen2.5-Coder-32B has emerged as the state-of-the-art open-source code language model, matching GPT-4o in its coding capabilities. This model offers a comprehensive foundation for practical applications like code assistants, enhancing coding ability while maintaining strengths in mathematics and general skills.

Qwen2.5-Coder is the latest series in the Qwen large language model, specifically designed for code generation, inference, and debugging. The model is based on Qwen2.5, extending the training tokens to 5.5 trillion, incorporating source code, textual code foundations, synthetic data, etc. Qwen2.5-Coder-32B stands out as a top performer among open-source code LLMs, matching the encoding capabilities of GPT-4o. This model is a GPTQ-quantized 4-bit instruction-tuned 3B parameter Qwen2.5-Coder model, featuring causal language modeling, pre-training and post-training phases, and a transformers architecture.

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 represents a series of large language models optimized for code generation, covering six mainstream model sizes with 0.5, 1.5, 3, 7, 14, and 32 billion parameters, catering to the diverse needs of developers. Qwen2.5-Coder shows significant improvements in code generation, inference, and debugging, trained on a robust Qwen2.5 backbone with a token expansion to 5.5 trillion, including source code, text grounding, and synthetic data, making it one of the most advanced open-source code LLMs, with coding capabilities comparable to GPT-4o. Additionally, Qwen2.5-Coder offers a more comprehensive foundation for applications in real-world scenarios such as code agents.

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.