Large language models (LLMs) have been widely applied in various practical applications, typically comprising billions of parameters, with inference processes requiring substantial energy and computational resources. In contrast, the human brain, employing bio-plausible spiking mechanisms, can accomplish the same tasks while significantly reducing energy consumption, even with a similar number of parameters. Based on this, several pioneering researchers have proposed and implemented various large language models that leverage spiking neural networks. They have demonstrated the feasibility of these models, validated their performance, and open-sourced their frameworks and partial source code. To accelerate the adoption of brain-inspired large language models and facilitate secondary development for researchers, we are releasing a software toolkit named DarwinKit (Darkit). The toolkit is designed specifically for learners, researchers, and developers working on spiking large models, offering a suite of highly user-friendly features that greatly simplify the learning, deployment, and development processes.

翻译：大语言模型（LLMs）已被广泛应用于各类实际应用中，其通常包含数十亿参数，推理过程需要消耗大量能源与计算资源。相比之下，采用生物合理性脉冲机制的人脑，即使使用相似数量的参数，也能在显著降低能耗的同时完成相同任务。基于此，多位先驱研究者已提出并实现了多种利用脉冲神经网络的大语言模型。他们论证了此类模型的可行性，验证了其性能，并开源了相关框架及部分源代码。为加速类脑大语言模型的推广应用，并便利研究者的二次开发，我们发布了一款名为DarwinKit（简称Darkit）的软件工具包。该工具包专为从事脉冲大模型工作的学习者、研究者及开发者设计，提供了一系列高度用户友好的功能，可极大简化学习、部署与开发流程。