Qwen2.5-Coder-1.5B-Instruct

This is a 1.5B parameter code generation model in the Qwen2.5-Coder series.

CommonProductProgrammingCode GenerationCode Reasoning

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.

Code Generation: Significantly enhances code generation capabilities
supporting multiple programming languages.
Code Reasoning: Capable of understanding and reasoning about code logic and functionality.
Code Fixing: Identifies and rectifies errors and defects in code.
Support for Source Code and Textual Code Bases: Trained on extensive datasets to enhance model generalization.
Synthetic Data Training: Improves model robustness and adaptability through synthetic data.
Fully Parameterized: 1.54B parameters
offering richer model expressive power.
Multi-Layer and Multi-Head Attention Mechanism: 28 layers with 12 query heads and 2 key-value heads
enhancing the model's deep learning capabilities.

The target audience includes developers and programmers
especially professionals who need to quickly generate
understand
and fix code for their projects. Qwen2.5-Coder significantly enhances development efficiency and reduces coding errors
making it a valuable assistant for developers.

Developers use Qwen2.5-Coder to quickly generate code for sorting algorithms.
Software engineers utilize the model to fix bugs in existing codebases.
In programming education
teachers use the model to provide code examples and exercises for students.

1. Visit the Hugging Face website and search for the Qwen2.5-Coder-1.5B-Instruct model.
2. Import AutoModelForCausalLM and AutoTokenizer based on the code examples provided on the page.
3. Set the model name to 'Qwen/Qwen2.5-Coder-1.5B-Instruct' and load the model and tokenizer.
4. Prepare input prompts
such as 'write a quick sort algorithm'.

Visit

Qwen2.5-Coder-1.5B-Instruct Visit Over Time

Monthly Visits

20899836

Bounce Rate

46.04%

Page per Visit

5.2

Visit Duration

00:04:57

Qwen2.5-Coder-1.5B-Instruct Visit Trend

Qwen2.5-Coder-1.5B-Instruct Visit Geography

Qwen2.5-Coder-1.5B-Instruct Traffic Sources

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Qwen2.5-Coder-1.5B-Instruct Visit Over Time

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