DNA Fine-Tuned Language Model (DFLM)
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
- Tongji University
- Organisation type
- Academia
- Country
- China
- Published
- 2 January 2023
- Authors
- Ying He , Qinhu Zhang , Siguo Wang , Zhanheng Chen , Zhen Cui , Zhen-Hao Guo, and De-Shuang Huang
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
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
- 3.5 × 10¹⁸ FLOP
- How it was established
- Hardware
"The pre-training of Human genome language model is resource-intensive (about 7-10 days on 2 NVIDIA TITAN X GPU)." Assuming FP32 and 30% utilization Estimate: (10*24*3600) s * 6.7e12 FLOP/s * 2 * 0.3 = 3.5e18
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 TITAN Xp
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
MIT for code. not sure there are weights here? https://github.com/Deep-Bioinfo/DFLM
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
- 7
Sources
Where this record came from and when it was last checked.
- Reference
- Predicting the Sequence Specificities of DNA-Binding Proteins by DNA Fine-Tuned Language Model With Decaying Learning Rates
- Last updated
- 11 February 2026
What the numbers mean
What this model is
DNA Fine-Tuned Language Model (DFLM) was published by Tongji University, in China, in January 2023. academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Producing it required around 3.5 × 10¹⁸ FLOP of arithmetic, on NVIDIA TITAN Xp, which is a statement about the training budget rather than about inference.
Answers
DNA Fine-Tuned Language Model (DFLM) — common questions
What GPU do I need to run DNA Fine-Tuned Language Model (DFLM)?
None. DNA Fine-Tuned Language Model (DFLM) 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 DNA Fine-Tuned Language Model (DFLM) open source?
No. DNA Fine-Tuned Language Model (DFLM) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does DNA Fine-Tuned Language Model (DFLM) have?
No parameter count has been published for DNA Fine-Tuned Language Model (DFLM), which is why no memory or speed figure appears on this page.
Who created DNA Fine-Tuned Language Model (DFLM)?
DNA Fine-Tuned Language Model (DFLM) was published by Tongji University, based in China, categorised as academia.
When was DNA Fine-Tuned Language Model (DFLM) released?
DNA Fine-Tuned Language Model (DFLM) was published in January 2023. 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 DNA Fine-Tuned Language Model (DFLM) used for?
DNA Fine-Tuned Language Model (DFLM) works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM). These are the areas it was designed around; they describe intent rather than a hard boundary.
How much compute was used to train DNA Fine-Tuned Language Model (DFLM)?
Around 3.5 × 10¹⁸ FLOP, on NVIDIA TITAN Xp. 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.
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.