Note: Text provided here for indexing only, please see the Google sheet above for formatting as intended.
What is in PanGu Alpha? PanGu Alpha contains (sorted by most tokens/effective size):
Common Crawl (www)
Public datasets: DuReader (discussion), Baidu QA (discussion), CAIL2018 (legal papers), SogouCA (news), and more…;
Encyclopedia: Baidu Baike (facts), Sogou Baike (facts), and more…;
-end of list-
WuDaoCorpora 1.0 (dataset) and Wudao 1.0 (model) were launched in March 2021.
WuDaoCorpora 2.0 (dataset) and Wudao 2.0 (model) were launched in June 2021 (at the 2021 BAAI conference).
WuDaoCorpora 2.0 is composed of three parts:
1. WDC-Text (3TB text), the world’s largest plain text dataset.
2. WDC-ImageCaption (90TB image and text), the world’s largest multimodal dataset.
3. WDC-Dialogue (180GB text), the world’s largest Chinese dialogue dataset.
WDC-Text (3TB text)
3TB of text data, with labelling. “20 strict cleaning rules used by WuDaoCorpora1.0, and derives high-quality datasets from more than 100TB of original web page data.”
WDC-ImageCaption (90TB image and text)
“Contains 630 million image and text pairs, with a total data volume of about 90TB, the largest in the world. Among them, 600 million is related to graphics and text, and 30 million is a specific description of the content of the image.”
WDC-Dialogue (180GB text)
“Contains 181GB of high-quality Chinese dialogue data, and the total number of dialogues reaches 1.4B… Cleaned up 180GB of high-quality dialogue data from 9TB of raw data.”
What is in Wudao 2.0? Wudao 2.0 contains:
WuDaoCorpora2 – Chinese text only:
Baidu Baike (facts/encyclopedia)
Sogou Baike (facts/encyclopedia)
Baidu QA (discussion)
(*best guess only, sorted by most visits);
Tencent QQ (messenger)
Sina Weibo (discussion)
Sina Corporation (news)
Xinhua News Agency (news)
Chinese Software Dev Network (discussion)
Global Times (news)
Tianya Club (discussion)
17ok.com (finance discussion)
BabyTree (parenting discussion)
CNBlogs (software discussion)
Hunan Rednet (news)
Bilibili (video discussion)
“Corpora contains various data types including news, post bar comments (sic), encyclopedia information, etc. More specifically, WuDaoCorpora contains a 3 TB Chinese corpus collected from 822 million Web pages” (WuDaoCorpora paper, Tang et al, June 2021).
“For training of base model, we use a training set of 302GB, the distribution of these data is shown in Table 7” (Inverse Prompting paper, Tang et al, June 2021).
Wudao 2.0 is sometimes misspelt as: Wudao-2, Wudao 2, Wu dao 2.0, Woodao, Woo dao.
PanGu Alpha: Launched by Huawei and others in April 2021.
Simplified Chinese: 盘古
Traditional Chinese: 盤古
Pinyin: Pán gǔ
Pronounced: pun-goo (rhymes with done tool)
English: Literal: ‘coil ancient’, first living being and the creator (coiled up in an egg).
Etymology: Mythical Chinese creation figure who emerged from a yin-yang egg and created the earth and sky (similar to the Christian creation story, and Pangu has been compared to Adam).
Wudao 2.0: Launched by the Beijing Academy of Artificial Intelligence (BAAI) and others in June 2021.
Simplified Chinese: 悟道
Traditional Chinese: 悟道
Pinyin: Wù dào
Pronounced: oo-dao (rhymes with tool now)
English: Literal: ‘Enlightenment’.
Etymology: Truth of the Dharma, the spiritual path.
Launched July 2021, BlenderBot 2.0 is pre-trained on (Reddit discussion), fine-tuned on ConvAI2, Empathetic Dialogues, and Wizard of Wikipedia (WoW) datasets. The two additional datasets are Multi-Session Chat and Wizard of the Internet (WizInt). To train for safety, it uses the BAD dataset. Finally—in realtime—it is able to add live results by ‘generating its own search queries, reading the results, and taking them into account when formulating a response.’
BlenderBot 2.0 chatbot uses live/realtime web search engine results as part of its language model. The validation set (WizInt) paired up humans to have a conversation, with one human given the option to perform a web search (query and query + "news") to respond to their partner in conversation. Search results were added to the conversation by the human 80.3% of the time. The resulting WizInt dataset (validation set of human conversations with search) is used as supervision for new queries in BlenderBot 2.0. That is, new conversations with BlenderBot 2.0 will generate new responses that may include live/realtime web search engine results.
Breakdown of most common domains used during search… (validation set breakdown). Shown is the most common 24.41%, there is a long tail of 1,233 other domains across the whole validation set.
