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Shawn-IEITSystems

Yuan-1.0

Yuan 1.0 Large pretrained LM.

ModelsFoundation modelsPython
Open on GitHub
Momentum
+0stars in 24 hours0.0%
588
Stars
109
Forks
+-1
This week
8
Contributors
Created 2021-09-27 · Updated 2026-10-13 · #16485 today
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Shawn-IEITSystemslifengssjoejeanjean

Shawn-IEITSystems/Yuan-1.0 is ranked 16,485 with 588 stars.

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Shawn-IEITSystems/Yuan-1.0 is ranked 16,485 among these 8 peers; microsoft/CSWin-Transformer is ranked 16,476.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguagePython
CategoryModels
SubcategoryFoundation models
Stars588
Forks109
Contributors8
Created date2021-09-27
Updated date2026-10-13
Stars in 1 day0
Stars in 7 days-1
Rank16,485
Previous rank16,273
Rank change-212
7-day stars rank17,899
Previous 7-day stars rank17,848
7-day stars rank change-51
Total stars rank17,033
Previous total stars rank17,011
Total stars rank change-22
CountryChina
README summary

0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning Introduction Recent work like GPT-3 has demonstrated excellent performance of Zero-Shot and Few-Shot learning on many natural language processing (NLP) tasks by scaling up model size, data size and the amount of compute. While training a model like GPT-3 requires huge amount of computing power that is a challenge to the researchers. In this work, we propose a method that incorporates large

-scale distributed training performance into model architecture design. 0, the current largest singleton language model with 246B parameters, which achieved excellent performance on thousands GPUs, and state-of-the-art results on different natural language processing tasks. We propose a data processing method that can efficiently filter massive amount of data from Internet. A new dataset with 5TB high-quality text, the current largest Chinese text corpus, is built based on this method. We propose a method based on calibration

and label expansion to improve the Zero-Shot and Few-Shot performance, and steady performance improvements were observed. 0 generated are difficult to distinguish from articles written by humans. Please find details in the paper of Yuan-1. 0 We will open the corpus (1TB) and API of the Yuan model, as well as the codes for fine-tune, few-shot and zero-shot learning. Please visit official website ( for details to get access of the corpus and APIs of

Yuan model. Requirements The inference code is provided on python3. Before start using Yuan API to build your application, several python libs are required. You can simply install them via pip tools. How to use Yuan-API After submit application on official website, it will take several days (normally less than one week) for us to check your application. Please keep your registered account and phone number properly, which will be used to generate an unique key to get access

the API. For more details, please check the example code, , and follow the API document. Applications Here we summarize some simple application example configuration methods for users' reference. The parameters not mentioned therein have adopted default values. |not recommended| Please look forward to more applications.

Frequently asked questions

How many stars and forks are recorded?

Shawn-IEITSystems/Yuan-1.0 has 588 stars and 109 forks.

What is the repository rank?

Shawn-IEITSystems/Yuan-1.0 is ranked 16,485.

What language and category are recorded?

Shawn-IEITSystems/Yuan-1.0 has 588 stars, uses Python and belongs to Models.

Which repository is closest in rank?

Shawn-IEITSystems/Yuan-1.0 is compared with microsoft/CSWin-Transformer, ranked 16,476 with 589 stars.

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