SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling

Understand

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.