Lightweight Fine-tuning a Pretrained Protein Language Model for Protein Secondary
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
- Henan University
- Organisation type
- Academia
- Country
- China
- Published
- 23 March 2023
- Authors
- Wei Yang, Chun Liu, Zheng Li
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), Protein folding prediction
- Base model
- ProtT5-XL-U50
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
- 10,000,001 tokens
25,792 training chains × 400 residues = 10,316,800 tokens ≈ 1.0e7
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
- 1.9 × 10²² FLOP
- How it was established
- Hardware
- Fine-tuning compute
- 6.1 × 10¹⁸ FLOP
1. Hardware setup: 1x NVIDIA GeForce RTX 3090 Ti (1.60 x 10^14 FP16 FLOP/s) 2. Training duration: Directly provided - 2,200 seconds/epoch × 43 epochs = 94,600 seconds 3. Utilization rate: 40% 4. Final calculation: 1.60 × 10^14 FLOP/s × 94,600 seconds × 0.4 = 6.05 × 10^18 FLOPs Base model: 18704498688000000000000 Total: 18704498688000000000000+6.05 × 10^18=18710548688000000000000
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 GeForce RTX 3090 Ti
- Chips used
- 1
- Wall-clock time
- 27 hours
- Power draw
- 493 W
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
- Lightweight Fine-tuning a Pretrained Protein Language Model for Protein Secondary Structure Prediction
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Lightweight Fine-tuning a Pretrained Protein Language Model for Protein Secondary was published by Henan University, in the country recorded as China, during March 2023. The category the publisher falls under is academia.
It works in the domain of Biology, and is recorded as performing the task of protein or nucleotide language model (pLM/nLM), Protein folding prediction.
It builds on ProtT5-XL-U50. That is the usual way a specialised model is produced.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Producing it required arithmetic totalling around 1.9 × 10²² FLOP, on hardware recorded as NVIDIA GeForce RTX 3090 Ti. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 10,000,001 tokens of text.
Answers
Lightweight Fine-tuning a Pretrained Protein Language Model for Protein Secondary — common questions
Lightweight Fine-tuning a Pretrained Protein Language Model for Protein Secondary— 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.
Lightweight Fine-tuning a Pretrained Protein Language Model for Protein Secondary— 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.
Lightweight Fine-tuning a Pretrained Protein Language Model for Protein Secondary— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Lightweight Fine-tuning a Pretrained Protein Language Model for Protein Secondary— who created it?
It was published by Henan University, based in China, an organisation categorised as academia.
Lightweight Fine-tuning a Pretrained Protein Language Model for Protein Secondary— when was it released?
It was published in March 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.
Lightweight Fine-tuning a Pretrained Protein Language Model for Protein Secondary— 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), Protein folding prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Lightweight Fine-tuning a Pretrained Protein Language Model for Protein Secondary— how much compute was used to train it?
Training consumed around 1.9 × 10²² FLOP, on hardware recorded as NVIDIA GeForce RTX 3090 Ti. 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?
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