MPDF
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
- Chinese University of Hong Kong (CUHK),Lanzhou University,Zhejiang Lab,Zhejiang University (ZJU)
- Organisation type
- Academia,Academia,Academia
- Country
- Hong Kong, China
- Published
- 7 September 2024
- Authors
- Chunbin Gu, Mutian He, Hanqun Cao, Guangyong Chen, Chang-yu Hsieh, Pheng Ann Heng
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Drug discovery
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 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.
- Training compute
- 3.1 × 10¹⁷ FLOP
- How it was established
- Hardware
1. Hardware: 1x NVIDIA GeForce RTX 3090 2. Training duration: 6 hours (max of 1-6 hour range) = 21,600 seconds 3. Utilization: 40% 4. Calculation: 35580000000000*6*60*60*0.4=3.074112e+17
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce RTX 3090
- Chips used
- 1
- Wall-clock time
- 6 hours
- Power draw
- 379 W
"with training times ranging from 1 to 6 hours, depending on the complexity and size of the different building blocks."
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
- Unreleased
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
- 2
Sources
Where this record came from and when it was last checked.
- Reference
- Unlocking Potential Binders: Multimodal Pretraining DEL-Fusion for Denoising DNA-Encoded Libraries
- Last updated
- 28 November 2025
What the numbers mean
About this model
MPDF was published by Chinese University of Hong Kong (CUHK),Lanzhou University,Zhejiang Lab,Zhejiang University (ZJU), in Hong Kong, in September 2024. The organisation is categorised as academia,Academia,Academia.
It works in Biology, and is recorded as doing drug discovery.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training it took roughly 3.1 × 10¹⁷ FLOP of computation, on NVIDIA GeForce RTX 3090 — a measure of what producing the model cost, not of how fast it answers.
Answers
MPDF — common questions
Who created MPDF?
MPDF was published by Chinese University of Hong Kong (CUHK),Lanzhou University,Zhejiang Lab,Zhejiang University (ZJU), based in Hong Kong, categorised as academia,Academia,Academia.
When was MPDF released?
MPDF 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 MPDF used for?
MPDF works in Biology, and is recorded as handling drug discovery. 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.
How much compute was used to train MPDF?
Around 3.1 × 10¹⁷ FLOP, on NVIDIA GeForce RTX 3090. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
What GPU do I need to run MPDF?
None. MPDF 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 MPDF open source?
No. MPDF has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does MPDF have?
No parameter count has been published for MPDF, which is why no memory or speed figure appears on this page.
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.