TinyStories-33M
| Property | Value |
|---|---|
| License | MIT |
| Architecture | GPT-Neo |
| Paper | Research Paper |
| Training Dataset | TinyStories |
What is TinyStories-33M?
TinyStories-33M is a specialized language model based on the GPT-Neo architecture, specifically designed for generating simple, coherent stories. Developed by roneneldan, this model has been trained on the TinyStories dataset with carefully tuned hyperparameters to optimize its narrative generation capabilities.
Implementation Details
The model implements a transformer-based architecture with specific training configurations including a learning rate of 5e-4, constant learning rate schedule, and weight decay of 0.1. It utilizes a context length of 512 tokens and was trained with a batch size of 80 with 16 gradient accumulation steps.
- Optimized with Adam optimizer (β1=0.9, β2=0.95)
- Supports PyTorch framework
- Compatible with Hugging Face's transformers library
- Includes inference endpoints support
Core Capabilities
- Generation of simple, coherent narratives
- Efficient text completion with customizable parameters
- Support for beam search generation
- Integration with standard NLP pipelines
Frequently Asked Questions
Q: What makes this model unique?
TinyStories-33M stands out for its specialized training on simple narratives, making it particularly efficient for generating straightforward, coherent stories while maintaining a relatively small parameter count of 33M.
Q: What are the recommended use cases?
The model is ideal for generating simple stories, educational content creation, and applications requiring straightforward narrative generation. It's particularly suitable for scenarios where computational resources are limited but narrative coherence is essential.
