Codegen 16B Multi

🚀 CodeGen (CodeGen-Multi 16B)

CodeGen is a family of autoregressive language models designed for program synthesis. It can extract features from natural and programming language texts and calculate their likelihood. The model is particularly effective at generating executable code from English prompts.

✨ Features

• Multiple Variants: Available in 3 pre - training data variants (NL, Multi, Mono) and 4 model size variants (350M, 2B, 6B, 16B).
• Cross - Language Support: The "Multi" variant is pre - trained on multiple programming languages, including C, C++, Go, Java, JavaScript, and Python.

📦 Installation

This model can be easily loaded using the AutoModelForCausalLM functionality:

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-16B-multi")
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-16B-multi")

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-16B-multi")
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-16B-multi")

text = "def hello_world():"
input_ids = tokenizer(text, return_tensors="pt").input_ids

generated_ids = model.generate(input_ids, max_length=128)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))

📚 Documentation

Model description

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 16B in the paper, where "Multi" means the model is initialized with CodeGen - NL 16B and further pre - trained on a dataset of multiple programming languages, and "16B" refers to the number of trainable parameters.

Training data

This checkpoint (CodeGen - Multi 16B) was firstly initialized with CodeGen - NL 16B, and then pre - trained on [BigQuery](https://console.cloud.google.com/marketplace/details/github/github - repos), 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.

Training procedure

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.

Evaluation results

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

Intended Use and Limitations

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.

Ethical Considerations

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.

🔧 Technical Details

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.

📄 License

This project is licensed under the BSD 3 - Clause License.

BibTeX entry and citation info

@article{Nijkamp2022ACP,
  title={A Conversational Paradigm for Program Synthesis},
  author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming},
  journal={arXiv preprint},
  year={2022}
}