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README.md

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+---
+language: en
+license: mit
+tags:
+- language-model
+- pytorch
+- rnn
+- text-generation
+datasets:
+- aditya-6122/tinystories-custom-dataset-17783-v1-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-17783-v1-test](https://huggingface.co/datasets/aditya-6122/tinystories-custom-dataset-17783-v1-test) dataset.
+
+### Training Procedure
+- **Training Regime**: Standard language model training with cross-entropy loss
+- **Epochs**: 1
+- **Batch Size**: 2
+- **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-17783-char_bpe-v1-test](https://huggingface.co/aditya-6122/tinystories-tokenizer-vb-17783-char_bpe-v1-test) tokenizer.
+
+### Model Architecture
+- **Architecture Type**: RNN-based language model with GRU cells
+- **Embedding Dimension**: 512
+- **Hidden Dimension**: 1024
+- **Vocabulary Size**: 17783
+- **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={vanilla-rnn-gru-like},
+  author={Your Name},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/aditya-6122/vanilla-rnn-gru-like}
+}
+```