Qwen2.5-Coder-0.5B-Instruct

Instruction-tuned 0.5B parameter code generation model in the Qwen2.5-Coder series

CommonProductProgrammingCode GenerationCode Reasoning

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.

Code Generation: Significantly boosts code generation capabilities
helping developers quickly accomplish programming tasks.
Code Reasoning: Enhances the model's understanding of code logic
improving the accuracy of code analysis.
Code Fixing: Assists developers in identifying and correcting errors in code
enhancing code quality.
Comprehensive Technical Foundation: Suitable for various real-world applications
such as code assistance.
Pre-training and Fine-tuning: The model undergoes pre-training and fine-tuning to meet diverse development needs.
High-Performance Architecture: Employs a transformer architecture

\Target audience includes developers
programming enthusiasts
and software engineers. Qwen2.5-Coder enhances their programming efficiency and code quality by providing strong capabilities for code generation
reasoning
and fixing

A developer uses Qwen2.5-Coder to generate code for a quicksort algorithm.
A software engineer employs the model to fix errors in existing code
enhancing project stability.
A programming enthusiast learns coding best practices through the model
improving personal programming skills.

1. Visit the Hugging Face website and search for the Qwen2.5-Coder-0.5B-Instruct model.
2. Import AutoModelForCausalLM and AutoTokenizer based on the code examples provided on the page.
3. Load the model and tokenizer using the model name: model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name).
4. Prepare input prompts

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Monthly Visits

19075321

Bounce Rate

45.07%

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5.5

Visit Duration

00:05:32

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