Technical Specifications

System Requirements

VRAM requirements for different quantization methods and context sizes

GLM-4-9B

The GLM-4-9B model, developed by THUDM (Tsinghua University Department of Computer Science and Technology) and Z.ai, represents an open-source iteration within the GLM-4 series of pre-trained language models. This model is engineered for general language tasks, exhibiting capabilities in semantic understanding, mathematical reasoning, code execution, and knowledge retrieval. It is designed to handle multilingual inputs and outputs, supporting 26 languages including Chinese, English, Japanese, Korean, and German. The GLM-4-9B also supports advanced functionalities such as web browsing, code execution, and custom tool calling through a Function Call mechanism.

Architecturally, GLM-4-9B employs a transformer architecture, which is a common deep learning structure for natural language processing. The model incorporates an autoregressive blank infilling approach for its pre-training phase. The architecture includes specific design choices such as the removal of bias terms except for those in the Query, Key, and Value (QKV) components of attention layers, which contributes to improved length extrapolation. It also utilizes RMSNorm for normalization. The base version of GLM-4-9B supports a context length of up to 128,000 tokens, with specialized variants offering an extended context length of up to 1 million tokens.

GLM-4-9B is engineered for a range of applications, including conversational AI assistants, content generation, and question answering systems. Its design facilitates integration with the Hugging Face Transformers library, simplifying deployment and adoption for developers. The model aims to provide a balance between efficiency and effectiveness, making it suitable for scenarios with resource constraints while maintaining performance across diverse tasks.

No evaluation benchmarks for GLM-4-9B available.