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Qwen2.5 Coder 3B Instruct GPTQ Int8
Overview :
The Qwen2.5-Coder-3B-Instruct-GPTQ-Int8 is a large language model optimized for code generation, reasoning, and debugging, part of the Qwen2.5-Coder series. Based on Qwen2.5, it has been trained on a dataset including source code, code-text associations, and synthetic data, achieving 550 trillion training tokens. The Qwen2.5-Coder-32B has emerged as the leading open-source large language model for code, matching coding capabilities with GPT-4o. This model also provides a comprehensive foundation for real-world applications such as code assistance, augmenting coding capabilities while maintaining strengths in mathematics and general skills.
Target Users :
The target audience includes software developers, programming enthusiasts, and data scientists. This product is ideal for them as it offers powerful code assistance features that significantly improve programming efficiency and code quality, while also supporting the processing of long code snippets, making it suitable for complex coding tasks.
Use Cases
Developers use this model to generate code for sorting algorithms.
Data scientists utilize the model for large-scale code analysis and optimization.
Educators integrate the model into programming education to aid students in understanding and learning code logic.
Features
Code Generation: Significantly enhances code generation capability, assisting developers in quickly implementing code logic.
Code Reasoning: Improves the model's understanding of code logic, increasing the accuracy of code analysis.
Code Debugging: Automatically detects and fixes errors in code, enhancing code quality.
Full Parameter Coverage: Offers a range of model sizes from 50 million to 3.2 billion parameters to meet diverse developer needs.
GPTQ Quantization: Employs an 8-bit quantization technique to optimize model performance and memory usage.
Long Context Support: Supports a context length of up to 32,768 tokens, suitable for handling lengthy code snippets.
Multi-Language Support: Primarily supports English, ideal for international development environments.
Open Source: The model is open-source, facilitating community contributions and further research.
How to Use
1. Install the Hugging Face 'transformers' library, ensuring the version is at least 4.37.0.
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2. Load the model and tokenizer from the Hugging Face Hub using AutoModelForCausalLM and AutoTokenizer.
3. Prepare an input prompt, such as writing a quicksort algorithm.
4. Use the tokenizer.apply_chat_template method to process the input message and generate model input.
5. Pass the generated model input to the model and set the max_new_tokens parameter to control the length of the generated code.
6. After the model generates the code, use the tokenizer.batch_decode method to convert the generated tokens back to text.
7. Test and debug the generated code as necessary.
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