Training Configuration

Experiment Configuration

ExperimentConfig

The main configuration class that orchestrates all aspects of model training.

DataConfig or List[DataConfig]

required

Data configuration(s) for potentially multi-objective modeling

OptimizationConfig

required

Optimization and training parameters

ModelConfig

required

Model architecture and initialization parameters

MetaConfig

default:"Uses MetaConfig defaults"

Metadata and run-specific parameters

MetaConfig

Configuration for experiment metadata and checkpointing behavior.

str

default:"trial-run"

Name identifier for this experimental run

int

default:"42"

Random seed for reproducible training

str

default:"current working directory / run_name"

Directory path for saving model checkpoints

int

default:"-1"

Frequency (in steps) for saving model checkpoints. Set to -1 to save only at the end of training.

int

default:"-1"

Maximum number of model checkpoints to retain. Set to -1 for no limit.

WandbConfig or None

default:"None"

Weights & Biases logging configuration for experiment tracking and visualization

WandbConfig

Configuration for Weights & Biases experiment tracking and logging.

str

required

Weights & Biases project name for organizing experiments

str or None

default:"None"

Weights & Biases team/organization name. If None, uses the default entity associated with your API key.

str or None

default:"None"

Custom run name for the experiment. If None, uses the MetaConfig name or auto-generates one.

List[str] or None

default:"None"

List of tags to associate with the run for easy filtering and organization

str or None

default:"None"

Optional notes or description for the experiment run

bool

default:"False"

Whether to log the model as a Weights & Biases artifact for version control

int

default:"100"

Frequency (in steps) for logging metrics to Weights & Biases

bool

default:"False"

Whether to log gradient histograms (can impact performance)

bool

default:"False"

Whether to log parameter histograms (can impact performance)

str or None

default:"None"

Model watching mode for logging gradients and parameters:

"gradients": Log gradient histograms
"parameters": Log parameter histograms
"all": Log both gradients and parameters
None: Disable model watching

dict or None

default:"None"

Additional configuration dictionary to log to Weights & Biases

DataConfig

Configuration for training data and objectives. Can be specified as a single instance or list for multi-task learning.

str or List[str]

required

Path(s) to preprocessed data files

str, List[str], callable, List[callable], or None

default:"None"

Feature engineering functions for lag tokens (historical lag features) and exogenous variables (external variables). Can be string identifier(s) or custom function(s).

float or None

default:"Equal weight among all data configs"

Relative sampling weight for this data source (normalized to sum to 1 across all data configs).

str or callable

default:"cross_entropy"

Loss function specification:

"cross_entropy": Chronos-style or text cross-entropy loss
"mse": Mean Squared Error (TimesFM-style)
"quantile" or "pinball": Quantile/Pinball loss (TiRex-style)
"multi_task": Multi-task learning (TimesFM 2.0-style)
Custom callable loss function

float

default:"0.1"

Portion of the dataset to use as validation data (0.0-1.0, where 1.0 means all data is validation).

At least one DataConfig must have validation_split < 1.0 for training to proceed.

OptimizationConfig

Configuration for training optimization parameters.

int

required

Total number of training steps for the experiment

float

required

Maximum learning rate value

int

required

Global batch size for training

str or callable

default:"constant"

Learning rate scheduling strategy:

String options: "constant", "linear", "cosine", "exponential"
Custom function with signature: (learning_rate, current_step, total_steps) → decayed_rate

Warmup is applied after this schedule and must be disabled separately if not needed

int

default:"0"

Number of learning rate warmup steps

int

default:"0"

Number of learning rate decay steps. Must be set to 0 when using custom learning rate schedules.

