Qwen2.5-Coder-14B-Instruct-AWQ

An open-source large language model focused on code generation and reasoning.

CommonProductProgrammingCode GenerationCode Reasoning

Qwen2.5-Coder is a series of large language models specifically designed for coding, ranging from 0.5 billion to 3.2 billion parameters to meet various developer needs. The model shows significant improvements in code generation, reasoning, and repair, leveraging a powerful Qwen2.5 framework trained on 5.5 trillion tokens, including source code, textual code bases, and synthetic data. Qwen2.5-Coder-32B is currently the most advanced open-source large language model for code generation, matching the coding capabilities of GPT-4o. Additionally, the model supports long contexts of up to 128K tokens and employs AWQ 4-bit quantization technology to enhance its efficiency and performance.

Code generation: Significantly improves code generation capabilities
helping developers quickly implement 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 in the code.
Long context support: Supports long contexts of up to 128K tokens
suitable for handling large codebases.
AWQ 4-bit quantization: Increases operational efficiency and reduces resource consumption.
Pre-training and fine-tuning: The model undergoes pre-training and fine-tuning to optimize performance.
Diverse encoding techniques: Utilizes a transformers architecture that includes RoPE

The target audience includes developers and programmers
especially those who need to handle large amounts of code and complex projects. Qwen2.5-Coder-14B-Instruct-AWQ enhances their development efficiency
reduces errors
and optimizes code quality by providing robust capabilities for code generation
reasoning

Developers use Qwen2.5-Coder to generate code for sorting algorithms.
When managing large software projects
the long context support is utilized to maintain code coherence.
During code reviews
the code repair feature is used to identify and fix potential bugs.

1. Visit the Hugging Face website and search for the Qwen2.5-Coder-14B-Instruct-AWQ model.
2. Import AutoModelForCausalLM and AutoTokenizer using the code snippets provided on the page.
3. Load the model and tokenizer using the model name.
4. Prepare input prompts
such as writing a quicksort algorithm.

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