# Large-scale Parameters

A pretrained language model based on the PyTorch framework released by Meta, suitable for non-commercial research purposes.

NLLB-200-3.3B is a multilingual neural machine translation model developed by Meta (formerly Facebook), supporting translation tasks between 200 languages.

GNER-T5-xxl is a generative named entity recognition model based on the Flan-T5 architecture, with 11B parameters, demonstrating excellent performance in zero-shot recognition tasks.

A large-scale pre-trained model with 13 billion parameters based on the LLaMa architecture, optimized for Chinese tokenization, completing 110 billion tokens of incremental pre-training in Chinese and English, significantly improving Chinese generation and comprehension capabilities

T5-Efficient-LARGE is a variant based on Google's T5, optimized for downstream task performance with a deep narrow architecture, featuring 737.7 million parameters.

T5 Efficient Large-NH32 is a deep-narrow variant of Google's T5 model, focusing on improving downstream task performance by increasing model depth.