StableLM is a new open-source language model designed for natural language processing tasks.
The model is unique in that it allows users to train and fine-tune it on their own specific datasets, thus increasing performance on task-specific language learning.
Its architecture is based on BERT, and it is designed to minimize catastrophic forgetting.
The model is pre-trained on a large corpus of text, including Wikipedia and Common Crawl.
The software is easy to use and can be accessed on GitHub, with documentation available to help users get started.
StableLM has already been used in various applications, including text classification and sentiment analysis.