  Large Language Models(LLMs) have shown exceptional abilities, yet training
these models can be quite challenging. There is a strong dependence on the
quality of data and finding the best instruction tuning set. Further, the
inherent limitations in training methods create substantial difficulties to
train relatively smaller models with 7B and 13B parameters. In our research, we
suggest an improved training method for these models by utilising knowledge
from larger models, such as a mixture of experts (8x7B) architectures. The
scale of these larger models allows them to capture a wide range of variations
from data alone, making them effective teachers for smaller models. Moreover,
we implement a novel post-training domain alignment phase that employs
domain-specific expert models to boost domain-specific knowledge during
training while preserving the model's ability to generalise. Fine-tuning
Mistral 7B and 2x7B with our method surpasses the performance of
state-of-the-art language models with more than 7B and 13B parameters:
achieving up to $7.9$ in MT-Bench and $93.04\%$ on AlpacaEval.
