---
language: en
license: mit
tags:
- language-model
- pytorch
- rnn
- text-generation
datasets:
- aditya-6122/tinystories-custom-dataset-18542-v2-test
pipeline_tag: text-generation
---

# vanilla-rnn-gru-like

## Model Details

### Model Description
This is a custom language model trained on a dataset of short stories, designed for text generation tasks.

### Model Sources
- **Repository**: [GitHub Repository](https://github.com/your-repo)  # Replace with actual repo if available
- **Paper**: N/A

## Uses

### Direct Use
This model can be used for generating short stories and text completion tasks.

### Downstream Use
Fine-tune the model on specific domains for specialized text generation.

### Out-of-Scope Use
Not intended for production use without further validation.

## Training Details

### Training Data
The model was trained on the [aditya-6122/tinystories-custom-dataset-18542-v2-test](https://huggingface.co/datasets/aditya-6122/tinystories-custom-dataset-18542-v2-test) dataset.

### Training Procedure
- **Training Regime**: Standard language model training with cross-entropy loss
- **Epochs**: 5
- **Batch Size**: 128
- **Learning Rate**: 0.001
- **Optimizer**: Adam (assumed)
- **Hardware**: Apple Silicon MPS (if available) or CPU

### Tokenizer
The model uses the [aditya-6122/tinystories-tokenizer-vb-18542-byte_level_bpe-v3-test](https://huggingface.co/aditya-6122/tinystories-tokenizer-vb-18542-byte_level_bpe-v3-test) tokenizer.

### Model Architecture
- **Architecture Type**: RNN-based language model with GRU cells
- **Embedding Dimension**: 512
- **Hidden Dimension**: 1024
- **Vocabulary Size**: 18542
- **Architecture Diagram**: See `model_arch.jpg` for visual representation

## Files
- `model.bin`: The trained model weights in PyTorch format.
- `tokenizer.json`: The tokenizer configuration.
- `model_arch.jpg`: Architecture diagram showing the GRU model structure.

## How to Use

Since this is a custom model, you'll need to load it using the provided code:

```python
import torch
from your_language_model import LanguageModel  # Replace with actual import
from tokenizers import Tokenizer

# Load tokenizer
tokenizer = Tokenizer.from_file("tokenizer.json")

# Load model
vocab_size = tokenizer.get_vocab_size()
model = LanguageModel(vocab_size=vocab_size, embedding_dimension=512, hidden_dimension=1024)
model.load_state_dict(torch.load("model.bin"))
model.eval()

# Generate text
input_text = "Once upon a time"
# Tokenize and generate (implement your generation logic)
```

## Limitations
- This is a basic RNN model and may not perform as well as transformer-based models.
- Trained on limited data, may exhibit biases from the training dataset.
- Not optimized for production deployment.

## Ethical Considerations
Users should be aware of potential biases in generated text and use the model responsibly.

## Citation
If you use this model, please cite:
```
@misc{vanilla-rnn-gru-like},
  title={Tiny-Stories-GRU-LanguageModel-ByteLevelEncoding},
  author={Aditya Wath},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/aditya-6122/Tiny-Stories-GRU-LanguageModel-ByteLevelEncoding}
}
```