  We release and introduce the TigerBot family of large language models (LLMs),
consisting of base and chat models, sized from 7, 13, 70 and 180 billion
parameters. We develop our models embarking from Llama-2 and BLOOM, and push
the boundary further in data, training algorithm, infrastructure, and
application tools. Our models yield meaningful performance gain over SOTA
open-source models, e.g., Llama-2, specifically 6% gain in English and 20% gain
in Chinese. TigerBot model family also achieves leading performance in major
academic and industrial benchmarks and leaderboards. We believe that TigerBot
represents just a snapshot of lightning-fast progression in LLM open-source
community. Therefore, we are thrilled to give back by publicly releasing our
models and reporting our approach behind, with additional emphases on building
SOTA LLMs in a democratized way and making LLMs of use in real-world
applications.
