---
language:
- en
license: apache-2.0
tags:
- time-series
- sequence-modeling
- lstm
- pytorch
- pytorch-lightning
- text-generation
pipeline_tag: text-generation
metrics:
- loss
- perplexity
---

# 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](https://github.com/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](https://github.com/Adhithpasu)

---

## 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

| Metric     | Value |
|------------|-------|
| Train Loss | TBD   |
| Val Loss   | TBD   |
| Perplexity | TBD   |

*(Fill in with your actual results)*

---

## How to Use

```python
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

```bibtex
@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](https://github.com/Adhithpasu)