  Despite the vast repository of global medical knowledge predominantly being
in English, local languages are crucial for delivering tailored healthcare
services, particularly in areas with limited medical resources. To extend the
reach of medical AI advancements to a broader population, we aim to develop
medical LLMs across the six most widely spoken languages, encompassing a global
population of 6.1 billion. This effort culminates in the creation of the
ApolloCorpora multilingual medical dataset and the XMedBench benchmark. In the
multilingual medical benchmark, the released Apollo models, at various
relatively-small sizes (i.e., 0.5B, 1.8B, 2B, 6B, and 7B), achieve the best
performance among models of equivalent size. Especially, Apollo-7B is the
state-of-the-art multilingual medical LLMs up to 70B. Additionally, these lite
models could be used to improve the multi-lingual medical capabilities of
larger models without fine-tuning in a proxy-tuning fashion. We will
open-source training corpora, code, model weights and evaluation benchmark.
