PTM-Mamba
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
- Duke University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 29 February 2024
- Authors
- Zhangzhi Peng, Benjamin Schussheim, Pranam Chatterjee
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)
- Base model
- ESM2-650M
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
79,707 sequences × 400 tokens/sequence = 31,882,800 tokens ≈ 3.2 × 10⁷ 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.
- How it was established
- Hardware
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 A100
- Chips used
- 8
- Power draw
- 6.3 kW
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 source
- Hugging Face
- ChatterjeeLab
CC-BY-NC-ND-4.0 for weights https://huggingface.co/ChatterjeeLab/PTM-Mamba Apache 2.0 for inference and training code https://github.com/programmablebio/ptm-mamba?tab=readme-ov-file
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 45
Sources
Where this record came from and when it was last checked.
- Reference
- PTM-Mamba: A PTM-Aware Protein Language Model with Bidirectional Gated Mamba Blocks
- Last updated
- 1 January 2026
What the numbers mean
Background
PTM-Mamba was published by Duke University, in United States of America, in February 2024. academia is the category the publisher falls under.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM).
It is derived from ESM2-650M rather than trained from scratch, which is the usual way a specialised model is produced.
The weights are published, so it can be downloaded and run on your own hardware indefinitely, offline, with no account attached. It is published under the ChatterjeeLab organisation on Hugging Face.
Answers
PTM-Mamba — common questions
When was PTM-Mamba released?
PTM-Mamba was published in February 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 PTM-Mamba used for?
PTM-Mamba 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.
Where can I download PTM-Mamba?
Its weights are published under the ChatterjeeLab organisation on Hugging Face. We do not host model files — this site calculates what hardware is needed to run them.
What GPU do I need to run PTM-Mamba?
We cannot say. PTM-Mamba 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.
Is PTM-Mamba open source?
Its weights are published, so PTM-Mamba 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.
How many parameters does PTM-Mamba have?
No parameter count has been published for PTM-Mamba, which is why no memory or speed figure appears on this page.
Who created PTM-Mamba?
PTM-Mamba was published by Duke University, based in United States of America, categorised as 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.