# `MlPutTrainedModelRequest` [interface-MlPutTrainedModelRequest]

| Name | Type | Description |
| - | - | - |
| `body` | string | ({ [key: string]: any; } & { model_id?: never; defer_definition_decompression?: never; wait_for_completion?: never; compressed_definition?: never; definition?: never; description?: never; inference_config?: never; input?: never; metadata?: never; model_type?: never; model_size_bytes?: never; platform_architecture?: never; tags?: never; prefix_strings?: never; }) | All values in `body` will be added to the request body. |
| `compressed_definition` | string | The compressed (GZipped and Base64 encoded) inference definition of the model. If compressed_definition is specified, then definition cannot be specified. |
| `defer_definition_decompression` | boolean | If set to `true` and a `compressed_definition` is provided, the request defers definition decompression and skips relevant validations. |
| `definition` | [MlPutTrainedModelDefinition](./MlPutTrainedModelDefinition.md) | The inference definition for the model. If definition is specified, then compressed_definition cannot be specified. |
| `description` | string | A human-readable description of the inference trained model. |
| `inference_config` | [MlInferenceConfigCreateContainer](./MlInferenceConfigCreateContainer.md) | The default configuration for inference. This can be either a regression or classification configuration. It must match the underlying definition.trained_model's target_type. For pre-packaged models such as ELSER the config is not required. |
| `input` | [MlPutTrainedModelInput](./MlPutTrainedModelInput.md) | The input field names for the model definition. |
| `metadata` | any | An object map that contains metadata about the model. |
| `model_id` | [Id](./Id.md) | The unique identifier of the trained model. |
| `model_size_bytes` | [long](./long.md) | The estimated memory usage in bytes to keep the trained model in memory. This property is supported only if defer_definition_decompression is true or the model definition is not supplied. |
| `model_type` | [MlTrainedModelType](./MlTrainedModelType.md) | The model type. |
| `platform_architecture` | string | The platform architecture (if applicable) of the trained mode. If the model only works on one platform, because it is heavily optimized for a particular processor architecture and OS combination, then this field specifies which. The format of the string must match the platform identifiers used by Elasticsearch, so one of, `linux-x86_64`, `linux-aarch64`, `darwin-x86_64`, `darwin-aarch64`, or `windows-x86_64`. For portable models (those that work independent of processor architecture or OS features), leave this field unset. |
| `prefix_strings` | [MlTrainedModelPrefixStrings](./MlTrainedModelPrefixStrings.md) | Optional prefix strings applied at inference |
| `querystring` | { [key: string]: any; } & { model_id?: never; defer_definition_decompression?: never; wait_for_completion?: never; compressed_definition?: never; definition?: never; description?: never; inference_config?: never; input?: never; metadata?: never; model_type?: never; model_size_bytes?: never; platform_architecture?: never; tags?: never; prefix_strings?: never; } | All values in `querystring` will be added to the request querystring. |
| `tags` | string[] | An array of tags to organize the model. |
| `wait_for_completion` | boolean | Whether to wait for all child operations (e.g. model download) to complete. |