Setting aside general-purpose LLMs, there exist a handful of models geared towards translation between hundred of language pairs: Meta's NLLB-200 [0] and M2M-10

Setting aside general-purpose LLMs, there exist a handful of models geared towards translation between hundred of language pairs: Meta's NLLB-200 [0] and M2M-100 [1] can be run using HuggingFace's transformers (plus numpy and sentencepieces), while Google's MADLAD-400 [2], in GGUF format [3], is also supported by llama.cpp. You could also look into Argos Translate, or just use the same models as Firefox through kotki [4]. [0] https://huggingface.co/facebook/nllb-200-distilled-600M https://huggingface.co/facebook/nllb-200-distilled-600M [1] https://huggingface.co/facebook/m2m100_418M https://huggingface.co/facebook/m2m100_418M [2] https://huggingface.co/google/madlad400-3b-mt https://huggingface.co/google/madlad400-3b-mt [3] https://huggingface.co/models?other=base_model:quantized:google/madlad400-3b-mt https://huggingface.co/models?other=base_model:quantized:goo... [4] https://github.com/kroketio/kotki https://github.com/kroketio/kotki