Qwen2.5-Coder-7B-Instruct

A language model for code generation with 7B parameters.

CommonProductProgrammingCode GenerationCode Reasoning

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.

Code Generation: Significantly enhances code generation capabilities and supports multiple programming languages.
Code Reasoning: Boosts understanding of code logic to assist developers in reasoning through their code.
Code Debugging: Automatically detects and fixes errors in code.
Long Context Support: Capable of handling long contexts of up to 128K tokens
ideal for large codebases.
Based on Transformers Architecture: Employs advanced techniques such as RoPE
SwiGLU
RMSNorm
and Attention QKV bias.
Parameter Count: Contains 7.61B parameters

\This model is targeted at developers and programmers
especially those handling large amounts of code and complex projects. Qwen2.5-Coder-7B-Instruct provides advanced capabilities in code generation
reasoning
and debugging
enhancing their development efficiency and code quality.\

A developer uses Qwen2.5-Coder-7B-Instruct to generate code for a quicksort algorithm.
A software engineer utilizes the model to fix bugs in an existing codebase.
A data scientist employs the model to generate code for data processing and analysis.

1. Visit the Hugging Face platform and locate the Qwen2.5-Coder-7B-Instruct model.
2. Import AutoModelForCausalLM and AutoTokenizer as per the code snippets provided on the page.
3. Load the model and tokenizer using the model name.
4. Prepare an input prompt
such as a request to write code for a specific function.

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Qwen2.5-Coder-7B-Instruct Visit Over Time

Monthly Visits

19075321

Bounce Rate

45.07%

Page per Visit

5.5

Visit Duration

00:05:32

Qwen2.5-Coder-7B-Instruct Visit Trend

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