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The rapid development of open-source large language models (LLMs) has beentruly remarkable. However, the scaling law described in previous literaturepresents varying conclusions, which casts a dark cloud over scaling LLMs. Wedelve into the study of scaling laws and present our distinctive findings thatfacilitate scaling of large scale models in two commonly used open-sourceconfigurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeekLLM, a project dedicated to advancing open-source language models with along-term perspective. To support the pre-training phase, we have developed adataset that currently consists of 2 trillion tokens and is continuouslyexpanding. We further conduct supervised fine-tuning (SFT) and DirectPreference Optimization (DPO) on DeepSeek LLM Base models, resulting in thecreation of DeepSeek Chat models. Our evaluation results demonstrate thatDeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly inthe domains of code, mathematics, and reasoning. Furthermore, open-endedevaluations reveal that DeepSeek LLM 67B Chat exhibits superior performancecompared to GPT-3.5.

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism