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# Code Fixing
Qwen2.5 Coder 1.5B Instruct AWQ
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.
Code Reasoning
44.7K
Qwen2.5 Coder 0.5B Instruct
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.
Coding Assistant
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Qwen2.5 Coder 1.5B Instruct
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.
Coding Assistant
48.9K
Qwen2.5 Coder 32B
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.
Coding Assistant
48.3K
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