| | --- |
| | license: bsd-3-clause |
| | --- |
| | # CodeGen (CodeGen-Multi 16B) |
| |
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| | ## Model description |
| |
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| | 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`). |
| |
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| | 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. |
| |
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| | ## Training data |
| |
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| | 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. |
| |
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| | ## Training procedure |
| |
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| | 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. |
| |
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| | ## Evaluation results |
| |
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| | 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. |
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| | ## Intended Use and Limitations |
| |
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| | 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 |
| |
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| | This model can be easily loaded using the `AutoModelForCausalLM` functionality: |
| |
|
| | ```python |
| | 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)) |
| | ``` |
| |
|
| | ## 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} |
| | } |
| | ``` |
| |
|
