CLR_ESP
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
- Kansas State University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 16 August 2024
- Authors
- Zhenjiao Du, Weiming Fu, Xiaolong Guo, Doina Caragea, Yonghui Li
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Enzyme substrate pair prediction
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
- 815,728 tokens
- Epochs
- 500
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
- 2.1 × 10¹⁷ FLOP
3*60*60*65130000000000*0.3=2.110212e+17
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 Tesla T4
- Chips used
- 1
- Wall-clock time
- 3 hours
- Power draw
- 76 W
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 (non-commercial)
- Training code
- Open (non-commercial)
"The datasets and codes used to generate the results of this paper are available from https://github.com/dzjxzyd/CLR_ESP" no clear license
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
Sources
Where this record came from and when it was last checked.
- Reference
- CLR_ESP: Improved enzyme-substrate pair prediction using contrastive learning
- Last updated
- 28 November 2025
What the numbers mean
What this model is
CLR_ESP was published by Kansas State University, in United States of America, in August 2024. It comes out of academia.
It works in Biology, and is recorded as doing enzyme substrate pair prediction.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
What went into building it
Producing it required around 2.1 × 10¹⁷ FLOP of arithmetic, on NVIDIA Tesla T4, which is a statement about the training budget rather than about inference.
It was trained on about 815,728 tokens of text.
Answers
CLR_ESP — common questions
How much compute was used to train CLR_ESP?
Around 2.1 × 10¹⁷ FLOP, on NVIDIA Tesla T4. 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.
What GPU do I need to run CLR_ESP?
We cannot say. CLR_ESP 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 CLR_ESP open source?
Its weights are published, so CLR_ESP 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 CLR_ESP have?
No parameter count has been published for CLR_ESP, which is why no memory or speed figure appears on this page.
Who created CLR_ESP?
CLR_ESP was published by Kansas State University, based in United States of America, categorised as academia.
When was CLR_ESP released?
CLR_ESP was published in August 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.
What is CLR_ESP used for?
CLR_ESP works in Biology, and is recorded as handling enzyme substrate pair prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
Where can I download CLR_ESP?
The weights for CLR_ESP are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
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.