Think you’re a fast typer? In March 2021, GPT-3 was typing 3.1 million words per minute, non-stop, 24×7. With the general availability of the model, I expect that number is a lot higher now… (Nov/2021).
Per day = 4,500,000,000 (4.5 billion)
Per hour = 187,500,000 (187.5 million)
Per minute = 3,125,000 (3.125 million)
Every day, GPT-3 generates the equivalent of an entire US public library (80,000 books) of new content.
(“…more than 300 applications are now using GPT-3, and tens of thousands of developers around the globe are building on our platform. We currently generate an average of 4.5 billion words per day, and continue to scale production traffic.” (OpenAI blog, March 2021). Using an average of 55k words per book = 81,818 books per day. “In 2017, there were 9,045 public libraries in the United States with a total of 715 million books and serial volumes” (US stats) = 79,049 books per library.)
“GitHub says that for some programming languages, about 30% of newly written code is being suggested by the company’s AI programming tool Copilot.” (Axios, October 2021)
“The supercomputer developed for OpenAI [as of May 2020] is a single system with more than 285,000 CPU cores, 10,000 GPUs [assume NVIDIA Tesla V100 GPUs released May/2017, superseded by NVIDIA Ampère A100 GPUs in May/2020] and 400 gigabits per second of network connectivity for each GPU server.”
Our model was trained… on 300B tokens drawn from publicly available resources, attempting, in part, to replicate the structure of the training data as reported in Brown et al. (2020) [the GPT-3 dataset, which is detailed in the viz above at LifeArchitect.ai/models].
— AI21’s Jurassic-1 paper
tr11 by BigScience
176B parameter multi-lingual model.
Trained in March 2022.
The BigScience project for open research is a year-long initiative (2021-2022) targeting the study of large models and datasets. The goal of the project is to research language models in a public environment outside large technology companies. The project has 600 researchers from 50 countries and more than 250 institutions. The BigScience project was initiated by Thomas Wolf at Hugging Face.
“using regular Megatron-LM GPT2 language model w/o multi-lingual dataset”
GPT-2’s dataset was just English WebText, which is popular outbound Reddit links with more than two upvotes. It is 40GB.
They then claim to be using more languages:
“XXX: correct this Languages: ar, ca, code, en, es, eu, fr, id, indic-as, indic-bn, indic-gu, indic-hi, indic-kn, indic-ml, indic-mr, indic-ne, indic-or, indic-pa, indic-ta, indic-te, indic-ur, nigercongo-all, oscar-en, oscar-zh, pt, vi, zhs, zht”
Given that OSCAR (oscar-en and oscar-zh) is sourced from the CommonCrawl, it might be safe to assume that all other languages are also sourced from the CC. Jesse and the Allen AI team found 101 languages in the massive CommonCrawl/C4 dataset: https://arxiv.org/abs/2104.08758.
M6 by Alibaba
Multi-Modality to MultiModality Multitask Mega-transformer (M6)
From 100B to 1T to 10T parameters in less than a year!
“M6-Corpus for pretraining in Chinese, which consists of over 1.9TB image and 292GB text. The dataset has large coverage over domains, including encyclopedia, question answering, forum discussion, common crawl, etc”
Due to the complexity of this transformer and related language models, Megatron has its own page showing a summary of timeline, labs involved, and other details.
The Luminous text model was announced at a conference in Nov/2021, where the parameter count for Luminous (assuming Luminous-World, in progress as of Apr/2022) was said to be 200B.
The announced luminous model uses up to 200 billion parameters and is considered to be just as powerful in the text part as GPT, whose third version includes up to 175 billion parameters. In contrast to the American counterpart, luminous can be combined with any number of images, the model is available in five languages (German, English, French, Italian, Spanish) and has been trained in the European cultural context. — Heise, translated.
12–40B (Alan best guess)
40–80B (Alan best guess)
80–180B (Alan best guess)
200B (from presentation)
* Alan best guess made during testing Apr/2022, based on informed ‘feel’, token/credit comparison, and tiering.
DeepMind’s models are: Gopher, Chinchilla, Flamingo, and Gato (cat).
by Dr Alan D. Thompson · Hundreds of paid subscribers Be inside the lightning-fast AI revolution. Artificial intelligence that matters, as it happens, in plain English. Get The Memo.
⬛ Dr Alan D. Thompson is an AI expert and consultant. With Leta (an AI powered by GPT-3), Alan co-presented a seminar called ‘The new irrelevance of intelligence’ at the World Gifted Conference in August 2021. His applied AI research and visualisations are featured across major international media, including citations in the University of Oxford’s debate on AI Ethics in December 2021. He has held positions as chairman for Mensa International, consultant to GE and Warner Bros, and memberships with the IEEE and IET. He is open to consulting and advisory on major AI projects with intergovernmental organisations and enterprise.Contact.