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

Qwen2.5 Coder 0.5B Instruct

Coding Assistant Development & Tools #Code Generation #Code Reasoning #Code Fixing #Programming Assistance #Transformers #Pre-trained Models Standard Picks Open Source

Overview :

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.

Target Users :

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, especially when handling large amounts of code and complex projects, offering effective technical support and assistance.

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Top Region： US(17.94%)

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Use Cases

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.

Features

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, incorporating advanced techniques like RoPE, SwiGLU, and RMSNorm.

Long Context Support: Supports context lengths of up to 32,768 tokens, ideal for managing complex programming tasks.

How to Use

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, such as a specific coding request.

5. Process the input message using the tokenizer.apply_chat_template method to generate model input.

6. Call the model.generate method to produce code.

7. Use tokenizer.batch_decode to convert the generated code IDs into text, obtaining the final code output.