Protllm
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
- Beijing Institute of Technology,Beihang University,Peking University,Smart Grid Research Institute,Shanghai AI Lab
- Organisation type
- Academia,Academia,Academia,Academia
- Country
- China
- Published
- 28 February 2024
- Authors
- Le Zhuo, Zewen Chi, Minghao Xu, Heyan Huang, Heqi Zheng, Conghui He, Xian-Ling Mao, Wentao Zhang
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
- LLaMA-7B
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
Summary of calculations: PubMed Articles: 165,206 × 10,000 = 1.65206 × 10^9 UniProt Annotations: 64,634 × 100 = 6.4634 × 10^6 STRING Annotations: 25,682 × 100 = 2.5682 × 10^6 Mol-Instructions: 173,973 × 500 = 86.9865 × 10^6 Dataset total: 1.65206 × 10^9 + 6.4634 × 10^6 + 2.5682 × 10^6 + 86.9865 × 10^6 = 1.7475 × 10^9 Training tokens: 10,000 × 256 × 512 = 1.31072 × 10^9 Final estimate: 1.31 × 10^9 data points Training step based estimate: 10000 steps with batch size 256 and length 512 (Tabl…
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
- Operation counting
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
- 4
- Power draw
- 3.2 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
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Open (non-commercial)
no clear license for training and inference code https://github.com/ProtLLM/ProtLLM/tree/main weights are announced but not released https://huggingface.co/ProtLLM
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
- 29
Sources
Where this record came from and when it was last checked.
- Reference
- ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Protllm was published by Beijing Institute of Technology,Beihang University,Peking University,Smart Grid Research Institute,Shanghai AI Lab, in the country recorded as China, during February 2024. It comes out of an organisation categorised as academia,Academia,Academia,Academia.
It works in the domain of Biology, and is recorded as performing the task of protein or nucleotide language model (pLM/nLM).
It builds on LLaMA-7B. That is the usual way a specialised model is produced.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
Protllm — common questions
Protllm— what GPU do I need to run it?
None. This 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.
Protllm— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Protllm— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Protllm— who created it?
It was published by Beijing Institute of Technology,Beihang University,Peking University,Smart Grid Research Institute,Shanghai AI Lab, based in China, an organisation categorised as academia,Academia,Academia,Academia.
Protllm— when was it released?
It 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.
Protllm— what is it used for?
It works in the domain of Biology, and is recorded as handling the task of protein or nucleotide language model (pLM/nLM). 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.
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.