RESP AI
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
- University of California San Diego
- Organisation type
- Academia
- Country
- United States of America
- Published
- 28 January 2023
- Authors
- Jonathan Parkinson, Ryan Hard, Wei Wang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Drug discovery, Proteins
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
- 8
"The end result of this process was thus a library of roughly 6 million sequences, half of which are human B-cell receptors and the other half of which are not. The autoencoder model is thus trained both to encode an input sequence and to embed information about typical features observed in true antibody sequences. The autoencoder was implemented using the PyTorch library in Python 3.6.9 and trained on the full 6 million sequence dataset until convergence." sequence length: 132 (amino acids) 6…
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)
- Training code
- Open source
"The code used in this study is available online at https://github.com/ Wang-lab-UCSD/RESP (https://doi.org/10.5281/zenodo.7508853), together with instructions on how to reproduce all key computational experiments."
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
- The RESP AI model accelerates the identification of tight-binding antibodies
- Last updated
- 28 November 2025
What the numbers mean
About this model
RESP AI was published by University of California San Diego, in the country recorded as United States of America, during January 2023. The publishing organisation is categorised as academia.
It works in the domain of Biology, and is recorded as performing the task of drug discovery, Proteins.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
Answers
RESP AI — common questions
RESP AI— when was it released?
It was published in January 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.
RESP AI— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of drug discovery, Proteins. These are the areas it was designed around; they describe intent rather than a hard boundary.
RESP AI— where can I download it?
The weights are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
RESP AI— what GPU do I need to run it?
We cannot say. It 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.
RESP AI— is it open source?
Its weights are published, so it 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.
RESP AI— 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.
RESP AI— who created it?
It was published by University of California San Diego, based in United States of America, an organisation categorised as 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.