

Qwen2.5 Coder 1.5B Instruct GGUF
Overview :
Qwen2.5-Coder is the latest series of the Qwen large language model, specifically designed for code generation, reasoning, and debugging. Built on the powerful Qwen2.5 framework, it has scaled training tokens to 5.5 trillion, incorporating source code, text code bases, synthetic data, among others. Qwen2.5-Coder-32B has emerged as the most advanced open-source large language model for code, matching the coding capabilities of GPT-4o. This model is a 1.5B parameter instruction-tuned version in GGUF format, featuring causal language modeling, pre-training and post-training phases, and a transformers architecture.
Target Users :
This model is aimed at developers and programmers, particularly professionals who need to quickly generate, understand, and debug code in their projects. Qwen2.5-Coder enhances developer productivity, minimizes coding errors, and accelerates the development process by providing powerful code generation and reasoning capabilities.
Use Cases
Developers use Qwen2.5-Coder to auto-complete code, increasing coding efficiency.
During code reviews, leverage Qwen2.5-Coder to identify potential code flaws and errors.
In educational settings, Qwen2.5-Coder serves as a teaching tool, helping students understand and learn programming concepts.
Features
Code Generation: Significantly enhances code generation capabilities, including source code generation, text code bases, and synthetic data.
Code Reasoning: Improves the model's understanding of code logic and structure.
Code Debugging: Increases the model's ability to identify and fix errors and defects in code.
Comprehensive Application: Suitable for practical applications such as coding assistants, enhancing coding skills while maintaining mathematical and general capabilities.
Model Parameters: 1.54B parameters, with 1.31B non-embedding parameters, 28 layers, 12 attention heads for Q, and 2 for KV.
Context Length: Supports a full 32,768 tokens, making it one of the models capable of handling long sequences.
Quantization: Supports various quantization levels, including q2_K, q3_K_M, q4_0, q4_K_M, q5_0, q5_K_M, q6_K, q8_0.
How to Use
1. Install huggingface_hub and llama.cpp to download and run the model.
2. Use huggingface-cli to download the required GGUF file.
3. Follow the official guide to install llama.cpp and ensure you are using the latest version.
4. Launch the model using llama-cli and configure it with the specified command-line parameters.
5. Run the model in chat mode for a chatbot-like interactive experience.
6. Adjust parameters such as GPU memory and throughput as needed to fit different usage scenarios.
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