Technical Specifications

System Requirements

VRAM requirements for different quantization methods and context sizes

GLM-130B

GLM-130B is a bidirectional dense model featuring 130 billion parameters, developed for both English and Chinese language processing. This model is pre-trained using the General Language Model (GLM) algorithm, which employs an autoregressive blank infilling objective. This pre-training approach involves masking random continuous spans of text and subsequently predicting these masked segments autoregressively. This methodology contributes to its performance in various natural language processing tasks, including text comprehension, generation, and translation.

The architectural design of GLM-130B incorporates specific innovations to enhance training stability and inference efficiency for a model of its scale. It utilizes Rotary Positional Encoding (RoPE) for positional embeddings and integrates the Gated Linear Unit (GLU) with the Gaussian Error Linear Unit (GeLU) activation function within its Feed-Forward Networks (FFNs). The model also employs DeepNorm for layer normalization, a Post-Layer Normalization (Post-LN) technique, which has been shown to stabilize the training of large language models.

GLM-130B supports fast inference, making it suitable for real-time large-scale language processing tasks. It is designed to enable inference on a single A100 (40G * 8) or V100 (32G * 8) server. Further optimizations, such as INT4 quantization, allow for efficient inference on more accessible hardware, including a single server equipped with 4 RTX 3090 (24G) GPUs with minimal performance degradation. The model has been trained on over 400 billion text tokens, with an equal distribution of English and Chinese data.

No evaluation benchmarks for GLM-130B available.