InstructPLM
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
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
- Zhejiang Lab,Zhejiang University (ZJU),Nanjing University,Tsinghua University,Alibaba,Chinese University of Hong Kong (CUHK)
- Organisation type
- Academia,Academia,Academia,Industry,Academia
- Country
- China, Hong Kong
- Published
- 20 April 2024
- Authors
- Jiezhong Qiu, Junde Xu, Jie Hu, Hanqun Cao, Liya Hou, Zijun Gao, Xinyi Zhou, Anni Li, Xiujuan Li, Bin Cui, Fei Yang, Shuang Peng, Ning Sun, Fangyu Wang, Aimin Pan, Jie Tang, Jieping Ye, Junyang Lin, Jin Tang, Xingxu Huang, Pheng Ann Heng, Guangyong Chen
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein generation, Protein folding prediction
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.
- Parameters
- 89.1M
- Training data
- 5,407,200 tokens
- Epochs
- 200
Table 2
InstructPLM Training Data Points: - Training Proteins: 18,024 - Avg Sequence Length: 300 - Total Tokens = 18,024 × 300 = 5,407,200 ≈ 5.4 × 10^6 data points
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA A100
- Chips used
- 4
- Power draw
- 3.2 kW
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 13
Sources
Where this record came from and when it was last checked.
- Reference
- InstructPLM: Aligning Protein Language Models to Follow Protein Structure Instructions
- Last updated
- 1 January 2026
What the numbers mean
What this model is
InstructPLM was published by Zhejiang Lab,Zhejiang University (ZJU),Nanjing University,Tsinghua University,Alibaba,Chinese University of Hong Kong (CUHK), in China, in April 2024. The organisation is categorised as academia,Academia,Academia,Industry,Academia.
It works in Biology, and is recorded as doing protein generation, Protein folding prediction.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training set ran to roughly 5,407,200 tokens.
Answers
InstructPLM — common questions
How many parameters does InstructPLM have?
InstructPLM has 89.1M parameters. Table 2. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created InstructPLM?
InstructPLM was published by Zhejiang Lab,Zhejiang University (ZJU),Nanjing University,Tsinghua University,Alibaba,Chinese University of Hong Kong (CUHK), based in China, categorised as academia,Academia,Academia,Industry,Academia.
When was InstructPLM released?
InstructPLM was published in April 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is InstructPLM used for?
InstructPLM works in Biology, and is recorded as handling protein generation, Protein folding prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run InstructPLM?
None. InstructPLM is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is InstructPLM open source?
The licensing for InstructPLM was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.