Wu Dao - Wen Su

Open weights Beijing Academy of Artificial Intelligence / BAAI March 2021

No estimate

No hardware requirements for this model

This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.

On record

Full specification

Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.

Origin

Who built this model, where, and when it was published.

Organisation
Beijing Academy of Artificial Intelligence / BAAI
Organisation type
Academia
Country
China
Published
1 March 2021
Authors
Tang Jie, Lu Bai, Qiu Jiezhong, Xie Changyu, Xiao Yijia, Zeng Aohan, Li Ziang

What it does

The problem areas the model was built for. A model can carry several of each.

Domain
Biology
Task
Proteins, Protein or nucleotide language model (pLM/nLM), Protein folding prediction
Approach
Self-supervised learning

Size

How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.

Training data
tokens

Availability

Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.

Weights
Open — downloadable
Model access
Open weights (non-commercial)

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Record confidence
Unknown

Sources

Where this record came from and when it was last checked.

Reference
China's GPT-3? BAAI Introduces Superscale Intelligence Model 'Wu Dao 1.0'
Last updated
28 November 2025

What the numbers mean

What this model is

Wu Dao - Wen Su was published by Beijing Academy of Artificial Intelligence / BAAI, in China, in March 2021. academia is the category the publisher falls under.

It works in Biology, and is recorded as doing proteins, Protein or nucleotide language model (pLM/nLM), Protein folding prediction.

The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.

Answers

Wu Dao - Wen Su — common questions

01

How many parameters does Wu Dao - Wen Su have?

No parameter count has been published for Wu Dao - Wen Su, which is why no memory or speed figure appears on this page.

02

Who created Wu Dao - Wen Su?

Wu Dao - Wen Su was published by Beijing Academy of Artificial Intelligence / BAAI, based in China, categorised as academia.

03

When was Wu Dao - Wen Su released?

Wu Dao - Wen Su was published in March 2021. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

04

What is Wu Dao - Wen Su used for?

Wu Dao - Wen Su works in Biology, and is recorded as handling proteins, Protein or nucleotide language model (pLM/nLM), Protein folding prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

05

Where can I download Wu Dao - Wen Su?

The weights for Wu Dao - Wen Su are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

06

What GPU do I need to run Wu Dao - Wen Su?

We cannot say. Wu Dao - Wen Su has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.

07

Is Wu Dao - Wen Su open source?

Its weights are published, so Wu Dao - Wen Su can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.

Source

Original publication

Record last updated 28 November 2025

The other direction

Looking at it from the other side?

This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.