CodeV is a series of open-source instruction-tuned large language models (LLMs) designed for Verilog generation. The paper introduces CodeV, which addresses the challenges of generating high-quality Verilog code using multi-level summarization. The key idea is to use real-world Verilog code to generate high-quality descriptions, which are then used to fine-tune LLMs for Verilog generation. This approach outperforms previous open-source and commercial models in Verilog generation tasks. The paper first collects high-quality Verilog code from GitHub, filters it, and then uses multi-level summarization to generate high-level descriptions. These descriptions are then used to fine-tune base LLMs, resulting in CodeV models. The CodeV models are evaluated on two benchmarks: VerilogEval and RTLLM. The results show that CodeV outperforms previous models by significant margins, achieving state-of-the-art results. The paper also discusses the challenges of Verilog generation, including the scarcity of high-quality instruction-tuning data and the difficulty of obtaining high-quality Verilog responses from advanced LLMs. The proposed method addresses these challenges by using real-world Verilog code to generate high-quality descriptions, which are then used to fine-tune LLMs for Verilog generation. The paper also presents an ablation study on the effectiveness of multi-level summarization and evaluates the impact of data size on model performance. The results show that multi-level summarization significantly improves model performance, and larger datasets lead to better model performance. The paper concludes that CodeV is a significant advancement in Verilog generation, demonstrating the effectiveness of the proposed method and the potential of instruction-tuned LLMs in this domain. The authors also acknowledge the limitations of their approach, including the inability to generate complex circuits without additional frameworks and the lack of circuit optimization capabilities. However, they believe that their approach can be integrated with other methods to further enhance the performance of Verilog generation models.CodeV is a series of open-source instruction-tuned large language models (LLMs) designed for Verilog generation. The paper introduces CodeV, which addresses the challenges of generating high-quality Verilog code using multi-level summarization. The key idea is to use real-world Verilog code to generate high-quality descriptions, which are then used to fine-tune LLMs for Verilog generation. This approach outperforms previous open-source and commercial models in Verilog generation tasks. The paper first collects high-quality Verilog code from GitHub, filters it, and then uses multi-level summarization to generate high-level descriptions. These descriptions are then used to fine-tune base LLMs, resulting in CodeV models. The CodeV models are evaluated on two benchmarks: VerilogEval and RTLLM. The results show that CodeV outperforms previous models by significant margins, achieving state-of-the-art results. The paper also discusses the challenges of Verilog generation, including the scarcity of high-quality instruction-tuning data and the difficulty of obtaining high-quality Verilog responses from advanced LLMs. The proposed method addresses these challenges by using real-world Verilog code to generate high-quality descriptions, which are then used to fine-tune LLMs for Verilog generation. The paper also presents an ablation study on the effectiveness of multi-level summarization and evaluates the impact of data size on model performance. The results show that multi-level summarization significantly improves model performance, and larger datasets lead to better model performance. The paper concludes that CodeV is a significant advancement in Verilog generation, demonstrating the effectiveness of the proposed method and the potential of instruction-tuned LLMs in this domain. The authors also acknowledge the limitations of their approach, including the inability to generate complex circuits without additional frameworks and the lack of circuit optimization capabilities. However, they believe that their approach can be integrated with other methods to further enhance the performance of Verilog generation models.