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#!/usr/bin/env python3
"""
Example usage of SuperLinear model for time series forecasting.
"""
import torch
from transformers import AutoModelForCausalLM, AutoConfig
def main():
# Load model configuration and model
config = AutoConfig.from_pretrained("./", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("./", trust_remote_code=True)
# Set model to evaluation mode
model.eval()
# Create sample time series data
# Shape: [batch_size, sequence_length, features]
batch_size = 4
sequence_length = 512
num_features = 1
prediction_length = 96
# Generate synthetic time series data
t = torch.linspace(0, 10, sequence_length)
sample_data = torch.sin(t).unsqueeze(0).unsqueeze(-1).repeat(batch_size, 1, num_features)
print(f"Input shape: {sample_data.shape}")
# Generate predictions
with torch.no_grad():
outputs = model(inputs_embeds=sample_data, pred_len=prediction_length)
predictions = outputs.logits
print(f"Prediction shape: {predictions.shape}")
print(f"Sample predictions: {predictions[0, :5, 0]}") # First 5 predictions of first batch
# Demonstrate with different prediction lengths
for pred_len in [24, 48, 96, 192]:
with torch.no_grad():
outputs = model(inputs_embeds=sample_data, pred_len=pred_len)
predictions = outputs.logits
print(f"Prediction length {pred_len}: output shape {predictions.shape}")
if __name__ == "__main__":
main()
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