Qwen2.5-Coder-7B

A 7 billion parameter code generation model from the Qwen2.5-Coder series.

CommonProductProgrammingCode GenerationCode Reasoning

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.

Code Generation: Significantly enhances code generation capabilities
assisting developers in quickly implementing code logic.
Code Reasoning: Improves the model's understanding of code logic
increasing the efficiency of code review and optimization.
Code Correction: Automatically detects and rectifies errors in code
reducing debugging time.
Long Context Support: Supports contexts of up to 128K tokens
suitable for handling large codebases.
Based on Transformers Architecture: Utilizes advanced RoPE
SwiGLU

\The target audience includes developers and programmers
especially those dealing with large amounts of code and complex projects. Qwen2.5-Coder-7B enhances their development efficiency and code quality by providing powerful code generation
reasoning
and correction capabilities.\

Developers use Qwen2.5-Coder-7B for code auto-completion
enhancing coding speed.
During code reviews
the model's reasoning capabilities help identify potential code issues.
When maintaining large codebases

1. Visit the Hugging Face platform and search for the Qwen2.5-Coder-7B model.
2. Read the model card to understand the detailed information and usage conditions of the model.
3. Download or deploy the model directly on the platform based on project requirements.
4. Use the Hugging Face Transformers library to load the model and configure your environment.
5. Input code-related queries or commands

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