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Qwen2.5 Coder 1.5B Instruct
Qwen2.5-Coder-1.5B-Instruct
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Qwen2.5 Coder 1.5B Instruct
Coding AssistantDevelopment & Tools#Code Generation#Code Reasoning#Code Fixing#Programming Assistance#Open Source ModelStandard PicksOpen Source
Overview :
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.
Target Users :
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.
Total Visits： 29.7M
Top Region： US(17.94%)
Website Views ： 48.6K
Use Cases
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.
Features
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.
Long Context Support: Capable of handling context lengths of up to 32,768 tokens, suitable for processing long code snippets.
How to Use
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'.
5. Use the tokenizer's apply_chat_template method to process the input prompt and generate model input.
6. Call the model's generate method to produce code.
7. Use the tokenizer's batch_decode method to convert the generated code IDs back into text format.
8. Review the generated code and make adjustments or optimizations as necessary.
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