Qwen2.5-Coder-3B-Instruct-GPTQ-Int8

The 3B parameter Instruct model from the Qwen2.5-Coder series.

CommonProductProgrammingCode GenerationCode Reasoning

The Qwen2.5-Coder-3B-Instruct-GPTQ-Int8 is a large language model optimized for code generation, reasoning, and debugging, part of the Qwen2.5-Coder series. Based on Qwen2.5, it has been trained on a dataset including source code, code-text associations, and synthetic data, achieving 550 trillion training tokens. The Qwen2.5-Coder-32B has emerged as the leading open-source large language model for code, matching coding capabilities with GPT-4o. This model also provides a comprehensive foundation for real-world applications such as code assistance, augmenting coding capabilities while maintaining strengths in mathematics and general skills.

Code Generation: Significantly enhances code generation capability
assisting developers in quickly implementing code logic.
Code Reasoning: Improves the model's understanding of code logic
increasing the accuracy of code analysis.
Code Debugging: Automatically detects and fixes errors in code
enhancing code quality.
Full Parameter Coverage: Offers a range of model sizes from 50 million to 3.2 billion parameters to meet diverse developer needs.
GPTQ Quantization: Employs an 8-bit quantization technique to optimize model performance and memory usage.
Long Context Support: Supports a context length of up to 32
768 tokens

The target audience includes software developers
programming enthusiasts
and data scientists. This product is ideal for them as it offers powerful code assistance features that significantly improve programming efficiency and code quality
while also supporting the processing of long code snippets
making it suitable for complex coding tasks.

Developers use this model to generate code for sorting algorithms.
Data scientists utilize the model for large-scale code analysis and optimization.
Educators integrate the model into programming education to aid students in understanding and learning code logic.

1. Install the Hugging Face 'transformers' library
ensuring the version is at least 4.37.0.
2. Load the model and tokenizer from the Hugging Face Hub using AutoModelForCausalLM and AutoTokenizer.
3. Prepare an input prompt
such as writing a quicksort algorithm.

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