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Qwen2.5 Coder 32B Instruct GPTQ Int8
Overview :
Qwen2.5-Coder-32B-Instruct-GPTQ-Int8 is a large language model specifically optimized for code generation within the Qwen series, featuring 3.2 billion parameters and supporting long text processing. It is one of the most advanced models in the field of open-source code generation. The model has been further trained and optimized based on Qwen2.5, showing significant improvements in code generation, inference, and debugging, while also maintaining strengths in mathematics and general capabilities. It utilizes GPTQ 8-bit quantization technology to reduce model size and enhance operational efficiency.
Target Users :
The target audience includes developers, coding enthusiasts, and teams that require code generation and optimization. This model, known for its powerful code generation and understanding capabilities, is particularly suited for software development projects that demand rapid development and code quality assurance.
Use Cases
Developers can quickly generate code for sorting algorithms using this model.
During code review, leverage the model's reasoning abilities to improve code quality.
When encountering complex bugs, utilize the model to assist in locating and fixing coding issues.
Features
Code Generation: Significantly enhances code generation capabilities, matching the coding performance of GPT-4o.
Code Inference: Improves code understanding, aiding developers in better understanding and optimizing their code.
Code Debugging: Assists developers in identifying and fixing errors in code.
Long Text Support: Handles long text up to 128K tokens.
Quantization Technology: Employs GPTQ 8-bit quantization to reduce model size and enhance efficiency.
High Performance: Adaptable to various development needs while maintaining high performance.
Open Source: Available as an open-source model for developers to use and further explore.
How to Use
1. Visit the Hugging Face website and search for the Qwen2.5-Coder-32B-Instruct-GPTQ-Int8 model.
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2. Import the necessary libraries and modules as demonstrated in the code samples on the page.
3. Load the model and tokenizer, then prepare the input prompt.
4. Convert the prompt into an input format that the model can understand.
5. Use the model to generate code or perform code inference.
6. Retrieve the model's output and process or utilize the generated code as needed.
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