esm2_t6_8 M_ U R50 D

Introduction

ESM-2 is an advanced protein model designed for masked language modeling tasks. It excels at processing protein sequences and can be fine-tuned for various specific tasks. The model's architecture and training details are available in the associated research paper.

Architecture

ESM-2 utilizes a deep neural network architecture tailored for processing protein sequences. It is available in several configurations, varying in size and number of parameters:

• esm2_t48_15B_UR50D: 48 layers, 15 billion parameters
• esm2_t36_3B_UR50D: 36 layers, 3 billion parameters
• esm2_t33_650M_UR50D: 33 layers, 650 million parameters
• esm2_t30_150M_UR50D: 30 layers, 150 million parameters
• esm2_t12_35M_UR50D: 12 layers, 35 million parameters
• esm2_t6_8M_UR50D: 6 layers, 8 million parameters

Larger models generally offer better accuracy but demand more computational resources.

Training

The model is trained using a masked language modeling objective, which is effective for understanding and predicting protein sequences. This method involves training the model to predict masked portions of sequences based on the context provided by the surrounding amino acids.

Guide: Running Locally

To run ESM-2 locally, follow these steps:

1. Setup Environment: Ensure you have Python installed. Use virtual environments to manage dependencies.

2. Install Required Libraries: Use pip to install necessary libraries, such as transformers and torch or tensorflow.

   pip install transformers torch
   

3. Download Model: Use the Hugging Face Transformers library to load the model.

   from transformers import AutoModelForMaskedLM, AutoTokenizer
   
   model_name = "facebook/esm2_t6_8M_UR50D"
   model = AutoModelForMaskedLM.from_pretrained(model_name)
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   

4. Run Model: You can now input protein sequences and perform tasks like masked token prediction.

5. Cloud GPU Suggestion: For larger models, consider using cloud-based GPU services such as AWS, Google Cloud, or Azure to handle the computational load efficiently.

License

The ESM-2 model is released under the MIT License, allowing for flexibility in use, modification, and distribution.