Qwen2.5-Coder-3B-Instruct-GGUF

Qwen2.5-Coder 3B is a parameter-tuned model within the Qwen2.5-Coder series.

CommonProductProgrammingCode GenerationCode Reasoning

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.

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

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.

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

1. Install huggingface_hub and llama.cpp to download and run the model.
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

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Monthly Visits

19075321

Bounce Rate

45.07%

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5.5

Visit Duration

00:05:32

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