Technical Specifications

GLM-4-9B-Chat

The GLM-4-9B-Chat model is a conversational large language model developed by the Knowledge Engineering Group (KEG) at Tsinghua University in collaboration with Z.ai. As a core component of the fourth-generation General Language Model (GLM) series, this variant is specifically optimized for human-preference alignment and complex multi-turn dialogue. The model is trained on a massive corpus of 10 trillion tokens and supports multilingual communication across 26 languages, making it a highly versatile tool for global conversational applications.

Architecturally, GLM-4-9B-Chat is built on a dense transformer framework utilizing 40 layers with a hidden dimension of 4096. A significant technical innovation in this variant is the implementation of Grouped Query Attention (GQA), which employs two key-value heads to optimize memory bandwidth and inference throughput without sacrificing modeling quality. The architecture further incorporates Rotary Position Embeddings (RoPE) for improved length extrapolation and utilizes SwiGLU activation functions in its feed-forward networks, replacing traditional ReLU to enhance the model's non-linear representative capacity. Normalized using RMSNorm, the model maintains stable training dynamics across its parameter space.

GLM-4-9B-Chat is engineered to handle extended context windows up to 128,000 tokens, enabling it to maintain coherence over long documents and extensive conversational histories. Beyond standard text generation, the model integrates sophisticated tool-use capabilities, including autonomous web browsing, Python code execution, and custom function calling. These features allow the model to interact with external environments to solve mathematical problems and perform real-time information retrieval, making it suitable for deployment in advanced AI assistants and automated agentic systems.

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