--- base_model: - Qwen/Qwen2.5-14B-Instruct datasets: - LLM4Code/expanded_origen_126k license: apache-2.0 tags: - Verilog - CodeGen pipeline_tag: text-generation library_name: transformers --- # VeriCoder: Enhancing LLM-Based RTL Code Generation through Functional Correctness Validation This repository hosts **VeriCoder**, a model presented in the paper [VeriCoder: Enhancing LLM-Based RTL Code Generation through Functional Correctness Validation](https://huggingface.co/papers/2504.15659). VeriCoder is a model for Register Transfer Level (RTL) code generation fine-tuned on a dataset validated for functional correctness. This fine-tuning dataset is constructed using a novel methodology that combines unit test generation with feedback-directed refinement. Given a natural language specification and an initial RTL design, a teacher model iteratively revises the RTL design based on simulation results using generated tests. Every example in the dataset is functionally validated, consisting of a natural language description, an RTL implementation, and passing tests. For more details and code, visit the [GitHub Repository](https://github.com/Anjiang-Wei/VeriCoder). ## Key Highlights - **Functionally Validated Dataset**: 125,000+ examples with simulation-passing RTL designs. - **Feedback-Driven Construction**: Iteratively refine designs and tests based on test results. - **Superior Performance**: Achieves up to +71.7% relative improvement on VerilogEval benchmarks. - **Comprehensive Resources**: Includes dataset, model weights, inference scripts, and training pipeline. ## Citation If you find VeriCoder helpful in your research, please consider citing: ```plaintext @article{wei2025vericoder, title={VeriCoder: Enhancing LLM-Based RTL Code Generation through Functional Correctness Validation}, author={Wei, Anjiang and Tan, Huanmi and Suresh, Tarun and Mendoza, Daniel and Teixeira, Thiago SFX and Wang, Ke and Trippel, Caroline and Aiken, Alex}, journal={arXiv preprint arXiv:2504.15659}, year={2025} } ```