#TinyLlama

This library facilitates integration with Hugging Face models and optimizes deployment using quantization algorithms. Notable features include support for safetensors-based formats and compatibility with large models through accelerate. It offers a variety of quantization options like W8A8, Mixed Precision, and SparseGPT. Algorithms such as SmoothQuant and GPTQ are readily applicable for activating and weighting. Discover comprehensive examples and user guides for rapid model deployment and execution using llmcompressor with vllm, promoting swift inference and model efficiency.

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.

This project enhances the Llama2 model inference using Mojo's SIMD and vectorization, offering a 250x speed increase in Python performance. It exceeds llama2.c by 30% and llama.cpp by 20% in multithreaded CPU tasks. Supported models are Stories (260K to 110M) and Tinyllama-1.1B-Chat-v0.2. Benchmarks on Apple M1 Max demonstrate its proficiency. Suitable for developers exploring efficient transformer models in Mojo.