InCoder: A Revolutionary Code Generation Model
InCoder, developed by dpfried, is a cutting-edge generative model designed for code infilling and synthesis. This model offers a powerful solution for developers looking to enhance their coding efficiency and productivity.
Core Features:
- Trained with advanced techniques to generate high-quality code.
- Utilizes HuggingFace's transformers library for seamless integration and usage.
- Offers two model options with different parameter sizes for flexibility.
Basic Usage: To use InCoder, users need to install the required dependencies such as pytorch, tokenizers, and transformers. The model can be obtained from HuggingFace's hub, and a custom tokenizer is used for optimal performance. Users can refer to the provided example scripts to understand how to leverage the infilling capability of the model and perform batched generation.
InCoder represents a significant advancement in the field of code generation, providing developers with a valuable tool to streamline their coding processes and create more efficient and effective code.
