PMLM-large
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
- Microsoft Research Asia,Nanyang Technological University,Xi’an Jiaotong University,Sun Yat-sen University
- Organisation type
- Industry,Academia,Academia,Academia
- Country
- China, Singapore
- Published
- 21 October 2021
- Authors
- Liang He, Shizhuo Zhang, Lijun Wu, Huanhuan Xia, Fusong Ju, He Zhang, Siyuan Liu, Yingce Xia, Jianwei Zhu, Pan Deng, Bin Shao, Tao Qin, Tie-Yan Liu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
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
- 250M
- Training data
- tokens
"Following the RoBERTa-base setting, the hidden size, feed forward dimension, number of encoder layers, and attention heads of the base models are set as 768, 3072, 12, 12 re- spectively (denoted as MLM-base for MLM and PMLM-base for PMLM). A larger model named PMLM-large is pre-trained with the same setting except using 34 as the number of encoder layers."
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 3.8 × 10²¹ FLOP
- How it was established
- Hardware
1176*60*60*125000000000000*24*0.3=3.81024e+21
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 V100
- Chips used
- 24
- Wall-clock time
- 1,176 hours (49 days)
- Power draw
- 14.5 kW
"about seven weeks for PMLM-large"
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
- 36
Sources
Where this record came from and when it was last checked.
- Reference
- Pre-Training Co-Evolutionary Protein Representation via a Pairwise Masked Language Model
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
PMLM-large was published by Microsoft Research Asia,Nanyang Technological University,Xi’an Jiaotong University,Sun Yat-sen University, in the country recorded as China, during October 2021. The category the publisher falls under is industry,Academia,Academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of protein or nucleotide language model (pLM/nLM).
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 run consumed about 3.8 × 10²¹ FLOP, on hardware recorded as NVIDIA V100. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
PMLM-large — common questions
PMLM-large— what GPU do I need to run it?
None. This 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.
PMLM-large— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
PMLM-large— how many parameters does it have?
It has a parameter count of 250M. "Following the RoBERTa-base setting, the hidden size, feed forward dimension, number of encoder layers, and attention heads of the base models are set as 768, 3072, 12, 12 re- spectively (denoted as MLM-base for MLM and PMLM-base for PMLM). A larger model named PMLM-large is pre-trained with the same setting except using 34 as the number of encoder layers.". 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.
PMLM-large— who created it?
It was published by Microsoft Research Asia,Nanyang Technological University,Xi’an Jiaotong University,Sun Yat-sen University, based in China, an organisation categorised as industry,Academia,Academia,Academia.
PMLM-large— when was it released?
It was published in October 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.
PMLM-large— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein or nucleotide language model (pLM/nLM). A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
PMLM-large— how much compute was used to train it?
Training consumed around 3.8 × 10²¹ FLOP, on hardware recorded as NVIDIA V100. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
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.