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Qwen2.5 Coder 3B Instruct GGUF
Overview :
Qwen2.5-Coder is the latest series of the Qwen large language models, focusing on code generation, reasoning, and repair. Built on the powerful Qwen2.5, it has been trained on a dataset of 550 trillion tokens including source code, code-grounded texts, and synthetic data. Qwen2.5-Coder-32B has emerged as the most advanced open-source code large language model, matching the coding capabilities of GPT-4o. In practical applications, it provides a more comprehensive foundation, such as a code agent, enhancing coding prowess while retaining advantages in math and general abilities.
Target Users :
The target audience includes developers, programming enthusiasts, and software engineers. Qwen2.5-Coder-3B-Instruct-GGUF is particularly well-suited for large projects that require handling complex code logic, optimizing code, and maintaining high standards.
Use Cases
Developers utilize Qwen2.5-Coder-3B-Instruct-GGUF to generate new code modules, enhancing development efficiency.
Software engineers leverage the model to fix existing errors in code, reducing debugging time.
Programming enthusiasts learn coding best practices through the model, improving their programming skills.
Features
Code Generation: Significantly boosts code generation capabilities, aiding developers in rapidly implementing code logic.
Code Reasoning: Enhances the model's understanding of code logic, improving the accuracy of code analysis.
Code Repair: Assists developers in identifying and fixing errors within code, elevating code quality.
Support for Long Sequence Processing: Supports context lengths of up to 32,768 tokens, suitable for managing large codebases.
Multiple Quantized Versions: Offers quantized versions ranging from 2-bit to 8-bit, catering to varying performance and resource needs.
Transformers Architecture: Utilizes advanced technologies like RoPE, SwiGLU, and RMSNorm to enhance model performance.
Open Source: The model is open source, facilitating community contributions and further research advancements.
How to Use
1. Install huggingface_hub and llama.cpp to download and run the model.
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2. Use huggingface-cli to download the necessary GGUF files.
3. Clone the llama.cpp repository following the documentation and install it according to the official guidelines.
4. Launch the model using llama-cli and set appropriate parameters for an interactive chat experience.
5. Adjust parameters as needed, such as token count and GPU memory usage, to optimize performance.
6. Utilize the model to generate code, reason through code logic, or fix code errors.
7. Engage in community discussions, contribute code, or further develop based on the model's outputs.
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