EvolutionaryScale · Schema
EvolutionaryScale GenerationConfig
Schema for the `GenerationConfig` object that controls iterative masked sampling for the EvolutionaryScale Forge ESM3 generate endpoint.
Artificial IntelligenceBiologyBioinformaticsComputational BiologyDrug DiscoveryESMESM3ESM CambrianFoundation ModelsGenerative BiologyLife SciencesMachine-LearningProtein DesignProtein FoldingProtein Language ModelsProteinsRepresentation LearningStructure Prediction
Properties
| Name | Type | Description |
|---|---|---|
| track | string | Which ESM3 track to generate. Sequence is the most common; structure and function tracks require the multimodal model checkpoints. |
| num_steps | integer | Number of iterative masked-sampling steps. Lower values are faster; higher values produce more refined outputs. |
| temperature | number | Sampling temperature. 0.0 yields greedy decoding; 1.0 matches the trained distribution; higher values increase diversity. |
| top_p | number | Nucleus sampling cutoff. Samples from the smallest set of tokens whose cumulative probability is at least `top_p`. |
| schedule | string | Decoding schedule controlling how many positions are unmasked per step. |
| invalid_ids | array | Token IDs the sampler must never emit. Useful for constraining amino acid alphabets or forbidding rare structure tokens. |
| condition_on_coordinates_only | boolean | If true, condition only on coordinates (ignore sequence tokens) when generating other tracks. |
JSON Schema
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"$id": "https://api-evangelist.com/schemas/evolutionaryscale/evolutionaryscale-generation-config-schema.json",
"title": "EvolutionaryScale GenerationConfig",
"description": "Schema for the `GenerationConfig` object that controls iterative masked sampling for the EvolutionaryScale Forge ESM3 generate endpoint.",
"type": "object",
"required": ["track"],
"properties": {
"track": {
"type": "string",
"description": "Which ESM3 track to generate. Sequence is the most common; structure and function tracks require the multimodal model checkpoints.",
"enum": ["sequence", "structure", "secondary_structure", "sasa", "function"]
},
"num_steps": {
"type": "integer",
"description": "Number of iterative masked-sampling steps. Lower values are faster; higher values produce more refined outputs.",
"default": 8,
"minimum": 1,
"maximum": 256
},
"temperature": {
"type": "number",
"description": "Sampling temperature. 0.0 yields greedy decoding; 1.0 matches the trained distribution; higher values increase diversity.",
"default": 1.0,
"minimum": 0.0
},
"top_p": {
"type": "number",
"description": "Nucleus sampling cutoff. Samples from the smallest set of tokens whose cumulative probability is at least `top_p`.",
"minimum": 0.0,
"maximum": 1.0
},
"schedule": {
"type": "string",
"description": "Decoding schedule controlling how many positions are unmasked per step.",
"enum": ["cosine", "linear"]
},
"invalid_ids": {
"type": "array",
"description": "Token IDs the sampler must never emit. Useful for constraining amino acid alphabets or forbidding rare structure tokens.",
"items": { "type": "integer", "minimum": 0 }
},
"condition_on_coordinates_only": {
"type": "boolean",
"description": "If true, condition only on coordinates (ignore sequence tokens) when generating other tracks.",
"default": false
}
},
"additionalProperties": false
}
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