title: TigerBot: An Open Multilingual Multitask LLM
summary: 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.
paper_authors._id: 657bc9b8b8d76880f898598c
paper_authors.name: Ye Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-15T12:58:03.698Z
paper_authors._id: 607838a98794cdeedcb7b04d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1619266745847-607838a98794cdeedcb7b04d.jpeg
paper_authors.fullname: Ye Chen
paper_authors.type: user
paper_authors.user: yechen
paper_authors._id: 657bc9b8b8d76880f898598d
paper_authors.name: Wei Cai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-15T12:58:18.237Z
paper_authors._id: 60ebf7cae8854295f12c8e67
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1638950898765-60ebf7cae8854295f12c8e67.jpeg
paper_authors.fullname: WEI CAI
paper_authors.type: user
paper_authors.user: vivicai
paper_authors._id: 657bc9b8b8d76880f898598e
paper_authors.name: Liangmin Wu
paper_authors._id: 657bc9b8b8d76880f898598f
paper_authors.name: Xiaowei Li
paper_authors._id: 657bc9b8b8d76880f8985990
paper_authors.name: Zhanxuan Xin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-15T12:59:14.158Z
paper_authors._id: 60db36c431e4d396140ebcfc
paper_authors.avatarUrl: /avatars/503b2ca33aabf8ffe3e45a0a52d7f97e.svg
paper_authors.fullname: xinzhanxuan
paper_authors.type: user
paper_authors.user: xuangege
paper_authors._id: 657bc9b8b8d76880f8985991
paper_authors.name: Cong Fu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-15T12:59:22.277Z
paper_authors._id: 64466231e0dbfe6c21a91711
paper_authors.avatarUrl: /avatars/96486ff18d371ec5d904ab96512a9750.svg
paper_authors.fullname: cong fu
paper_authors.type: user
paper_authors.user: HetairoiF
paper_title: TigerBot: An Open Multilingual Multitask LLM
paper_summary: 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.
paper_ai_summary: TigerBot, a family of large language models, achieves superior performance over existing open-source models and benchmarks by advancing data, training algorithms, infrastructure, and application tools.
paper_ai_keywords: large language models LMMs Llama-2 BLOOM performance gain English Chinese academic benchmarks industrial leaderboards democratized way real-world applications