> ## Documentation Index > Fetch the complete documentation index at: https://proto.evodesign.org/docs/llms.txt > Use this file to discover all available pages before exploring further. # ESM2 Protein Language Model > ESM-2 masked protein language model for local sequence mutation/refinement
ESM2 Protein Language Model
**License:** ESM2 is open source and free for academic and commercial use under an MIT license. Please refer to [the license](https://github.com/facebookresearch/esm/blob/main/LICENSE) for full terms.

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evo-design/proto-language/proto\_language/generator/esm2\_generator.py
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```bibtex theme={null} @article{lin2023esm2, title={Evolutionary-scale prediction of atomic-level protein structure with a language model}, author={Lin, Zeming and Akin, Halil and Rao, Roshan and Hie, Brian and Zhu, Zhongkai and Lu, Wenting and Smetanin, Nikita and Verkuil, Robert and Kabeli, Ori and Shmueli, Yaniv and others}, journal={Science}, volume={379}, number={6637}, pages={1123--1130}, year={2023}, publisher={American Association for the Advancement of Science}, doi={10.1126/science.ade2574} } ```
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Protein sequence mutation/refinement generator using ESM2 language model. This generator uses the ESM2 protein language model to refine existing protein sequences through iterative mutation. It masks positions according to the configured masking strategy and samples biologically plausible amino acids at those positions. The generator category is `"mutation"`, indicating it refines proposal sequences through targeted mutations. ## API Reference
ConfigESM2GeneratorConfig Source
Configuration object for ESM2Generator. This class defines configuration parameters for the ESM2 generator, which uses a protein language model to refine existing protein sequences through iterative mutation of masked positions. In Proto Language, ESM2 is registered as a mutation-category generator that edits the supplied starting sequence; the segment must carry a sequence (directly or from a prior optimizer stage). ESM-2 model variant to load (e.g. esm2\_t33\_650M\_UR50D). Options: `esm2_t6_8M_UR50D`, `esm2_t12_35M_UR50D`, `esm2_t30_150M_UR50D`, `esm2_t33_650M_UR50D`, `esm2_t36_3B_UR50D`, `esm2_t48_15B_UR50D` Controls which positions to mask for sampling. Default: random 30%. 'single\_pass' fills all masks in one forward; 'iterative\_refinement' runs a MaskGIT-style loop. Options: `single_pass`, `iterative_refinement` Sharpness of sampling. Below 1 sharpens toward the likely amino acid; above 1 increases diversity. Nucleus sampling cumulative probability cutoff used in iterative refinement. 1.0 disables it. Number of iterative-refinement rounds. Returns diminish above 20. Per-round unmask rate. 'cosine' commits more positions late; 'linear' commits the same each round. Options: `cosine`, `linear` How positions are picked each round. 'entropy' takes most-confident first; 'random' is uniform. Options: `random`, `entropy` Anneal temperature toward 0 across rounds GPU device to run ESM2 on (e.g. 'cuda' or 'cuda:0'). Number of sequences to process simultaneously on GPU
## Usage ```python python icon="python" theme={null} >>> from proto_language.generator import ESM2Generator, ESM2GeneratorConfig >>> from proto_language.core import Segment >>> from proto_tools.transforms.masking import MaskingStrategy >>> config = ESM2GeneratorConfig( ... temperature=1.0, ... masking_strategy=MaskingStrategy(num_mutations=5), ... ) >>> gen = ESM2Generator(config) >>> segment = Segment(sequence="M" * 100, sequence_type="protein") >>> gen.assign(segment) >>> gen.sample() # Re-samples 5 randomly masked positions ``` ## Metadata | Property | Value | | ------------------------ | ------------------- | | Key | `esm2` | | Class | `ESM2Generator` | | Category | `mutation` | | Input Type | `starting_sequence` | | Uses GPU | `True` | | Supported Sequence Types | `protein` | | Allows Empty Start | `False` |