The paper introduces CodeV, a series of instruction-tuned large language models (LLMs) designed for generating Verilog code. The authors observe that while LLMs excel in summarizing Verilog code, they struggle with generating it directly due to the scarcity of high-quality instruction tuning data. To address this, CodeV employs a multi-level summarization method, where Verilog code is first summarized into high-level descriptions by GPT-3.5, and then these descriptions are used to fine-tune the base LLMs. Experimental results show that CodeV outperforms previous state-of-the-art (SOTA) models by significant margins, achieving 14.4% and 11.3% improvements over BetterV and RTLcoder, respectively, on the VerilogEval benchmark, and a 22.1% improvement over GPT-4 on the RTLLM benchmark. The paper also discusses the limitations of CodeV, such as its inability to generate complex circuits without a framework and its lack of circuit optimization capabilities.The paper introduces CodeV, a series of instruction-tuned large language models (LLMs) designed for generating Verilog code. The authors observe that while LLMs excel in summarizing Verilog code, they struggle with generating it directly due to the scarcity of high-quality instruction tuning data. To address this, CodeV employs a multi-level summarization method, where Verilog code is first summarized into high-level descriptions by GPT-3.5, and then these descriptions are used to fine-tune the base LLMs. Experimental results show that CodeV outperforms previous state-of-the-art (SOTA) models by significant margins, achieving 14.4% and 11.3% improvements over BetterV and RTLcoder, respectively, on the VerilogEval benchmark, and a 22.1% improvement over GPT-4 on the RTLLM benchmark. The paper also discusses the limitations of CodeV, such as its inability to generate complex circuits without a framework and its lack of circuit optimization capabilities.