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Qwen2.5 Coder 32B Instruct GPTQ Int4
Overview :
The Qwen2.5-Coder-32B-Instruct-GPTQ-Int4 is a large language model based on Qwen2.5, featuring 3.25 billion parameters and supporting long text processing with a maximum of 128K tokens. This model has shown significant improvements in code generation, code inference, and code repair, making it a leader among current open-source code language models. It not only enhances coding capabilities but also maintains strengths in mathematics and general reasoning.
Target Users :
The target audience includes developers and programmers, particularly professionals who need to write, understand, and maintain code. This model assists them in rapidly generating code, enhancing development efficiency, minimizing errors, and tackling complex programming tasks.
Use Cases
Developers use this model to generate code for sorting algorithms.
Software engineers utilize the model to fix errors in existing code.
In programming education, teachers use the model to explain code logic as a teaching aid.
Features
Code Generation: Significantly improves code generation capabilities, matching the coding proficiency of GPT-4o.
Code Inference: Enhances understanding of code logic and structure.
Code Repair: Improves the ability to identify and fix errors or vulnerabilities in code.
Long Text Support: Capable of processing long texts up to 128K tokens.
4-bit Quantization: Optimizes model performance and efficiency through GPTQ technology for 4-bit quantization.
Multilingual Support: Primarily supports English, suitable for coding and related tasks.
Pretraining and Post-training: Combines both pretraining and post-training phases to enhance model performance.
How to Use
1. Visit the Hugging Face website and search for the Qwen2.5-Coder-32B-Instruct-GPTQ-Int4 model.
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2. Import the necessary libraries and modules based on the code examples provided on the page.
3. Load the model and the tokenizer using AutoModelForCausalLM and AutoTokenizer.
4. Prepare input prompts, such as requests for specific functionality code.
5. Generate code or responses using the model.
6. Handle the model output by decoding the generated code or text.
7. Adjust model parameters, such as the maximum number of new tokens, to optimize the output.
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