MolMVC

Open weights Central South University,Singapore Agency for Science September 2024

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
Central South University,Singapore Agency for Science
Organisation type
Academia,Government
Country
China, Singapore
Published
4 September 2024
Authors
Zhijian Huang, Ziyu Fan, Siyuan Shen, Min Wu, Lei Deng

What it does

The problem areas the model was built for. A model can carry several of each.

Domain
Biology
Task
Molecular representation learning

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

Total Datapoints = 3.4M molecular instances = 3.4e6 (Direct value from dataset size, no calculations needed)

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 (non-commercial)
Training code
Open (non-commercial)

no clear license https://github.com/Hhhzj-7/MolMVC

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
MolMVC: Enhancing molecular representations for drug-related tasks through multi-view contrastive learning
Last updated
28 November 2025

What the numbers mean

About this model

MolMVC was published by Central South University,Singapore Agency for Science, in China, in September 2024. The organisation is categorised as academia,Government.

It works in Biology, and is recorded as doing molecular representation learning.

Because its weights were released, nothing about running it depends on a provider staying available — it is yours once downloaded.

Answers

MolMVC — common questions

01

Who created MolMVC?

MolMVC was published by Central South University,Singapore Agency for Science, based in China, categorised as academia,Government.

02

When was MolMVC released?

MolMVC 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.

03

What is MolMVC used for?

MolMVC works in Biology, and is recorded as handling molecular representation learning. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

04

Where can I download MolMVC?

The weights for MolMVC are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.

05

What GPU do I need to run MolMVC?

We cannot say. MolMVC 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.

06

Is MolMVC open source?

Its weights are published, so MolMVC 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.

07

How many parameters does MolMVC have?

No parameter count has been published for MolMVC, which is why no memory or speed figure appears on this page.

Source

Original publication

Record last updated 28 November 2025

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.