Originally published at: https://developer.nvidia.com/blog/training-localized-multilingual-llms-with-nvidia-nemo-part-1/
In today’s globalized world, the ability of AI systems to understand and communicate in diverse languages is increasingly crucial. Large language models (LLMs) have revolutionized the field of natural language processing, enabling AI to generate human-like text, answer questions, and perform various language tasks. However, most mainstream LLMs are trained on data corpora that primarily…
In this post, we shared about training a localized LLM with additional language support from foundation LLM. We have mainly covered how to train a customized tokenizer (Part1) and how to integrate the customized tokenizer for continual pretraining in NeMo (Part 2).
This pipeline uses Thai dataset as an example. Please do try out and share your feedback on constructing localized multilinugal LLM using NeMo
Can this process be used for any other llm models? or are there any limitations?
You can repeat this process for any of the LLMs supported by NeMo; LLama, Mistral, Gemma, Mistral, and many more. See the full list here: Large Language Models — NVIDIA NeMo Framework User Guide latest documentation
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