| Property | Value |
|---|---|
| Parameter Count | 650M parameters |
| License | MIT |
| Author | |
| Model Type | Protein Language Model |
| Framework Support | PyTorch, TensorFlow |
What is esm2_t33_650M_UR50D?
ESM2_t33_650M_UR50D is a medium-sized protein language model featuring 33 layers and 650 million parameters. It's part of Facebook's ESM-2 family of models, designed for protein sequence analysis through masked language modeling. This particular variant offers a balanced compromise between computational efficiency and performance.
Implementation Details
The model is implemented using both PyTorch and TensorFlow frameworks, supporting Fill-Mask operations and utilizing Safetensors for efficient tensor storage. It's trained on a masked language modeling objective, specifically designed for protein sequence analysis.
- 33-layer transformer architecture
- 650M parameter size - middle ground in the ESM-2 family
- Supports both F32 and I64 tensor types
- Trained on the UR50D dataset
Core Capabilities
- Protein sequence analysis and prediction
- Masked language modeling for protein sequences
- Fine-tunable for various protein-related tasks
- Sequence-to-sequence protein modeling
Frequently Asked Questions
Q: What makes this model unique?
This model represents a sweet spot in the ESM-2 family, offering good performance while being more manageable than larger variants like the 15B parameter model. It's particularly suitable for research and production environments where computational resources are limited but high-quality protein analysis is required.
Q: What are the recommended use cases?
The model is ideal for protein sequence analysis, structure prediction, and protein engineering applications. It can be fine-tuned for specific tasks like protein function prediction, stability assessment, and sequence generation.