According to the webmaster's home report on August 1, researchers from Huawei Cloud, the Chinese Academy of Sciences and Peking University recently proposed a new framework called RRTF (Rank Responses to align Test&_Teacher Feedback), which can effectively improve the pre-trained large-scale Performance of language models (LLMs) for code generation. The RRTF framework improves the performance of code-generating LLMs by means of natural language LLM alignment techniques and ranking feedback. The research team also introduced the PanGu-Coder2 model, which achieved an excellent 62.20% pass rate on the OpenAI Human_ benchmark. This study demonstrates the effectiveness of RRTF by applying the RRTF framework on StarCoder15B, surpassing PanGu-Coder and achieving the best performance among all recorded code LLMs. A thorough analysis of three benchmarks (Human_, Coder_, and LeetCode) shows that Code LLM may be able to outperform natural language models of equal or larger scale in code generation tasks. Research also highlights the value of high-quality data in improving a model's ability to follow instructions and write code.