BetterBodies
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
- University of Freiburg,Collaborative Research Institute Intelligent Oncology ,BrainLinks-BrainTools
- Organisation type
- Academia,Research collective,Academia
- Country
- Germany
- Published
- 9 September 2024
- Authors
- Yannick Vogt, Mehdi Naouar, Maria Kalweit, Christoph Cornelius Miething, Justus Duyster, Joschka Boedecker, Gabriel Kalweit
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein design
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
- 30,283 tokens
- Epochs
- 700
New estimate: 2753*11=30283 Total sequences: 2,500 + 2,753 + 2,167 = 7,420 Data points = 7,420 sequences × 11 residues/sequence = 81,620 data points Final estimate: 81,620
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
- BETTERBODIES: Reinforcement Learning Guided Diffusion for Antibody Sequence Design
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
BetterBodies was published by University of Freiburg,Collaborative Research Institute Intelligent Oncology ,BrainLinks-BrainTools, in Germany, in September 2024. It comes out of academia,Research collective,Academia.
It works in Biology, and is recorded as doing protein design.
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
It was trained on about 30,283 tokens of text.
Answers
BetterBodies — common questions
What is BetterBodies used for?
BetterBodies works in Biology, and is recorded as handling protein design. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run BetterBodies?
None. BetterBodies 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 BetterBodies open source?
The licensing for BetterBodies 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 BetterBodies have?
No parameter count has been published for BetterBodies, which is why no memory or speed figure appears on this page.
Who created BetterBodies?
BetterBodies was published by University of Freiburg,Collaborative Research Institute Intelligent Oncology ,BrainLinks-BrainTools, based in Germany, categorised as academia,Research collective,Academia.
When was BetterBodies released?
BetterBodies 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.