LLaMA2 for Multilingual Fine Tuning

LLaMA2 for Multilingual Fine Tuning

Explore multilingual fine-tuning capabilities of various language models in this informative video. Delve into the LLaMA 2 paper before diving into hands-on code demonstrations. Compare the performance of LLaMA 2, Bloom, GLM2-6B, and MT5 models for multilingual tasks. Discover the potential of the open-sourced RedPajama-INCITE 7B Base model as an alternative to LLaMA. Gain insights into the strengths and limitations of each model for multilingual applications through practical examples and analysis.