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Qwen2.5 Coder 7B
Overview :
Qwen2.5-Coder-7B is a large language model based on Qwen2.5, focusing on code generation, reasoning, and correction. It has been trained on 5.5 trillion tokens, including source code, textual code grounding, synthetic data, etc., representing the latest advancements in open-source code language models. This model not only matches GPT-4o in programming capabilities but also retains advantages in mathematics and general skills, supporting long contexts of up to 128K tokens.
Target Users :
The target audience includes developers and programmers, especially those dealing with large amounts of code and complex projects. Qwen2.5-Coder-7B enhances their development efficiency and code quality by providing powerful code generation, reasoning, and correction capabilities.
Use Cases
Developers use Qwen2.5-Coder-7B for code auto-completion, enhancing coding speed.
During code reviews, the model's reasoning capabilities help identify potential code issues.
When maintaining large codebases, leverage the model’s long-context support to manage complex code dependencies.
Features
Code Generation: Significantly enhances code generation capabilities, assisting developers in quickly implementing code logic.
Code Reasoning: Improves the model's understanding of code logic, increasing the efficiency of code review and optimization.
Code Correction: Automatically detects and rectifies errors in code, reducing debugging time.
Long Context Support: Supports contexts of up to 128K tokens, suitable for handling large codebases.
Based on Transformers Architecture: Utilizes advanced RoPE, SwiGLU, RMSNorm, and Attention QKV bias techniques.
Parameter Count: Contains 7.61 billion parameters, of which 6.53 billion are non-embedding parameters.
Layers and Attention Heads: Comprising 28 layers with 28 attention heads for Q and 4 for KV.
How to Use
1. Visit the Hugging Face platform and search for the Qwen2.5-Coder-7B model.
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2. Read the model card to understand the detailed information and usage conditions of the model.
3. Download or deploy the model directly on the platform based on project requirements.
4. Use the Hugging Face Transformers library to load the model and configure your environment.
5. Input code-related queries or commands, and the model will generate corresponding code or provide relevant reasoning.
6. Make necessary adjustments and optimizations based on the model's outputs.
7. Apply the generated or optimized code in actual projects to improve development efficiency.
8. Fine-tune the model as needed to fit specific development environments or requirements.
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