ChuXin: 1.6B Technical Report

Unlike the majority of works that only opensourced the model weights and architecture, we have made everything needed to train a model available, including the training data, the training process, and the evaluation code. Our goal is to empower and strengthen the open research community, fostering transparency and enabling a new wave of innovation in the field of language modeling. Furthermore, we extend the context length to 1M tokens through lightweight continual pretraining and demonstrate strong needlein-a-haystack retrieval performance. Countless models have been opensourced on AI communities like HuggingFace to facilitate their use by researchers (Bai et al., 2023; Singer et al., 2024; Zhang et al., 2024). These models can broadly be divided into two categories: 1) Open source model weights and data sources, which constitute the vast majority.