对比主流大模型部署需求，助你选对模型不踩坑
Comparison of Large Language Models for Deployment Requirements
- 梳理开源大模型的发布年份、许可证和硬件要求
- 涵盖基础模型与垂直领域模型，支持持续更新
- 适合研究者和企业快速评估模型部署成本
大型语言模型（LLMs），如生成式预训练变换器（GPTs），正在革新人类文本生成，能够产出语境相关且语法正确的内容。尽管存在偏见和幻觉等挑战，这些人工智能模型在内容创作、翻译和代码生成等任务中表现出色。通过微调和新型架构（如混合专家模型，MoE）可缓解上述问题。过去两年间，大量开源的基础模型与微调模型相继问世，使研究人员和企业在选择合适模型时面临许可与硬件需求的复杂性。为应对快速演进的大模型生态并辅助模型选型，本文呈现一份包含基础模型与领域特定模型的对比列表，重点关注发布年份、许可证及硬件要求。该列表已发布于 GitLab，将持续更新。
原文摘要 · Abstract (English)
Large Language Models (LLMs), such as Generative Pre-trained Transformers (GPTs) are revolutionizing the generation of human-like text, producing contextually relevant and syntactically correct content. Despite challenges like biases and hallucinations, these Artificial Intelligence (AI) models excel in tasks, such as content creation, translation, and code generation. Fine-tuning and novel architectures, such as Mixture of Experts (MoE), address these issues. Over the past two years, numerous open-source foundational and fine-tuned models have been introduced, complicating the selection of the optimal LLM for researchers and companies regarding licensing and hardware requirements. To navigate the rapidly evolving LLM landscape and facilitate LLM selection, we present a comparative list of foundational and domain-specific models, focusing on features, such as release year, licensing, and hardware requirements. This list is published on GitLab and will be continuously updated.
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