# Long text processing

Aria-Base-64K is one of the foundational models in the Aria series, designed for research purposes and further training. It emerged after the long text pre-training phase, trained on 33 billion tokens (21 billion multimodal and 12 billion language tokens, with 69% being long texts). It is suitable for further pre-training or fine-tuning on long video question answering datasets or long document question answering datasets, even in resource-constrained environments, through post-training with short instruction tuning datasets tailored for long text scenarios. The model can comprehend up to 250 high-resolution images or up to 500 medium-resolution images, maintaining robust foundational performance in both language and multimodal contexts.

AI21-Jamba-1.5-Mini is the latest generation of hybrid SSM-Transformer instruction-following foundational models developed by AI21 Labs. This model stands out in the market with its exceptional long text processing capabilities, speed, and quality, achieving up to 2.5 times faster inference compared to other leading models of similar size. Jamba 1.5 Mini and Jamba 1.5 Large are optimized for commercial use cases and functionalities, such as function calling, structured output (JSON), and foundational generation.

The Jamba 1.5 Open Model Family is the latest series of AI models launched by AI21, built on the SSM-Transformer architecture. It is capable of processing ultra-long texts with high speed and quality, outperforming other similar products on the market. These models are designed specifically for enterprise applications, taking into account resource efficiency, quality, speed, and the ability to solve critical tasks.

GLM-4-9B-Chat is the open-source version of the latest pre-trained model GLM-4 series released by Zhi谱 AI. It boasts advanced features such as multi-turn dialogue, web browsing, code execution, custom tool invocation, and long-text reasoning. It supports 26 languages including Japanese, Korean, and German, and has launched a model with support for a 1M context length.