Adhithpasu/LSTMPytorchandLightning

LSTM with PyTorch & Lightning

Model Summary

A Long Short-Term Memory (LSTM) network implemented in PyTorch and trained using PyTorch Lightning for clean, scalable training loops. This model demonstrates sequence modeling — applicable to time series forecasting, text generation, or sequential pattern learning depending on the dataset used.

Model Details

• —Developed by: Chandrasekar Adhithya Pasumarthi (@Adhithpasu)
• —Affiliation: Frisco ISD, TX | AI Club Leader | Class of 2027
• —Model type: LSTM (Recurrent Neural Network)
• —Framework: PyTorch + PyTorch Lightning
• —License: Apache 2.0
• —Related work: Part of a broader ML portfolio spanning CNNs, regression, and NLP — see @Adhithpasu on GitHub

Intended Uses

Direct use:

• —Sequential data modeling (time series, text, sensor data)
• —Educational demonstration of LSTM architecture and PyTorch Lightning training patterns
• —Baseline recurrent model for comparison against Transformers and attention-based architectures

Out-of-scope use:

• —Production deployment without fine-tuning on domain-specific data
• —Long-context tasks where Transformer architectures are more suitable

Training Data

(Update with your specific dataset — e.g., a time series dataset, text corpus, or other sequential data)

Evaluation

(Fill in with your actual results)

How to Use

import torch
import pytorch_lightning as pl

# Load the model checkpoint
model = LSTMModel.load_from_checkpoint("lstm_model.ckpt")
model.eval()

# Example inference — replace with your actual input tensor
# Shape: (batch_size, seq_len, input_size)
sample_input = torch.randn(1, 50, 1)

with torch.no_grad():
    output = model(sample_input)
    print(f"Output shape: {output.shape}")

Model Architecture

Input (seq_len, input_size)
→ LSTM(hidden_size=128, num_layers=2, dropout=0.2)
→ Linear(128, output_size)

(Update to match your actual architecture)

Why PyTorch Lightning?

PyTorch Lightning removes boilerplate from training loops — separating research code (model definition) from engineering code (training, logging, checkpointing). This makes the code more readable, reproducible, and scalable to multi-GPU setups without changes to the model itself.

Limitations & Bias

• —LSTMs struggle with very long sequences compared to Transformer-based models
• —Performance is highly dependent on sequence length, hidden size, and the nature of the input data
• —May require significant hyperparameter tuning for new domains

Citation

@misc{pasumarthi2026lstm,
  author    = {Chandrasekar Adhithya Pasumarthi},
  title     = {LSTM with PyTorch and Lightning},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/Chandrasekar123/LSTMPytorchandLightning}
}

Contact

• —GitHub: @Adhithpasu