DiffPALM
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
- Ecole Polytechnique F´ed´erale de Lausanne (EPFL),Swiss Institute of Bioinformatics
- Organisation type
- Academia
- Country
- Switzerland
- Published
- 24 June 2024
- Authors
- Umberto Lupo, Damiano Sgarbossa, Anne-Florence Bitbol
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM)
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
Single MSA data points = 125 × 300 = 37,500 tokens Total data points = 37,500 × 1,000 = 37,500,000 tokens Final estimate = 4.0 × 10^7 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
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open source
Apache 2.0 for code https://github.com/Bitbol-Lab/DiffPALM/tree/v1.0
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
- 19
Sources
Where this record came from and when it was last checked.
- Reference
- Pairing interacting protein sequences using masked language modeling
- Last updated
- 1 January 2026
What the numbers mean
About this model
DiffPALM was published by Ecole Polytechnique F´ed´erale de Lausanne (EPFL),Swiss Institute of Bioinformatics, in Switzerland, in June 2024. academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
DiffPALM — common questions
How many parameters does DiffPALM have?
No parameter count has been published for DiffPALM, which is why no memory or speed figure appears on this page.
Who created DiffPALM?
DiffPALM was published by Ecole Polytechnique F´ed´erale de Lausanne (EPFL),Swiss Institute of Bioinformatics, based in Switzerland, categorised as academia.
When was DiffPALM released?
DiffPALM was published in June 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.
What is DiffPALM used for?
DiffPALM works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
What GPU do I need to run DiffPALM?
None. DiffPALM 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 DiffPALM open source?
No. DiffPALM has not had its weights published, so it exists only as a service controlled by its owner.
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.