👨‍💻 Awesome Code LLM

• 🧵 Table of Contents
• 🚀 Leaderboard
• 📚 Paper
  • ▶️ Pre-Training
  • ▶️ Instruction Tuning
  • ▶️ Alignment with Feedback
  • ▶️ Prompting
  • ▶️ Evaluation & Benchmark
  • ▶️ Using LLMs while coding
• 🙌 Contributors
• Cite as
• Acknowledgement
• Star History

Leaderboard (Sort by HumanEval Pass@1)

💡 Toolkit:

• bigcode-evaluation-harness: A framework for the evaluation of autoregressive code generation language models.
• multilingual-code-evals: Multilingual Code Models Evaluation.

1. Evaluating Large Language Models Trained on Code Preprint

   [Paper] Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto. et al. 2021.07

2. CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis ICLR23

   [Paper] Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. 2022.03

3. SantaCoder: don't reach for the stars! Preprint

   [Paper] Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff. et al. 2023.01

4. CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X Preprint

   [Paper] Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, Jie Tang. 2023.03

5. CodeGen2: Lessons for Training LLMs on Programming and Natural Languages ICLR23

   [Paper] Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, Yingbo Zhou. 2023.05

6. StarCoder: may the source be with you! Preprint

   [Paper] Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou. et al. 2023.05

7. CodeT5+: Open Code Large Language Models for Code Understanding and Generation Preprint

   [Paper] Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi D.Q. Bui, Junnan Li, Steven C.H. Hoi. 2023.05

8. Textbooks Are All You Need Preprint

   [Paper] Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi. et al. 2023.06

9. Code Llama: Open Foundation Models for Code Preprint

   [Paper] Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat. et al. 2023.08

1. WizardCoder: Empowering Code Large Language Models with Evol-Instruct Preprint

   [Paper] Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, Daxin Jiang. 2023.07

2. OctoPack: Instruction Tuning Code Large Language Models Preprint

   [Paper][Repo] Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, Shayne Longpre. 2023.08

1. CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning NeurIPS22

   [Paper] Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, Steven C.H. Hoi. 2022.07

2. Execution-based Code Generation using Deep Reinforcement Learning TMLR23

   [Paper] Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, Chandan K. Reddy. 2023.01

3. RLTF: Reinforcement Learning from Unit Test Feedback Preprint

   [Paper] Jiate Liu, Yiqin Zhu, Kaiwen Xiao, Qiang Fu, Xiao Han, Wei Yang, Deheng Ye. 2023.07

4. PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback Preprint

   [Paper] Bo Shen, Jiaxin Zhang, Taihong Chen, Daoguang Zan, Bing Geng, An Fu, Muhan Zeng, Ailun Yu, Jichuan Ji, Jingyang Zhao, Yuenan Guo, Qianxiang Wang. 2023.07

1. CodeT: Code Generation with Generated Tests ICLR23

   [Paper] Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen. 2022.07

2. Coder Reviewer Reranking for Code Generation ICML23

   [Paper] Tianyi Zhang, Tao Yu, Tatsunori B Hashimoto, Mike Lewis, Wen-tau Yih, Daniel Fried, Sida I Wang. 2022.11

3. LEVER: Learning to Verify Language-to-Code Generation with Execution ICML23

   [Paper] Ansong Ni, Srini Iyer, Dragomir Radev, Ves Stoyanov, Wen-tau Yih, Sida I. Wang, Xi Victoria Lin. 2023.02

4. Teaching Large Language Models to Self-Debug Preprint

   [Paper] Xinyun Chen, Maxwell Lin, Nathanael Schärli, Denny Zhou. 2023.06

5. Demystifying GPT Self-Repair for Code Generation Preprint

   [Paper] Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, Armando Solar-Lezama. 2023.06

6. SelfEvolve: A Code Evolution Framework via Large Language Models Preprint

   [Paper] Shuyang Jiang, Yuhao Wang, Yu Wang. 2023.06

1. Measuring Coding Challenge Competence With APPS NeurIPS21

   Named APPS

   [Paper][Repo] Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, Jacob Steinhardt. 2021.05

2. Program Synthesis with Large Language Models Preprint

   Named MBPP

   [Paper] Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, Charles Sutton. 2021.08

3. DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation ICML23

   [Paper] Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Scott Wen-tau Yih, Daniel Fried, Sida Wang, Tao Yu. 2022.11

4. RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems Preprint

   [Paper] Tianyang Liu, Canwen Xu, Julian McAuley. 2023.06

5. Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generation Preprint

   [Paper] Li Zhong, Zilong Wang. 2023.08

1. Awesome-DevAI: A list of resources about using LLMs while building software Awesome

   [Repo] Ty Dunn, Nate Sesti. 2023.10

This is an active repository and your contributions are always welcome! If you have any question about this opinionated list, do not hesitate to contact me [email protected].

@software{awesome-code-llm,
  author = {Binyuan Hui},
  title = {An awesome and curated list of best code-LLM for research},
  howpublished = {\url{https://github.com/huybery/Awesome-Code-LLM}},
  year = 2023,
}

This project is inspired by Awesome-LLM.

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