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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 for details.
provider str Yes Reference to the name of the Provider to use (e.g., "nvidia", "openai", "openrouter").
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.

Upgrade note

Every ModelConfig must now specify provider. Existing model_configs.yaml entries from older releases that omit provider or set it to null must be updated with an explicit provider name before loading. Agent tooling that parses data-designer agent context should read each model alias item's provider field; the top-level default_provider and per-item configured_provider / effective_provider fields are no longer emitted.

Examples

Basic Model Configuration

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

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-3-nano-omni-30b-a3b-reasoning",
        provider="nvidia",
        inference_parameters=dd.ChatCompletionInferenceParams(
            temperature=0.60,
            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:

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