glm-4v

GLM-4V Model Introduction

Key Capabilities and Primary Use Cases

• Multimodal Conversations: Engages in text and image-based conversations.
• Image Understanding: Analyzes and describes images, including high-resolution images up to 1120x1120 pixels.
• Text Generation: Generates human-like text for tasks like chatbots, language translation, and text summarization.
• Use Cases: Intelligent assistants, multimodal content generation, multilingual language understanding, and customer service[1][2][4].

Most Important Features and Improvements

• Multilingual Support: Strong performance in both English and Chinese.
• Visual Understanding: Excels in image description, visual question answering, and optical character recognition.
• All Tools Feature: Autonomously uses web browsers, Python interpreters, and text-to-image models to complete complex tasks[2][3][5].

Essential Technical Specifications

• Context Length: Supports up to 128k tokens or 1 million context length in some variants.
• Training Data: Pre-trained on approximately ten trillion tokens of multilingual corpus.
• Architecture: Built on Transformer architecture with DeepNorm, Rotary Positional Encoding, and Gated Linear Unit[3][5].

Notable Performance Characteristics

• High Accuracy: Outperforms models like GPT-4, Gemini 1.0 Pro, and Claude 3 Opus in various benchmarks.
• Efficient Processing: Fast processing of large-scale datasets with high accuracy in image understanding and text generation[2][4][5].