The content discusses the development of a smaller language model, ZEPHYR7B, that is aligned to user intent. The model is created through distilled direct preference optimization (dDPO) using preference data from AI Feedback (AIF) to improve intent alignment. The model sets a new state-of-the-art on chat benchmarks for 7B parameter models without requiring human annotation. The approach involves training the model on 16 A100s (80GB) and achieves performance comparable to larger models aligned with human feedback. The use of preference learning is crucial in achieving these results, with the model showing improvements in standard academic benchmarks and conversational capabilities. The research focuses on intent alignment for helpfulness and does not address safety considerations such as producing harmful outputs or illegal advice. The work highlights the importance of future research in addressing these safety concerns and the challenges in curating synthetic data for distillation. The code, models, data, and tutorials for the system are available at https://github.com/huggingface/alignment-handbook.

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Source link: https://hackernoon.com/zephyr-direct-distillation-of-lm-alignment-abstract-and-introduction