RENNAISSANCE
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
- Ecole Polytechnique F´ed´erale de Lausanne (EPFL),University of Cambridge,Harvard Medical School
- Organisation type
- Academia,Academia,Academia
- Country
- Switzerland, United Kingdom of Great Britain and Northern Ireland, United States of America
- Published
- 30 August 2024
- Authors
- Subham Choudhury, Bharath Narayanan, Michael Moret, Vassily Hatzimanikatis, Ljubisa Miskovic
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Cell Biology
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
"a machine learning framework that efficiently parameterizes biologically relevant kinetic models of metabolism without requiring training data."
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
Code availability A Python implementation of the RENAISSANCE workflow is publicly available via GitHub at https://github.com/EPFL-LCSB/renaissance (apache 2.0) and https://gitlab.com/EPFL-LCSB/renaissance. The ORACLE framework is implemented in the SKimPy (Symbolic Kinetic models in Python) toolbox, available via GitHub at https://github.com/EPFL-LCSB/skimpy Creative Commons Attribution 4.0 International for weights https://zenodo.org/records/7930002
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 10
Sources
Where this record came from and when it was last checked.
- Reference
- Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states
- Last updated
- 28 November 2025
What the numbers mean
What this model is
RENNAISSANCE was published by Ecole Polytechnique F´ed´erale de Lausanne (EPFL),University of Cambridge,Harvard Medical School, in the country recorded as Switzerland, during August 2024. The category the publisher falls under is academia,Academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of cell Biology.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached.
Answers
RENNAISSANCE — common questions
RENNAISSANCE— 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.
RENNAISSANCE— who created it?
It was published by Ecole Polytechnique F´ed´erale de Lausanne (EPFL),University of Cambridge,Harvard Medical School, based in Switzerland, an organisation categorised as academia,Academia,Academia.
RENNAISSANCE— when was it released?
It was published in August 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.
RENNAISSANCE— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of cell Biology. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
RENNAISSANCE— 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.
RENNAISSANCE— 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.
RENNAISSANCE— 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.
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.