Constraint: warmup_steps + decay_steps ≤ total_training_steps

float or None

default:"max_learning_rate / 10"

Minimum learning rate value

str

default:"Adam"

Optimizer algorithm. Options: "Adam", "SGD", "Lion"

float

default:"0.01"

L2 regularization coefficient

float

default:"0.0"

Z-loss regularization coefficient. Set to 0.0 to disable.

float or None

default:"None"

Load balancing coefficient for Mixture of Experts (MoE) models. Only applicable for MoE architectures.

float

default:"1.0"

Gradient clipping threshold based on global L2 norm

Example Configurations

from pynolano import ExperimentConfig, DataConfig, ModelConfig, OptimizationConfig

def build() -> ExperimentConfig:
    return ExperimentConfig(
        data_configs=DataConfig(data_paths="./prepared_data"),
        model_config=ModelConfig("Qwen/Qwen3-4B"),
        optimization_config=OptimizationConfig(
            total_training_steps=1000,
            max_learning_rate=3e-4,
            global_batch_size=32
        )
    )

from pynolano import ExperimentConfig, DataConfig, ModelConfig, OptimizationConfig, MetaConfig, WandbConfig

def build() -> ExperimentConfig:
    return ExperimentConfig(
        data_configs=[
            DataConfig(
                data_paths="./text_data",
                training_objective="cross_entropy",
                sampling_weight=0.7
            ),
            DataConfig(
                data_paths="./code_data", 
                training_objective="cross_entropy",
                sampling_weight=0.3
            )
        ],
        model_config=ModelConfig(
            architecture="Qwen/Qwen3-4B",
            init_method="normal"
        ),
        optimization_config=OptimizationConfig(
            total_training_steps=10000,
            max_learning_rate=1e-4,
            global_batch_size=64,
            learning_rate_schedule="cosine",
            warmup_steps=1000,
            optimizer_type="Adam",
            weight_decay=0.01
        ),
        meta_config=MetaConfig(
            name="multi-task-experiment",
            seed=42,
            model_save_frequency=1000,
            max_checkpoints=5,
            wandb_config=WandbConfig(
                project="nolano-training",
                tags=["multi-task", "experiment"],
                log_model=True,
                log_frequency=50
            )
        )
    )

from pynolano import ExperimentConfig, DataConfig, ModelConfig, OptimizationConfig

def build() -> ExperimentConfig:
    return ExperimentConfig(
        data_configs=DataConfig(
            data_paths="./ts_prepared_data",
            training_objective="mse",
            validation_split=0.2
        ),
        model_config=ModelConfig("TimesFM-1B"),  # Coming soon
        optimization_config=OptimizationConfig(
            total_training_steps=5000,
            max_learning_rate=5e-4,
            global_batch_size=128,
            learning_rate_schedule="linear",
            warmup_steps=500
        )
    )

Additional Features

Hyperparameter Sweep

Hyperparameter sweep functionality is currently in development and will be available in a future release.

convert_to_hf()

The convert_to_hf() function converts trained Nolano.AI models to Hugging Face format for easy sharing and deployment.

pynolano.convert_to_hf(
    input_dir: str,
    config_file: str,
    output_dir: str,
    upload: bool = False
)

nolano convert_to_hf ./checkpoints/global_step_1000 config.yaml ./hf_model --upload

str

required

Path to the checkpoint directory (e.g., /path/to/checkpoint/global_step_XXXXX)

str

required

Path to the model configuration YAML file used during training

str

required

Destination directory for the converted Hugging Face model

bool

default:"False"

Whether to directly upload the converted model to Hugging Face Hub

Get Started

Core Concepts

Tutorials

Training Configuration

Experiment Configuration

ExperimentConfig

MetaConfig

WandbConfig

DataConfig

OptimizationConfig

Example Configurations

Additional Features

Hyperparameter Sweep

convert_to_hf()

​Experiment Configuration

​ExperimentConfig

​MetaConfig

​WandbConfig

​DataConfig

​OptimizationConfig

​Example Configurations

​Additional Features

​Hyperparameter Sweep

​convert_to_hf()

Experiment Configuration

ExperimentConfig

MetaConfig

WandbConfig

DataConfig

OptimizationConfig

Example Configurations

Additional Features

Hyperparameter Sweep

convert_to_hf()