AI Language Model Training Simulator

1 Data Preprocessing

Configure your model and click "Start Training" to begin the data preprocessing step.

2 Tokenization

Complete the data preprocessing step to proceed to tokenization.

3 Model Architecture

Complete the tokenization step to proceed to model architecture.

4 Training Process

Complete the model architecture step to proceed to training.

5 Text Generation

Complete the training process to proceed to text generation.

6 Hyperparameter Tuning

Complete the text generation step to proceed to hyperparameter tuning.

Experiment with different hyperparameters to see their impact on model performance:

Learning Rate
Batch Size
Model Layers
Optimizers

Compare the effect of different learning rates on training loss:

Compare the effect of different batch sizes on training efficiency and convergence:

Compare the effect of different model depths on performance:

Compare the performance of different optimizers:

Parameter Impact Analysis

Model Size

Larger models can capture more complex patterns but require more computational resources and training data.

Learning Rate

Higher rates can speed up training but risk overshooting optimal weights. Lower rates provide stability but slower convergence.

Batch Size

Larger batches provide more stable gradient estimates but may lead to poorer generalization. Smaller batches introduce more noise but can help escape local minima.

Context Length

Longer contexts allow the model to capture more long-range dependencies but increase memory requirements and training time.

AI Language Model Training Simulator

Sample Raw Text:

Sample Tokenized Text:

Token Visualization:

Model Summary:

Parameter Distribution:

Parameter Impact Analysis