ProteinChat
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 California San Diego,BioMap Research,The Scripps Research Institute,Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)
- Organisation type
- Academia,Industry,Academia
- Country
- United States of America, China, United Arab Emirates
- Published
- 10 October 2024
- Authors
- Mingjia Huo, Han Guo, Xingyi Cheng, Digvijay Singh, Hamidreza Rahmani, Shen Li, Philipp Gerlof, Trey Ideker, Danielle A. Grotjahn, Elizabeth Villa, Le Song, Pengtao Xie
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein function prediction
- Base model
- xTrimoPGLM - 1B,Vicuna-13B v0
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
- 14B
- Training data
- tokens
"We utilized xTrimoPGLM (27), a state-of-the-art protein language model, as the protein encoder, and Vicuna- 13B (25), fine-tuned from Llama-2 (21), as the LLM of ProteinChat."
Assuming average protein length of 300 tokens (600 is the maximum length), 20 tokens for the prompt and 50 tokens for the answer. 1500000 examples * (300 tokens per protein + 70 prompt tokens) * 0.9 training split =499500000
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.
- How it was established
- Hardware
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 8
Sources
Where this record came from and when it was last checked.
- Reference
- Multi-Modal Large Language Model Enables Protein Function Prediction
- Last updated
- 1 January 2026
What the numbers mean
Background
ProteinChat was published by University of California San Diego,BioMap Research,The Scripps Research Institute,Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), in United States of America, in October 2024. It comes out of academia,Industry,Academia.
It works in Biology, and is recorded as doing protein function prediction.
Its starting point was xTrimoPGLM - 1B,Vicuna-13B v0 — most models at this scale are adapted from an existing base rather than built from nothing.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
ProteinChat — common questions
What is ProteinChat used for?
ProteinChat works in Biology, and is recorded as handling protein function prediction. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run ProteinChat?
None. ProteinChat 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 ProteinChat open source?
The licensing for ProteinChat 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 ProteinChat have?
ProteinChat has 14B parameters. "We utilized xTrimoPGLM (27), a state-of-the-art protein language model, as the protein encoder, and Vicuna- 13B (25), fine-tuned from Llama-2 (21), as the LLM of ProteinChat.". 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.
Who created ProteinChat?
ProteinChat was published by University of California San Diego,BioMap Research,The Scripps Research Institute,Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), based in United States of America, categorised as academia,Industry,Academia.
When was ProteinChat released?
ProteinChat was published in October 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.