Technical Specifications

System Requirements

VRAM requirements for different quantization methods and context sizes

GLM-4-9B-Chat-1M

GLM-4-9B-Chat-1M is a conversational large language model developed by Z.ai as part of the GLM-4 family. This particular model variant is specifically engineered to process and generate content within an exceptionally long context window, supporting up to 1,048,576 tokens. Its primary purpose is to facilitate advanced human-machine interactions, enabling applications that require a deep understanding of extensive textual information, such as multi-round conversations, comprehensive document analysis, and detailed query responses. The model also incorporates capabilities for web browsing, code execution, and custom tool invocation, extending its utility beyond conventional text generation.

From a technical perspective, GLM-4-9B-Chat-1M is built upon a transformer architecture, characterizing it as a dense model. This architecture processes information through multiple layers with a uniform activation of parameters, distinguishing it from sparse Mixture-of-Experts (MoE) designs. A notable innovation within this model is the utilization of Rotary Position Embedding (RoPE) in conjunction with the YaRN (Yet another RoPE N) scaling method. This combination is crucial for the model's ability to effectively extrapolate to and manage its extensive 1 million token context length, addressing challenges associated with processing long sequences in large language models. While specific internal dimensions such as hidden size, number of layers, or attention head configurations are not explicitly detailed in public documentation, its foundation in the transformer paradigm implies the use of multi-head attention mechanisms to capture diverse linguistic patterns.

The operational characteristics of GLM-4-9B-Chat-1M are defined by its capacity for handling substantial input lengths and its integrated functionalities. This enables its application in scenarios demanding significant contextual memory and complex reasoning over large datasets. Practical use cases include sophisticated chatbot systems, automated long-form content summarization, support for software development through code generation and execution, and data extraction from extensive documents. The model's open-source release, accompanied by publicly available weights, promotes its adoption within the research community and allows for integration into various commercial applications, particularly where computational resources might be a consideration for models of this scale.

No evaluation benchmarks for GLM-4-9B-Chat-1M available.