Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient
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
- Huazhong University of Science and Technology,Fudan University,Northwestern Polytechnical University
- Organisation type
- Academia,Academia,Academia
- Country
- China
- Published
- 24 April 2024
- Authors
- Shujian Jiao, Bingxuan Li, Lei Wang, Xiaojin Zhang, Wei Chen, Jiajie Peng, Zhongyu Wei
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)
- Base model
- ESM2-35M
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
- 35.8M
- Training data
- 30,841,287 tokens
Table 2: 540601*367.01=198405973
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient was published by Huazhong University of Science and Technology,Fudan University,Northwestern Polytechnical University, in the country recorded as China, during April 2024. It comes out of an organisation categorised as 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).
Rather than being trained from scratch, it is derived from ESM2-35M. That is the usual way a specialised model is produced.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
Training consumed a corpus of around 30,841,287 tokens of text.
Answers
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient — common questions
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient— when was it released?
It 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.
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient— 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.
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient— 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.
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient— 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.
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient— how many parameters does it have?
It has a parameter count of 35.8M. 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.
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient— who created it?
It was published by Huazhong University of Science and Technology,Fudan University,Northwestern Polytechnical University, based in China, an organisation categorised as academia,Academia,Academia.
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.