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Large language models have gained immense importance in recent years and havedemonstrated outstanding results in solving various tasks. However, despitethese achievements, many questions remain unanswered in the context of largelanguage models. Besides the optimal use of the models for inference and thealignment of the results to the desired specifications, the transfer of modelsto other languages is still an underdeveloped area of research. The recentpublication of models such as Llama-2 and Zephyr has provided new insights intoarchitectural improvements and the use of human feedback. However, insightsinto adapting these techniques to other languages remain scarce. In this paper,we build on latest improvements and apply the Direct PreferenceOptimization(DPO) approach to the German language. The model is available athttps://huggingface.co/DRXD1000/Phoenix.

PHOENIX: Open-Source Language Adaption for Direct Preference Optimization