> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nemo/datadesigner/llms.txt. > For full documentation content, see https://docs.nvidia.com/nemo/datadesigner/llms-full.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/datadesigner/_mcp/server. # Model Configurations Model configurations define the specific models you use for synthetic data generation and their associated inference parameters. Each `ModelConfig` represents a named model that can be referenced throughout your data generation workflows. ## Overview A `ModelConfig` specifies which LLM model to use and how it should behave during generation. When you create column configurations (like `LLMText`, `LLMCode`, or `LLMStructured`), you reference a model by its alias. Data Designer uses the model configuration to determine which model to call and with what parameters. ## ModelConfig Structure The `ModelConfig` class has the following fields: | Field | Type | Required | Description | | ---------------------- | ------------------ | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `alias` | `str` | Yes | Unique identifier for this model configuration (e.g., `"my-text-model"`, `"reasoning-model"`) | | `model` | `str` | Yes | Model identifier as recognized by the provider (e.g., `"nvidia/nemotron-3-nano-30b-a3b"`, `"gpt-4"`) | | `inference_parameters` | `InferenceParamsT` | No | Controls model behavior during generation. Use `ChatCompletionInferenceParams` for text/code/structured generation or `EmbeddingInferenceParams` for embeddings. Defaults to `ChatCompletionInferenceParams()` if not provided. The generation type is automatically determined by the inference parameters type. See [Inference Parameters](/concepts/models/inference-parameters) for details. | | `provider` | `str` | No | Reference to the name of the Provider to use (e.g., `"nvidia"`, `"openai"`, `"openrouter"`). If not specified, one set as the default provider, which may resolve to the first provider if there are more than one | | `skip_health_check` | `bool` | No | Whether to skip the health check for this model. Defaults to `False`. Set to `True` to skip health checks when you know the model is accessible or want to defer validation. | ## Examples ### Basic Model Configuration ```python import data_designer.config as dd # Simple model configuration with fixed parameters model_config = dd.ModelConfig( alias="my-text-model", model="nvidia/nemotron-3-nano-30b-a3b", provider="nvidia", inference_parameters=dd.ChatCompletionInferenceParams( temperature=0.85, top_p=0.95, max_tokens=2048, ), ) ``` ### Multiple Model Configurations for Different Tasks ```python import data_designer.config as dd model_configs = [ # Creative tasks dd.ModelConfig( alias="creative-model", model="nvidia/nemotron-3-nano-30b-a3b", provider="nvidia", inference_parameters=dd.ChatCompletionInferenceParams( temperature=0.9, top_p=0.95, max_tokens=2048, ), ), # Critic tasks dd.ModelConfig( alias="critic-model", model="nvidia/nemotron-3-nano-30b-a3b", provider="nvidia", inference_parameters=dd.ChatCompletionInferenceParams( temperature=0.25, top_p=0.95, max_tokens=2048, ), ), # Reasoning and structured tasks dd.ModelConfig( alias="reasoning-model", model="nvidia/nemotron-3-super-120b-a12b", provider="nvidia", inference_parameters=dd.ChatCompletionInferenceParams( temperature=1.0, top_p=0.95, max_tokens=4096, ), ), # Vision tasks dd.ModelConfig( alias="vision-model", model="nvidia/nemotron-nano-12b-v2-vl", provider="nvidia", inference_parameters=dd.ChatCompletionInferenceParams( temperature=0.7, top_p=0.95, max_tokens=2048, ), ), # Embedding tasks dd.ModelConfig( alias="embedding_model", model="nvidia/llama-3.2-nv-embedqa-1b-v2", provider="nvidia", inference_parameters=dd.EmbeddingInferenceParams( encoding_format="float", extra_body={ "input_type": "query" } ) ) ] ``` Experiment with max\_tokens for Task-Specific Model Configurations The number of tokens required to generate a single data entry can vary significantly with use case. For example, reasoning models often need more tokens to "think through" problems before generating a response. Note that `max_tokens` specifies the **maximum number of output tokens** to generate in the response, so set this value based on the expected length of the generated content. ### Skipping Health Checks By default, Data Designer runs a health check for each model before starting data generation to ensure the model is accessible and configured correctly. You can skip this health check for specific models by setting `skip_health_check=True`: ```python import data_designer.config as dd model_config = dd.ModelConfig( alias="my-model", model="nvidia/nemotron-3-nano-30b-a3b", provider="nvidia", inference_parameters=dd.ChatCompletionInferenceParams( temperature=0.85, top_p=0.95, max_tokens=2048, ), skip_health_check=True, # Skip health check for this model ) ``` When to Skip Health Checks Skipping health checks can be useful when: * You've already verified the model is accessible and want to speed up initialization * You're using a model that doesn't support the standard health check format * You want to defer model validation until the model is actually used Note that skipping health checks means errors will only be discovered during actual data generation. ## See Also * **[Inference Parameters](/concepts/models/inference-parameters)**: Detailed guide to inference parameters and how to configure them * **[Model Providers](/concepts/models/model-providers)**: Learn about configuring model providers * **[Default Model Settings](/concepts/models/default-model-settings)**: Pre-configured model settings included with Data Designer * **[Custom Model Settings](/concepts/models/custom-model-settings)**: Learn how to create custom providers and model configurations * **[Configure Model Settings With the CLI](/concepts/models/configure-with-the-cli)**: Use the CLI to manage model settings * **[Column Configurations](/code-reference/topic-overviews/column-configs)**: Learn how to use models in column configurations * **[Architecture & Performance](/concepts/architecture-and-performance)**: Understanding separation of concerns and optimizing concurrency