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Qwen2.5 Coder 3B Instruct GPTQ Int4
Overview :
Qwen2.5-Coder is the latest series in the Qwen large language model, specifically designed for code generation, inference, and debugging. The model is based on Qwen2.5, extending the training tokens to 5.5 trillion, incorporating source code, textual code foundations, synthetic data, etc. Qwen2.5-Coder-32B stands out as a top performer among open-source code LLMs, matching the encoding capabilities of GPT-4o. This model is a GPTQ-quantized 4-bit instruction-tuned 3B parameter Qwen2.5-Coder model, featuring causal language modeling, pre-training and post-training phases, and a transformers architecture.
Target Users :
The target audience includes software developers, programming enthusiasts, and researchers needing code generation, inference, and debugging. The Qwen2.5-Coder series models significantly enhance programming efficiency, reduce development time, and assist in solving complex programming issues, making them valuable aides for developers and researchers.
Use Cases
Developers using Qwen2.5-Coder to generate code for a sorting algorithm.
Researchers leveraging the model for logical reasoning and analysis of specific code snippets.
Software engineers employing the model to fix bugs in existing code, enhancing code stability.
Features
Code Generation: Significantly enhances code generation capabilities, helping developers achieve programming tasks quickly.
Code Inference: Improves the model's understanding of code logic, enhancing code analysis and inference abilities.
Code Debugging: Assists developers in diagnosing and fixing errors in code, improving code quality.
Comprehensive Parameter Coverage: Offers models with parameter sizes ranging from 0.5 to 3.2 billion, catering to different developer needs.
Real-World Application Foundation: Strengthens the model's performance in mathematical and general capabilities, laying a solid foundation for practical applications.
GPTQ 4-bit Quantization: Optimizes model performance, reduces hardware requirements, and enhances deployment flexibility.
Long Text Support: Supports a context length of up to 32,768 tokens, suitable for handling long code snippets.
How to Use
1. Visit the Hugging Face platform and search for the Qwen2.5-Coder-3B-Instruct-GPTQ-Int4 model.
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2. Import necessary libraries as per the code examples provided on the page, such as transformers and torch.
3. Load the model and tokenizer using AutoModelForCausalLM and AutoTokenizer.from_pretrained methods.
4. Prepare the input prompt, such as a requirement to write a sorting algorithm.
5. Use the tokenizer.apply_chat_template method to convert the input prompt into a format understandable by the model.
6. Pass the transformed input to the model and call the model.generate method to generate code.
7. Obtain the generated code IDs and use tokenizer.batch_decode method to convert the IDs back into text format.
8. Analyze the generated code and make adjustments as needed or use it directly.
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