alreadyme ai research

已經Me.md AI研究

使用GPT-3生成readme.md幾乎沒有學習

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介紹

Asme-ai-Research是一個核心項目，用於從任何存儲庫中的源代碼生成README.md 。 AI模型讀取源代碼的某些部分，並編寫相應的README.md文檔。 Aboutme.md團隊目前正在提供有關此功能的服務，您可以在此頁面上找到我們的結果。

該存儲庫包含幾個子標記。您可以在目錄中看到詳細的描述。

• 數據準備：準備火車數據集的源代碼。
• 模型 - 網絡：如何有效地調整大規模語言模型。
• 句子生成：為模型服務生成句子的有效且可擴展的方式。

它如何工作？

正如GPT-3這樣的大型模型所顯示的那樣，很少有射擊學習是構建廣義語言模型的最重要關鍵。他們可以根據以前的提示和很少的示例來理解應該寫的內容。使用此功能，他們幾乎可以在不進行微調的情況下做任何事情。他們可以總結新聞，回答問題，甚至進行對話！

Openai Codex通過微調GPT-3推出了新的大型Langauge模型，用於編程語言。現在，我們可以期望對編程語言的廣義性能（幾乎沒有學習）。例如，從源代碼創建DOCSTRING，從描述中編寫新代碼（這就是Copilot的工作方式），然後從Python轉換為Java。

我們使用的是開放科學和大規模語言模型的開放訪問。 Bloom支持多語言，不僅是自然語言，而且是編程語言。我們設計了及時的模板，並找到了它們的最佳版本。

 &&&&&&
$ head -n 30 model-finetuning/src/data.py
from __future__ import annotations

from dataclasses import dataclass

import torch
[...]

&&&&&&
$ head -n 37 model-finetuning/src/train.py
from __future__ import annotations

import argparse
import os
[...]

&&&&&&
$ git config --get remote.origin.url
https://github.com/readme-generator/alreadyme-ai-research.git

&&&&&&
$ cat README.md
[...]

所有示例將由&&&&&&隔離。我們旨在使Bloom執行（或模擬）Linux Bash命令。 Bloom將從給定的提示符中讀取源代碼的某些部分，並生成適當的README.md文件。

有關更多詳細信息，請查看我們的模型調整子標題。

執照

已經以Apache許可證2.0發布了已經發布的研究。可以在此處找到許可證。

引用

 @misc { https://doi.org/10.48550/arxiv.2005.14165 ,
	title        = { Language Models are Few-Shot Learners } ,
	author       = { Brown, Tom B. and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and Agarwal, Sandhini and Herbert-Voss, Ariel and Krueger, Gretchen and Henighan, Tom and Child, Rewon and Ramesh, Aditya and Ziegler, Daniel M. and Wu, Jeffrey and Winter, Clemens and Hesse, Christopher and Chen, Mark and Sigler, Eric and Litwin, Mateusz and Gray, Scott and Chess, Benjamin and Clark, Jack and Berner, Christopher and McCandlish, Sam and Radford, Alec and Sutskever, Ilya and Amodei, Dario } ,
	year         = 2020 ,
	publisher    = { arXiv } ,
	doi          = { 10.48550/ARXIV.2005.14165 } ,
	url          = { https://arxiv.org/abs/2005.14165 } ,
	copyright    = { arXiv.org perpetual, non-exclusive license } ,
	keywords     = { Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences }
}

 @misc { https://doi.org/10.48550/arxiv.2107.03374 ,
	title        = { Evaluating Large Language Models Trained on Code } ,
	author       = {Chen, Mark and Tworek, Jerry and Jun, Heewoo and Yuan, Qiming and Pinto, Henrique Ponde de Oliveira and Kaplan, Jared and Edwards, Harri and Burda, Yuri and Joseph, Nicholas and Brockman, Greg and Ray, Alex and Puri, Raul and Krueger, Gretchen and Petrov, Michael and Khlaaf, Heidy and Sastry, Girish and Mishkin, Pamela and Chan, Brooke and Gray, Scott and Ryder, Nick and Pavlov, Mikhail and Power, Alethea and Kaiser, Lukasz and Bavarian, Mohammad and Winter, Clemens and Tillet, Philippe and Such, Felipe Petroski and Cummings, Dave and Plappert, Matthias and Chantzis, Fotios and Barnes, Elizabeth and Herbert-Voss, Ariel and Guss, William Hebgen and Nichol, Alex and Paino, Alex and Tezak, Nikolas and Tang, Jie and Babuschkin, Igor and Balaji, Suchir and Jain, Shantanu and Saunders, William and Hesse, Christopher and Carr, Andrew N. and Leike, Jan and Achiam, Josh and Misra, Vedant and Morikawa, Evan and Radford, Alec and Knight, Matthew and Brundage, Miles and Murati, Mira and Mayer, Katie and Welinder, Peter and McGrew, Bob and Amodei, Dario and McCandlish, Sam and Sutskever, Ilya and Zaremba, Wojciech},
	year         = 2021 ,
	publisher    = { arXiv } ,
	doi          = { 10.48550/ARXIV.2107.03374 } ,
	url          = { https://arxiv.org/abs/2107.03374 } ,
	copyright    = { arXiv.org perpetual, non-exclusive license } ,
	keywords     = { Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences }
}

 @misc { https://doi.org/10.48550/arxiv.2106.09685 ,
	title        = { LoRA: Low-Rank Adaptation of Large Language Models } ,
	author       = { Hu, Edward J. and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu } ,
	year         = 2021 ,
	publisher    = { arXiv } ,
	doi          = { 10.48550/ARXIV.2106.09685 } ,
	url          = { https://arxiv.org/abs/2106.09685 } ,
	copyright    = { arXiv.org perpetual, non-exclusive license } ,
	keywords     = { Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences }
}

 @misc { bigscience_2022 ,
	title        = { Bigscience large open-science openaccess multilingual language model. } ,
	author       = { BigScience } ,
	year         = 2022 ,
	journal      = { bigscience/bloom · Hugging Face } ,
	url          = { https://huggingface.co/bigscience/bloom }
}