McMLP
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
- Harvard Medical School,University of Illinois Urbana-Champaign (UIUC),Harvard TH Chan School of Public Health
- Organisation type
- Academia,Academia,Academia
- Country
- United States of America
- Published
- 19 September 2024
- Authors
- Tong Wang, Hannah D Holscher, Sergei Maslov, Frank B Hu, Scott T Weiss, Yang-Yu Liu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Human physiology
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
Training data points calculation: Synthetic: 250 * 0.8 = 200 points Avocado study: 132 points Total = 200 + 132 = 332 points (3.32e2)
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
"Code Availability. The code of McMLP was deposited to the same McMLP GitHub repository." MIT license https://github.com/wt1005203/McMLP
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
- Predicting metabolite response to dietary intervention using deep learning
- Last updated
- 28 November 2025
What the numbers mean
What this model is
McMLP was published by Harvard Medical School,University of Illinois Urbana-Champaign (UIUC),Harvard TH Chan School of Public Health, in United States of America, in September 2024. academia,Academia,Academia is the category the publisher falls under.
It works in Biology, and is recorded as doing human physiology.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
McMLP — common questions
When was McMLP released?
McMLP 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.
What is McMLP used for?
McMLP works in Biology, and is recorded as handling human physiology. 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 McMLP?
None. McMLP 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 McMLP open source?
No. McMLP has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does McMLP have?
No parameter count has been published for McMLP, which is why no memory or speed figure appears on this page.
Who created McMLP?
McMLP was published by Harvard Medical School,University of Illinois Urbana-Champaign (UIUC),Harvard TH Chan School of Public Health, based in United States of America, categorised as academia,Academia,Academia.
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.