[!NOTE]
This model has been optimized using NVIDIA's
TransformerEngine
library. Slight numerical differences may be observed between the original model and the optimized
version. For instructions on how to install TransformerEngine, please refer to the
official documentation.
ESM-2 (TransformerEngine-Optimized) Overview
Description:
ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It predicts protein
structures from amino acid sequences, leveraging a transformer-based architecture for accurate 3D modeling. It is
suitable for fine-tuning on a wide range of tasks that take protein sequences as input.
This version of the ESM-2 model is optimized with NVIDIA's
TransformerEngine library. It is based on the original ESM-2 model from
Facebook Research, and (within numerical precision) has identical weights and outputs.
This model is ready for commercial/non-commercial use.
Third-Party Community Consideration
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements
for this application and use case; see link to Non-NVIDIA Model Card
ESM-2 Model
Card.
License/Terms of Use:
ESM-2 is licensed under the
MIT license.
Deployment Geography:
Global
Use Case:
Protein structure prediction, specifically predicting 3D protein structures from amino acid sequences.
Release Date:
Reference(s):
- Evolutionary-scale prediction of atomic level protein structure with a language
model - detailed information on the model architecture
and training data, please refer to the accompanying [paper].
- Demo notebooks
(PyTorch,
TensorFlow)
which demonstrate how to fine-tune ESM-2 models on your tasks of interest.
Model Architecture:
Architecture Type: Transformer
Network Architecture: ESM-2
This model was developed based on: ESM-2
Number of model parameters: 7.5 x 10^6
Input:
Input Type: Text (Protein Sequences)
Input Format: String
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: Protein sequence represented as a string of canonical amino acids, of maximum
length 1022. Longer sequences are automatically truncated to this length.
Output:
Output Type: Embeddings (Amino acid and sequence-level)
Output Format: Vector
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: Numeric vector with floating-point values corresponding to an embedding for each
amino acid in the input protein sequence. Maximum output length is 1022 embeddings - one embedding vector per amino
acid.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware
(e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times
compared to CPU-only solutions.
Software Integration:
Runtime Engine(s):
- Hugging Face Transformers
Supported Hardware Microarchitecture Compatibility:
- NVIDIA Ampere
- NVIDIA Blackwell
- NVIDIA Hopper
[Preferred/Supported] Operating System(s):
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific
data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at
both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure
compliance with safety and ethical standards before deployment.
Model Version: This model features the following version/checkpoints:
Several ESM-2 checkpoints are available with varying sizes. Larger sizes have better accuracy, but require more memory
and time to train:
Training and Evaluation Datasets:
Training Datasets:
Data Modality:
Text Training Data Size:
- 1 Billion to 10 Trillion Tokens
Data Collection Method:
Labeling Method:
Properties (Quantity, Dataset Descriptions, Sensor(s)): UniRef90 clusters are generated from the UniRef100 seed
sequences with a 90% sequence identity threshold using the MMseqs2 algorithm. The seed sequences are the longest members
of the UniRef100 cluster. However, the longest sequence is not always the most informative. There is often more
biologically relevant information and annotation (name, function, cross-references) available on other cluster members.
All the proteins in each cluster are ranked to facilitate the selection of a biologically relevant representative for
the cluster.
Data Modality:
Text Training Data Size:
- 1 Billion to 10 Trillion Tokens
Data Collection Method:
Labeling Method:
Properties: UniRef50 clusters are generated from the UniRef90 seed sequences with a 50% sequence identity threshold
using the MMseqs2 algorithm. The seed sequences are the longest members of the UniRef90 cluster. However, the longest
sequence is not always the most informative. There is often more biologically relevant information and annotation (name,
function, cross-references) available on other cluster members. All the proteins in each cluster are ranked to
facilitate the selection of a biologically relevant representative for the cluster.
Evaluation Datasets:
Benchmark Score: 0.48
Data Collection Method:
Labeling Method:
Properties: The data is collected by taking sequences of protein structures that are about to be released weekly by
the Protein Data Bank (PDB). These sequences are sent as "blind targets" to participating protein structure prediction
servers, which then return their predictions.
Benchmark Score: 0.37
Data Collection Method:
Labeling Method:
Properties: The data for CASP14 targets is collected from protein structures that are newly solved by experimental
structural biologists. The CASP organizers receive the amino acid sequences of these proteins before their full,
three-dimensional structures are publicly released in the Protein Data Bank (PDB). They then provide these sequences to
participating research groups and servers, who must submit their predicted structures within a specific time frame.
Inference:
Acceleration Engine:
- Hugging Face Transformers
Test Hardware:
Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable
development for a wide array of AI applications. When downloaded or used in accordance with our terms of service,
developers should work with their internal model team to ensure this model meets requirements for the relevant industry
and use case and addresses unforeseen product misuse.
Users are responsible for ensuring the physical properties of model-generated molecules are appropriately evaluated and
comply with applicable safety regulations and ethical standards.
Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns
here.
