GPT-MolBERTa
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
- Carnegie Mellon University (CMU)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 20 September 2023
- Authors
- Suryanarayanan Balaji, Rishikesh Magar, Yayati Jadhav, Amir Barati Farimani
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Molecular property 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
- 9,780,000 tokens
326,000 molecules × 100 tokens/molecule = 32,600,000 (3.26e7) total tokens
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 Python code and datasets used in this study can be accessed on GitHub using the following link: https://github.com/Suryanarayanan-Balaji/GPT-MolBERTa MIT license
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
- Citations
- 25
Sources
Where this record came from and when it was last checked.
- Reference
- GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction
- Last updated
- 25 May 2026
What the numbers mean
About this model
GPT-MolBERTa was published by Carnegie Mellon University (CMU), in the country recorded as United States of America, during September 2023. The category the publisher falls under is academia.
It works in the domain of Biology, and is recorded as performing the task of molecular property prediction.
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.
Training and provenance
It was trained on a corpus of about 9,780,000 tokens of text.
Answers
GPT-MolBERTa — common questions
GPT-MolBERTa— 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.
GPT-MolBERTa— 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.
GPT-MolBERTa— 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.
GPT-MolBERTa— 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.
GPT-MolBERTa— who created it?
It was published by Carnegie Mellon University (CMU), based in United States of America, an organisation categorised as academia.
GPT-MolBERTa— when was it released?
It was published in September 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.
GPT-MolBERTa— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of molecular property prediction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
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.