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