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--- |
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language: |
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- code |
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license: bsd-3-clause |
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tags: |
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- code |
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- generative |
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datasets: |
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- bigcode/the-stack |
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--- |
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# CodeGen (CodeGen-CSS 350M) |
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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 finetuned on top of the **CodeGen-Multi 350M**, where "Multi" means the model is initialized with *CodeGen-NL 350M* and further pre-trained on a dataset of multiple programming languages, and "350M" refers to the number of trainable parameters. |
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It has been finetuned on CSS code contained in bigcode/the-stack dataset on huggingface |
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## Training data |
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This checkpoint (CodeGen-Multi 350M) was firstly initialized with *CodeGen-NL 350M*, 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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Lastly it has been finetuned on CSS code contained in [bigcode/the-stack](https://huggingface.co/datasets/bigcode/the-stack) dataset on huggingface |
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## Training procedure |
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Initially: |
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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 multiple TPU-v4-512 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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Finetune: |
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I fine tuned the 350M model on a single A100 with 40Gb of RAM, with batch size 10 and an input length of 512 tokens |
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Used 80-90% of the RAM |
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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-350M-multi") |
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model = AutoModelForCausalLM.from_pretrained("alecsharpie/codegen_350m_css") |
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text = ".header-container {" |
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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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``` |
