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license: bsd-3-clause |
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--- |
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# CodeGen (CodeGen-Mono 6B) |
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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-Mono 6B** in the paper, where "Mono" means the model is initialized with *CodeGen-Multi 6B* and further pre-trained on a Python programming language dataset, and "6B" refers to the number of trainable parameters. |
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## Training data |
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This checkpoint (CodeGen-Mono 6B) was firstly initialized with *CodeGen-Multi 6B*, and then pre-trained on BigPython dataset. The data consists of 71.7B tokens of Python programming language. See Section 2.1 of the [paper](https://arxiv.org/abs/2203.13474) for more details. |
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## Training procedure |
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CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs. |
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The family of models are trained using 4 TPU-v4 chips by Google, leveraging data and model parallelism. |
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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. |
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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. |
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## How to use |
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This model can be easily loaded using the `AutoModelForCausalLM` functionality: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-6B-mono') |
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model = AutoModelForCausalLM.from_pretrained('Salesforce/codegen-6B-mono') |
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text = "def hello_world():" |
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input_ids = tokenizer(text, return_tensors="pt").input_ids |
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generated_ids = model.generate(input_ids, max_length=128) |
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print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) |
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``` |
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## BibTeX entry and citation info |
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```bibtex |
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@article{Nijkamp2022ACP, |
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title={A Conversational Paradigm for Program Synthesis}, |
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author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming}, |
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journal={arXiv preprint}, |
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year={2022} |
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} |
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``` |
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