Enhance model card for VeriCoder: Add paper, GitHub link, pipeline tag, and library name

README.md

@@ -1,10 +1,40 @@
 ---
-license: apache-2.0
-datasets:
-- LLM4Code/expanded_origen_126k
 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}
+}
+```