TinyLlama-15M
| Property | Value |
|---|---|
| Parameter Count | 15 Million |
| Model Type | Language Model |
| Architecture | Llama 2 |
| Training Dataset | TinyStories |
| Source | karpathy/tinyllamas |
| Model URL | https://huggingface.co/nickypro/tinyllama-15M |
What is tinyllama-15M?
TinyLlama-15M is a lightweight language model that implements the Llama 2 architecture with only 15 million parameters. It's specifically trained on the TinyStories dataset, making it an efficient model for simple text generation tasks. This model represents a significant optimization of the larger Llama 2 architecture, designed for scenarios where computational resources are limited.
Implementation Details
The model is built on the proven Llama 2 architecture but scaled down to just 15M parameters. It's been trained on TinyStories, a dataset specifically designed for simple narrative generation. The model is compatible with the llama2.c project, making it particularly suitable for embedded systems and lightweight applications.
- Compact 15M parameter implementation
- Based on Llama 2 architecture
- Optimized for efficiency and minimal resource usage
- Compatible with llama2.c project
Core Capabilities
- Simple text generation
- Lightweight deployment options
- Efficient processing on limited hardware
- Basic narrative generation
Frequently Asked Questions
Q: What makes this model unique?
TinyLlama-15M stands out for its extremely compact size while maintaining the core Llama 2 architecture. At just 15M parameters, it's one of the smallest implementations of the Llama architecture, making it ideal for resource-constrained environments.
Q: What are the recommended use cases?
This model is best suited for simple text generation tasks, particularly in environments with limited computational resources. It's ideal for embedded systems, educational purposes, and applications where a lightweight language model is required.
