Technical Specifications

GLM-4-9B-Chat-1M

GLM-4-9B-Chat-1M is a specialized large language model within the GLM-4 family, developed by Zhipu AI to address the complexities of ultra-long sequence processing. This model variant is distinguished by its massive context window of 1,048,576 tokens, allowing it to ingest and reason over entire libraries of technical documentation, legal contracts, or multi-hour conversation transcripts. As a chat-optimized model, it is fine-tuned to follow complex instructions and engage in nuanced human-machine interactions while supporting integrated tool use such as web browsing and code execution.

Technically, the model utilizes a dense transformer architecture featuring 40 layers and a hidden dimensionality of 4096. To achieve its million-token context capacity, it employs an advanced positional encoding scheme combining Rotary Position Embeddings (RoPE) with the YaRN (Yet another RoPE N) scaling method. This configuration enables the model to maintain high retrieval accuracy across its entire context window, a capability often verified through needle-in-a-haystack evaluations. The architecture further incorporates RMSNorm for stable layer normalization and a Gated Linear Unit (GLU) with SwiGLU activation to optimize the feed-forward network's expressive power.

Operational flexibility is a core attribute of the GLM-4-9B-Chat-1M, as it is released with open weights under the Apache 2.0 license for the accompanying code and a permissive community license for the weights. It is designed to be compatible with the Hugging Face Transformers library and vLLM, facilitating deployment in diverse environments ranging from local research workstations to production inference servers. The model's multilingual capabilities extend to 26 languages, making it a versatile asset for global applications requiring deep semantic understanding and long-form document synthesis.

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