title: Xmodel-1.5: An 1B-scale Multilingual LLM
summary: We introduce Xmodel-1.5, a novel 1-billion-parameter multilingual large model
  pretrained on approximately 2 trillion tokens. The model demonstrates strong
  performance across several languages, with particularly notable results in
  Thai, Arabic, and French, alongside its effectiveness in Chinese and English.
  In addition, we contribute to the research community by releasing a Thai
  evaluation dataset, which includes hundreds of questions annotated by students
  from Chulalongkorn University's School of Integrated Innovation. While the
  results are promising, we acknowledge that there is still room for improvement.
  We hope this work advances ongoing efforts in multilingual AI research and
  promotes better cross-linguistic understanding in various natural language
  processing tasks. Our models and code are publicly available on GitHub at
  https://github.com/XiaoduoAILab/XmodelLM.
paper_authors._id: 673b59cad55948359af213ef
paper_authors.name: Wang Qun
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-30T19:37:39.593Z
paper_authors._id: 647adfd0e3a7d24c8e46d7d1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/647adfd0e3a7d24c8e46d7d1/UqZegkj3P2XyPe4Lgnrt7.jpeg
paper_authors.fullname: ValeriaWong
paper_authors.type: user
paper_authors.user: valeriaWong
paper_authors._id: 673b59cad55948359af213f0
paper_authors.name: Liu Yang
paper_authors._id: 673b59cad55948359af213f1
paper_authors.name: Lin Qingquan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-18T16:19:06.486Z
paper_authors._id: 66d53492ad293ffc4b17d99b
paper_authors.avatarUrl: /avatars/1f89566c6652637731fbc7c1d69314cb.svg
paper_authors.fullname: qq
paper_authors.type: user
paper_authors.user: linqingquan
paper_authors._id: 673b59cad55948359af213f2
paper_authors.name: Jiang Ling
paper_title: Xmodel-1.5: An 1B-scale Multilingual LLM
paper_summary: We introduce Xmodel-1.5, a novel 1-billion-parameter multilingual large model
  pretrained on approximately 2 trillion tokens. The model demonstrates strong
  performance across several languages, with particularly notable results in
  Thai, Arabic, and French, alongside its effectiveness in Chinese and English.
  In addition, we contribute to the research community by releasing a Thai
  evaluation dataset, which includes hundreds of questions annotated by students
  from Chulalongkorn University's School of Integrated Innovation. While the
  results are promising, we acknowledge that there is still room for improvement.
  We hope this work advances ongoing efforts in multilingual AI research and
  promotes better cross-linguistic understanding in various natural language
  processing tasks. Our models and code are publicly available on GitHub at
  https://github.com/XiaoduoAILab/XmodelLM.
paper_ai_keywords: multilingual model 1-billion-parameter model pretrained maximum likelihood training Thai Arabic French Chinese English dataset calibration Thai scheme dataset cross-linguistic understanding natural language processing tasks