| | --- |
| | 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} |
| | } |
| | ``` |
