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license: bsd-3-clause |
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--- |
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# CodeT5+ 16B |
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## Model description |
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[CodeT5+](https://github.com/salesforce/CodeT5/tree/main/CodeT5+) is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. _encoder-only_, _decoder-only_, and _encoder-decoder_) to support a wide range of code understanding and generation tasks. |
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It is introduced in the paper: |
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[CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://arxiv.org/pdf/2305.07922.pdf) |
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by [Yue Wang](https://yuewang-cuhk.github.io/)\*, [Hung Le](https://sites.google.com/view/henryle2018/home?pli=1)\*, [Akhilesh Deepak Gotmare](https://akhileshgotmare.github.io/), [Nghi D.Q. Bui](https://bdqnghi.github.io/), [Junnan Li](https://sites.google.com/site/junnanlics), [Steven C.H. Hoi](https://sites.google.com/view/stevenhoi/home) (* indicates equal contribution). |
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Compared to the original CodeT5 family (base: `220M`, large: `770M`), CodeT5+ is pretrained with a diverse set of pretraining tasks including _span denoising_, _causal language modeling_, _contrastive learning_, and _text-code matching_ to learn rich representations from both unimodal code data and bimodal code-text data. |
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Additionally, it employs a simple yet effective _compute-efficient pretraining_ method to initialize the model components with frozen off-the-shelf LLMs such as [CodeGen](https://github.com/salesforce/CodeGen) to efficiently scale up the model (i.e. `2B`, `6B`, `16B`), and adopts a "shallow encoder and deep decoder" architecture. |
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Furthermore, it is instruction-tuned to align with natural language instructions (see our InstructCodeT5+ 16B) following [Code Alpaca](https://github.com/sahil280114/codealpaca). |
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## How to use |
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This model can be easily loaded using the `AutoModelForSeq2SeqLM` functionality and employs the same tokenizer as [CodeGen](https://github.com/salesforce/CodeGen). |
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```python |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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checkpoint = "Salesforce/codet5p-16b" |
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device = "cuda" # for GPU usage or "cpu" for CPU usage |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, |
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torch_dtype=torch.float16, |
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low_cpu_mem_usage=True, |
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trust_remote_code=True).to(device) |
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encoding = tokenizer("def print_hello_world():", return_tensors="pt").to(device) |
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encoding['decoder_input_ids'] = encoding['input_ids'].clone() |
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outputs = model.generate(**encoding, max_length=15) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Pretraining data |
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This checkpoint is trained on the stricter permissive subset of the deduplicated version of the [github-code dataset](https://huggingface.co/datasets/codeparrot/github-code). |
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The data is preprocessed by reserving only permissively licensed code ("mit" “apache-2”, “bsd-3-clause”, “bsd-2-clause”, “cc0-1.0”, “unlicense”, “isc”). |
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Supported languages (9 in total) are as follows: |
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`c`, `c++`, `c-sharp`, `go`, `java`, `javascript`, `php`, `python`, `ruby.` |
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## Training procedure |
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This checkpoint is initialized from off-the-shelf LLMs, i.e. its encoder is initialized from [CodeGen-350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) and its decoder is initialized from [CodeGen-16B-mono](https://huggingface.co/Salesforce/codegen-16B-mono). |
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It is trained on the unimodal code data at the first-stage pretraining, which includes a diverse set of pretraining tasks including _span denoising_ and two variants of _causal language modeling_. |
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After that, it is further trained on the Python subset with the causal language modeling objective for another epoch to better adapt for Python code generation. |
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Please refer to the paper for more details. |
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## Evaluation results |
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CodeT5+ models have been comprehensively evaluated on a wide range of code understanding and generation tasks in various settings: _zero-shot_, _finetuning_, and _instruction-tuning_. |
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Specifically, CodeT5+ yields substantial performance gains on many downstream tasks compared to their SoTA baselines, e.g., |
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8 text-to-code retrieval tasks (+3.2 avg. MRR), 2 line-level code completion tasks (+2.1 avg. Exact Match), and 2 retrieval-augmented code generation tasks (+5.8 avg. BLEU-4). |
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In 2 math programming tasks on MathQA-Python and GSM8K-Python, CodeT5+ models of below billion-parameter sizes significantly outperform many LLMs of up to 137B parameters. |
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Particularly, in the zero-shot text-to-code generation task on HumanEval benchmark, InstructCodeT5+ 16B sets new SoTA results of 35.0% pass@1 and 54.5% pass@10 against other open code LLMs, even surpassing the closed-source OpenAI code-cushman-001 mode |
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Please refer to the [paper](https://arxiv.org/pdf/2305.07922.pdf) for more details. |
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## BibTeX entry and citation info |
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```bibtex |
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@article{wang2023codet5plus, |
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title={CodeT5+: Open Code Large Language Models for Code Understanding and Generation}, |
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author={Wang, Yue and Le, Hung and Gotmare, Akhilesh Deepak and Bui, Nghi D.Q. and Li, Junnan and Hoi, Steven C. H.}, |
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journal={arXiv preprint}, |
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year={2023} |
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} |
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``` |
