Fengshen List (Fengshen List Large Model)

The "Fengshen List" is a long-term open source project jointly maintained by a team of engineers, researchers, and interns from the Cognitive Computing and Natural Language Center of the International Digital Economy Academy (IDEA) in the Greater Bay Area of Guangdong, Hong Kong, and Macau. The "Fengshen List" open-source system will re-examine the entire Chinese pre training big model open-source community, comprehensively promote the development of the entire Chinese big model community, and aim to become the infrastructure for Chinese cognitive intelligence.

Ziya's Universal Large Model V1 is a large-scale pre trained model based on LLaMa with 13 billion parameters, capable of translation, programming, text classification, information extraction, summarization, copy generation, common sense Q&A, and mathematical calculations. At present, the Jiang Ziya general large model has completed a three-stage training process including large-scale pre training (PT), multi task supervised fine-tuning (SFT), and human feedback learning (RLHF).

Ziya's universal big model can assist human-machine collaboration in multiple application scenarios such as digital humans, copywriting, chatbots, business assistants, Q&A, and code generation, improving work and production efficiency.

The "Fengshen List" is the largest open-source pre training model system in Chinese, with over 98 open-source pre training models currently available. At present, the first Chinese Stable Diffusion and CLIP model have been open sourced. Models such as Erlangshen UniMC have won multiple championships on lists such as FewCLUE/ZeroCLUE.

Accumulate data and computing power into pre trained models with cognitive abilities, with the goal of becoming a solid foundation for massive downstream tasks and various algorithm innovation research.

The GTS model production platform focuses on the field of natural language processing, serving numerous business scenarios such as intelligent customer service, data semantic analysis, recommendation systems, etc. It supports tasks such as e-commerce comment sentiment analysis, scientific literature subject classification, news classification, content review, etc

Under the GTS training system, only a small number of training samples need to be input, and there is no need for AI model training related knowledge to obtain a lightweight small model that can be directly deployed.