SOLAR 10.7B: Scaling Large Language Models With Simple Yet Effective Depth Up-scaling
Kim Dahyun, Park Chanjun, Kim Sanghoon, Lee Wonsung, Song Wonho, Kim Yunsu, Kim Hyeonwoo, Kim Yungi, Lee Hyeonju, Kim Jihoo, Ahn Changbae, Yang Seonghoon, Lee Sukyung, Park Hyunbyung, Gim Gyoungjin, Cha Mikyoung, Lee Hwalsuk, Kim Sunghun. Arxiv 2023
[Paper]
We introduce SOLAR 10.7B, a large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks. Inspired by recent efforts to efficiently up-scale LLMs, we present a method for scaling LLMs called depth up-scaling (DUS), which encompasses depthwise scaling and continued pretraining. In contrast to other LLM up-scaling methods that use mixture-of-experts, DUS does not require complex changes to train and inference efficiently. We show experimentally that DUS is simple yet effective in scaling up high-performance LLMs from small ones. Building on the DUS model, we additionally present SOLAR 10.7B-Instruct, a variant fine-tuned for instruction-following capabilities, surpassing Mixtral-8x7B-Instruct. SOLAR 10.7B is publicly available under the Apache 2.0 license, promoting broad access and application in the LLM field.
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