趋近智
参数
9B
上下文长度
1,000K
模态
Text
架构
Dense
许可证
MIT License
发布日期
30 Jun 2024
知识截止
-
注意力结构
Multi-Head Attention
隐藏维度大小
-
层数
-
注意力头
-
键值头
-
激活函数
-
归一化
-
位置嵌入
Absolute Position Embedding
不同量化方法和上下文大小的显存要求
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.
General Language Models from Z.ai
排名适用于本地LLM。
没有可用的 GLM-4-9B-Chat-1M 评估基准。