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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) |