AbGPT
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
- Carnegie Mellon University (CMU)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 9 September 2024
- Authors
- Desmond Kuan, Amir Barati Farimani
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein design
- Base model
- ProtGPT2
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
- 734M
- Training data
- 6,840,000,000 tokens
- Epochs
- 5
Finetuned ProtGPT2 on 57M sequences with average length of ~120 (Figure 6) FT dataset in tokens: 57000000*120=6840000000
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
- 4.3 × 10²¹ FLOP
Finetuned ProtGPT2 on 57M sequences with average length of ~120 (Figure 6) FT compute: 734000000*6840000000*5*6=1.506168e+20 Base model compute: 4.1e+21 Total: 4.2506168e+21
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 RTX A6000
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
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
MIT license: https://github.com/deskk/AbGPT
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
- AbGPT: De Novo Antibody Design via Generative Language Modeling
- Last updated
- 28 November 2025
What the numbers mean
About this model
AbGPT was published by Carnegie Mellon University (CMU), in the country recorded as United States of America, during September 2024. The category the publisher falls under is academia.
It works in the domain of Biology, and is recorded as performing the task of protein design.
Rather than being trained from scratch, it is derived from ProtGPT2. That is why it shares the base model's general shape and size.
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
Training it took a computation budget of roughly 4.3 × 10²¹ FLOP, on hardware recorded as NVIDIA RTX A6000. That figure measures what producing the model cost, and has no bearing on how fast it answers.
It was trained on a corpus of about 6,840,000,000 tokens of text.
Answers
AbGPT — common questions
AbGPT— when was it released?
It 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.
AbGPT— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein design. These are the areas it was designed around; they describe intent rather than a hard boundary.
AbGPT— how much compute was used to train it?
Training consumed around 4.3 × 10²¹ FLOP, on hardware recorded as NVIDIA RTX A6000. 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.
AbGPT— 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.
AbGPT— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
AbGPT— how many parameters does it have?
It has a parameter count of 734M. 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.
AbGPT— who created it?
It was published by Carnegie Mellon University (CMU), based in United States of America, an organisation categorised as academia.
The other direction
Looking at it from the other side?
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