ESM2_t6_8M_UR50D
| Property | Value |
|---|---|
| Parameter Count | 7.84M parameters |
| License | MIT |
| Author | |
| Framework Support | PyTorch, TensorFlow |
What is esm2_t6_8M_UR50D?
ESM2_t6_8M_UR50D is the most lightweight variant of Facebook's ESM-2 protein language model family, designed for efficient protein sequence analysis. This 6-layer transformer model represents a careful balance between computational efficiency and performance, making it ideal for rapid prototyping and resource-constrained environments.
Implementation Details
The model implements a transformer architecture with 6 layers, optimized for masked language modeling of protein sequences. It utilizes both INT64 and FP32 tensor types and is available in both PyTorch and TensorFlow frameworks. The model has been trained on protein sequence data with a specific focus on the UR50D dataset.
- Lightweight architecture with 7.84M parameters
- Supports masked language modeling for protein sequences
- Available in multiple framework implementations
- Trained on comprehensive protein sequence data
Core Capabilities
- Protein sequence analysis and prediction
- Masked language modeling for protein sequences
- Feature extraction for downstream protein analysis tasks
- Efficient inference with minimal computational requirements
Frequently Asked Questions
Q: What makes this model unique?
This model represents the most compact version of the ESM-2 family, offering a great entry point for protein sequence analysis with minimal computational overhead. Its 6-layer architecture makes it particularly suitable for rapid prototyping and applications where computational resources are limited.
Q: What are the recommended use cases?
The model is ideal for initial protein sequence analysis, proof-of-concept implementations, and scenarios where computational efficiency is prioritized over maximum accuracy. It's particularly useful for educational purposes and small-scale protein research projects.
