CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in this repository, under 3 pre-training data variants (NL, Multi, Mono) and 4 model size variants (350M, 2B, 6B, 16B).

The checkpoint included in this repository is denoted as CodeGen-Multi 6B in the paper, where "Multi" means the model is initialized with CodeGen-NL 6B and further pre-trained on a dataset of multiple programming languages, and "6B" refers to the number of trainable parameters.

This checkpoint (CodeGen-Multi 6B) was firstly initialized with CodeGen-NL 6B, and then pre-trained on BigQuery, a large-scale dataset of multiple programming languages from GitHub repositories. The data consists of 119.2B tokens and includes C, C++, Go, Java, JavaScript, and Python.

CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs. The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism. See Section 2.3 of the paper for more details.

We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the paper for more details.

As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. However, the model is intended for and best at program synthesis, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well.

This model can be easily loaded using the AutoModelForCausalLM functionality:

This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.