|
|
|
# CT2Fast-CodeT5P-220M: Fast Inference Model |
|
# Fast-Inference with Ctranslate2 |
|
|
|
Quantized version of Salesforce/codet5p-220m |
|
|
|
```python |
|
!ct2-transformers-converter --model Salesforce/codet5p-220m --output_dir /content/sample_data/codet5p-220m-ct2 --quantization float16``` |
|
|
|
|
|
|
|
|
|
## Licence and Other Remarks: |
|
|
|
This model is just a quantized version of Codet5p-220m. |
|
Licence conditions are intended to be idential to original huggingface repo. |
|
|
|
|
|
|
|
--- |
|
license: bsd-3-clause |
|
--- |
|
|
|
# CodeT5+ 220M |
|
|
|
## Model description |
|
|
|
[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. |
|
It is introduced in the paper: |
|
|
|
[CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://arxiv.org/pdf/2305.07922.pdf) |
|
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). |
|
|
|
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. |
|
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. |
|
Furthermore, it is instruction-tuned to align with natural language instructions (see our InstructCodeT5+ 16B) following [Code Alpaca](https://github.com/sahil280114/codealpaca). |
|
|
|
## How to use |
|
|
|
This model can be easily loaded using the `T5ForConditionalGeneration` functionality and employs the same tokenizer as original [CodeT5](https://github.com/salesforce/CodeT5). |
|
|
|
```python |
|
from transformers import T5ForConditionalGeneration, AutoTokenizer |
|
|
|
checkpoint = "Salesforce/codet5p-220m" |
|
device = "cuda" # for GPU usage or "cpu" for CPU usage |
|
|
|
tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
|
model = T5ForConditionalGeneration.from_pretrained(checkpoint).to(device) |
|
|
|
inputs = tokenizer.encode("def print_hello_world():<extra_id_0>", return_tensors="pt").to(device) |
|
outputs = model.generate(inputs, max_length=10) |
|
print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
|
# ==> print "Hello World" |
|
``` |
|
|
|
## Pretraining data |
|
|
|
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). |
|
The data is preprocessed by reserving only permissively licensed code ("mit" “apache-2”, “bsd-3-clause”, “bsd-2-clause”, “cc0-1.0”, “unlicense”, “isc”). |
|
Supported languages (9 in total) are as follows: |
|
`c`, `c++`, `c-sharp`, `go`, `java`, `javascript`, `php`, `python`, `ruby.` |
|
|
|
## Training procedure |
|
|
|
This checkpoint 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_. |
|
Please refer to the paper for more details. |
|
|
|
## Evaluation results |
|
|
|
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_. |
|
Specifically, CodeT5+ yields substantial performance gains on many downstream tasks compared to their SoTA baselines, e.g., |
|
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). |
|
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. |
|
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 |
|
Please refer to the [paper](https://arxiv.org/pdf/2305.07922.pdf) for more details. |
|
|
|
|
|
## BibTeX entry and citation info |
|
|
|
```bibtex |
|
@article{wang2023codet5plus, |
|
title={CodeT5+: Open Code Large Language Models for Code Understanding and Generation}, |
|
author={Wang, Yue and Le, Hung and Gotmare, Akhilesh Deepak and Bui, Nghi D.Q. and Li, Junnan and Hoi, Steven C. H.}, |
|
journal={arXiv preprint}, |
|
year={2023} |
|
} |
|
|
|
|