RNA-MSM
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
- Peking University,Shanghai AI Lab,Griffith University,Peng Cheng Laboratory,Shenzhen Bay Laboratory
- Organisation type
- Academia,Academia,Academia,Academia
- Country
- China, Australia
- Published
- 6 November 2023
- Authors
- Yikun Zhang, Mei Lang, Jiuhong Jiang, Zhiqiang Gao, Fan Xu, Thomas Litfin, Ke Chen, Jaswinder Singh, Xiansong Huang, Guoli Song, Yonghong Tian, Jian Zhan, Jie Chen, Yaoqi Zhou
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.
- Training data
- tokens
- Epochs
- 300
- Batch size
- 1
3932 (families) × 512 (sequences per family) × 32 (tokens per sequence) = 64,421,888 unique tokens Total: 6.4e7 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 (unrestricted)
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
- 82
Sources
Where this record came from and when it was last checked.
- Reference
- Multiple sequence alignment-based RNA language model and its application to structural inference
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
RNA-MSM was published by Peking University,Shanghai AI Lab,Griffith University,Peng Cheng Laboratory,Shenzhen Bay Laboratory, in China, in November 2023. academia,Academia,Academia,Academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.
Answers
RNA-MSM — common questions
Who created RNA-MSM?
RNA-MSM was published by Peking University,Shanghai AI Lab,Griffith University,Peng Cheng Laboratory,Shenzhen Bay Laboratory, based in China, categorised as academia,Academia,Academia,Academia.
When was RNA-MSM released?
RNA-MSM was published in November 2023. 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 RNA-MSM used for?
RNA-MSM works in Biology, and is recorded as handling 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.
Where can I download RNA-MSM?
The weights for RNA-MSM are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
What GPU do I need to run RNA-MSM?
We cannot say. RNA-MSM 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.
Is RNA-MSM open source?
Its weights are published, so RNA-MSM 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.
How many parameters does RNA-MSM have?
No parameter count has been published for RNA-MSM, which is why no memory or speed figure appears on this page.
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.