GLM-130B vs GPT-4

GLM-130B, showcased at ICLR 2023, represents a groundbreaking open bilingual pre-trained model that stands out with its impressive 130 billion parameters. Developed for bidirectional dense modeling in both English and Chinese, the GLM-130B leverages the General Language Model (GLM) algorithm for pre-training and is optimized to run inference tasks on a single server setup whether it be the A100 (40G * 8) or the V100 (32G * 8). Furthermore, its compatibility with INT4 quantization means that the already modest hardware requirements can be reduced even further, allowing a server with 4 * RTX 3090 (24G) to support the model with minimal performance degradation.

As part of its training process, the GLM-130B has digested an extensive dataset consisting of over 400 billion text tokens, equally divided between Chinese and English. It boasts exceptional bilingual support, superior performance across various datasets when compared to its counterparts, and offers fast inference times. Additionally, this repository also promotes reproducibility by facilitating open-source code and model checkpoints for over 30 tasks.

GPT-4 is the latest milestone in OpenAI’s effort in scaling up deep learning.

GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks. For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT-3.5’s score was around the bottom 10%. We’ve spent 6 months iteratively aligning GPT-4 using lessons from our adversarial testing program as well as ChatGPT, resulting in our best-ever results (though far from perfect) on factuality, steerability, and refusing to go outside of guardrails.

GPT-4 is more creative and collaborative than ever before. It can generate, edit, and iterate with users on creative and technical writing tasks, such as composing songs, writing screenplays, or learning a user’s writing style.