Tsinghua University’s Knowledge Engineering Group (KEG) has introduced GLM-4 9B, an innovative, open-source language model that surpasses other models like GPT-4 and Gemini in different benchmark tests. Developed by the Tsinghua Deep Model (THUDM) team, GLM-4 9B signals an important development in the sphere of natural language processing.
At its core, GLM-4 9B is a colossal language model trained on an unparalleled 10 trillion tokens spanning 26 languages. This model supports several capabilities, including Chinese and English multi-round dialogue, code execution, web navigation, and customised tool calling through function call. The model’s architecture is rooted in the newest improvements in deep learning, incorporating advanced techniques like attention mechanisms and transformer structures. The model’s architecture supports a context window of up to 128,000 tokens, while a specific variant allows for a remarkable one million token context length.
In comparison to major players like GPT and Gemini, GLM-4 9B’s structure stands out with its capacity for high-resolution vision tasks (up to 1198 x 1198 pixels) and its ability to handle a diverse range of languages. This versatility places GLM-4 9B as a compelling contender in the language model scene.
Assessments on various datasets have shown that GLM-4 9B performs better in many areas and matches the best models’ performance for some tasks. Particularly, it has outperformed GPT-4, Gemini Pro (in vision tasks), Mistral, and Llama 3 8B, thus strengthening its position as a powerful entity in the field.
Due to its open-source nature and allowable commercial use (under specific conditions), GLM-4 9B offers a plethora of opportunities for developers, scientists, and businesses alike. Possible applications range from natural language processing tasks to computer vision, code generation, and more. The model’s seamless integration with the Transformers library further eases its adoption and deployment.
The launch of GLM-4 9B by Tsinghua University’s KEG is an important landmark in language models. With its impressive performance, multilingual capabilities, and flexible architecture, this model sets a new standard for open-source language models and opens up possibilities for further advancements in natural language processing and artificial intelligence. All credit goes to the researchers of the project for their groundbreaking work. Don’t forget to follow us on Twitter, join our Telegram Channel, Discord Channel, and LinkedIn Group.
