ESM-DBP
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
- Hunan University
- Organisation type
- Academia
- Country
- China
- Published
- 7 September 2024
- Authors
- Wenwu Zeng, Yutao Dou, Liangrui Pan, Liwen Xu, Shaoliang Peng
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
- ESM2-650M
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
- 650M
- Training data
- 7,661,880 tokens
- Batch size
- 100
"so the ESM2 model with 650 million parameters is used to continue training here."
"170,264 non-redundant DBP sequences (abbreviate as UniDBP40) are used as the pretraining data set."
"The batch size is set to 100."
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.
- Fine-tuning compute
- 1.3 × 10¹⁹ FLOP
"The model is trained for approximately 51k steps and took about 3 days on four Tesla V100 GPUs with 16 G memory." Assume FP16 precision and 40% utilization.
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chips used
- 4
- Wall-clock time
- 72 hours
"The model is trained for approximately 51k steps and took about 3 days on four Tesla V100 GPUs with 16 G memory."
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
- Improving prediction performance of general protein language model by domain-adaptive pretraining on DNA-binding protein
- Last updated
- 28 November 2025
What the numbers mean
What this model is
ESM-DBP was published by Hunan University, in China, in September 2024. The organisation is categorised as academia.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
Its starting point was ESM2-650M — most models at this scale are adapted from an existing base rather than built from nothing.
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 7,661,880 tokens.
Answers
ESM-DBP — common questions
What is ESM-DBP used for?
ESM-DBP works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run ESM-DBP?
None. ESM-DBP 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 ESM-DBP open source?
The licensing for ESM-DBP was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does ESM-DBP have?
ESM-DBP has 650M parameters. "so the ESM2 model with 650 million parameters is used to continue training here.". 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 ESM-DBP?
ESM-DBP was published by Hunan University, based in China, categorised as academia.
When was ESM-DBP released?
ESM-DBP was published in September 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.
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.