readme: ---
  license: bsd-3-clause
  ---
  # CodeGen (CodeGen-Multi 6B)
  
  ## Model description
  
  CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) 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](https://github.com/salesforce/CodeGen), 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.
  
  ## Training data
  
  This checkpoint (CodeGen-Multi 6B) was firstly initialized with *CodeGen-NL 6B*, 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](https://arxiv.org/abs/2203.13474) for more details.
  
  ## Evaluation results
  
  We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the [paper](https://arxiv.org/abs/2203.13474) 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.
  
  ## How to use
  
  This model can be easily loaded using the `AutoModelForCausalLM` functionality:
  
  ```python
  from transformers import AutoTokenizer, AutoModelForCausalLM
  tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-6B-multi")
  model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-6B-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))
  ```
  
  ## BibTeX entry and citation info
  
  ```bibtex
  @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}
  }
  ```
  