#Llama model

TinyLlama focuses on efficiently pretraining a 1.1 billion parameter language model across 3 trillion tokens in 90 days, sharing architectural similarities with Llama 2. Its compact design allows deployment on edge devices, supporting real-time tasks without internet dependency. As an adaptable solution for open-source projects, it offers essential updates and evaluation metrics, serving as a valuable resource for those interested in language models under 5 billion parameters. The project supports advanced distributed training capabilities alongside optimizations for increased processing efficiency.