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`	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

1 import data_designer.config as dd
2 
3 # Simple model configuration with fixed parameters
4 model_config = dd.ModelConfig(
5     alias="my-text-model",
6     model="nvidia/nemotron-3-nano-30b-a3b",
7     provider="nvidia",
8     inference_parameters=dd.ChatCompletionInferenceParams(
9         temperature=0.85,
10         top_p=0.95,
11         max_tokens=2048,
12     ),
13 )

Multiple Model Configurations for Different Tasks

1 import data_designer.config as dd
2 
3 model_configs = [
4     # Creative tasks
5     dd.ModelConfig(
6         alias="creative-model",
7         model="nvidia/nemotron-3-nano-30b-a3b",
8         provider="nvidia",
9         inference_parameters=dd.ChatCompletionInferenceParams(
10             temperature=0.9,
11             top_p=0.95,
12             max_tokens=2048,
13         ),
14     ),
15     # Critic tasks
16     dd.ModelConfig(
17         alias="critic-model",
18         model="nvidia/nemotron-3-nano-30b-a3b",
19         provider="nvidia",
20         inference_parameters=dd.ChatCompletionInferenceParams(
21             temperature=0.25,
22             top_p=0.95,
23             max_tokens=2048,
24         ),
25     ),
26     # Reasoning and structured tasks
27     dd.ModelConfig(
28         alias="reasoning-model",
29         model="nvidia/nemotron-3-super-120b-a12b",
30         provider="nvidia",
31         inference_parameters=dd.ChatCompletionInferenceParams(
32             temperature=1.0,
33             top_p=0.95,
34             max_tokens=4096,
35         ),
36     ),
37     # Vision tasks
38     dd.ModelConfig(
39         alias="vision-model",
40         model="nvidia/nemotron-nano-12b-v2-vl",
41         provider="nvidia",
42         inference_parameters=dd.ChatCompletionInferenceParams(
43             temperature=0.7,
44             top_p=0.95,
45             max_tokens=2048,
46         ),
47     ),
48     # Embedding tasks
49     dd.ModelConfig(
50         alias="embedding_model",
51         model="nvidia/llama-3.2-nv-embedqa-1b-v2",
52         provider="nvidia",
53         inference_parameters=dd.EmbeddingInferenceParams(
54             encoding_format="float",
55             extra_body={
56                 "input_type": "query"
57             }
58         )
59     )
60 ]

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:

1 import data_designer.config as dd
2 
3 model_config = dd.ModelConfig(
4     alias="my-model",
5     model="nvidia/nemotron-3-nano-30b-a3b",
6     provider="nvidia",
7     inference_parameters=dd.ChatCompletionInferenceParams(
8         temperature=0.85,
9         top_p=0.95,
10         max_tokens=2048,
11     ),
12     skip_health_check=True,  # Skip health check for this model
13 )

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.

Model Configurations

Model Configurations

Overview

ModelConfig Structure

Examples

Basic Model Configuration

Multiple Model Configurations for Different Tasks

Skipping Health Checks

See Also

Overview

ModelConfig Structure

Examples

Basic Model Configuration

Multiple Model Configurations for Different Tasks

Skipping Health Checks

See Also

1	import data_designer.config as dd
2
3	# Simple model configuration with fixed parameters
4	model_config = dd.ModelConfig(
5	alias="my-text-model",
6	model="nvidia/nemotron-3-nano-30b-a3b",
7	provider="nvidia",
8	inference_parameters=dd.ChatCompletionInferenceParams(
9	temperature=0.85,
10	top_p=0.95,
11	max_tokens=2048,
12	),
13	)

1	import data_designer.config as dd
2
3	model_configs = [
4	# Creative tasks
5	dd.ModelConfig(
6	alias="creative-model",
7	model="nvidia/nemotron-3-nano-30b-a3b",
8	provider="nvidia",
9	inference_parameters=dd.ChatCompletionInferenceParams(
10	temperature=0.9,
11	top_p=0.95,
12	max_tokens=2048,
13	),
14	),
15	# Critic tasks
16	dd.ModelConfig(
17	alias="critic-model",
18	model="nvidia/nemotron-3-nano-30b-a3b",
19	provider="nvidia",
20	inference_parameters=dd.ChatCompletionInferenceParams(
21	temperature=0.25,
22	top_p=0.95,
23	max_tokens=2048,
24	),
25	),
26	# Reasoning and structured tasks
27	dd.ModelConfig(
28	alias="reasoning-model",
29	model="nvidia/nemotron-3-super-120b-a12b",
30	provider="nvidia",
31	inference_parameters=dd.ChatCompletionInferenceParams(
32	temperature=1.0,
33	top_p=0.95,
34	max_tokens=4096,
35	),
36	),
37	# Vision tasks
38	dd.ModelConfig(
39	alias="vision-model",
40	model="nvidia/nemotron-nano-12b-v2-vl",
41	provider="nvidia",
42	inference_parameters=dd.ChatCompletionInferenceParams(
43	temperature=0.7,
44	top_p=0.95,
45	max_tokens=2048,
46	),
47	),
48	# Embedding tasks
49	dd.ModelConfig(
50	alias="embedding_model",
51	model="nvidia/llama-3.2-nv-embedqa-1b-v2",
52	provider="nvidia",
53	inference_parameters=dd.EmbeddingInferenceParams(
54	encoding_format="float",
55	extra_body={
56	"input_type": "query"
57	}
58	)
59	)
60	]

1	import data_designer.config as dd
2
3	model_config = dd.ModelConfig(
4	alias="my-model",
5	model="nvidia/nemotron-3-nano-30b-a3b",
6	provider="nvidia",
7	inference_parameters=dd.ChatCompletionInferenceParams(
8	temperature=0.85,
9	top_p=0.95,
10	max_tokens=2048,
11	),
12	skip_health_check=True, # Skip health check for this model
13	)