GLM-4V-9B
| Property | Value |
|---|---|
| Parameter Count | 13.9B |
| Model Type | Multimodal LLM |
| License | GLM-4 |
| Tensor Type | BF16 |
| Paper | Research Paper |
What is GLM-4V-9B?
GLM-4V-9B is a state-of-the-art multimodal language model developed by THUDM, capable of processing both text and images at high resolution (1120 x 1120). It's particularly notable for outperforming models like GPT-4-turbo, Gemini 1.0 Pro, and Claude 3 Opus in various multimodal evaluation benchmarks.
Implementation Details
The model utilizes a transformer-based architecture with 13.9B parameters and supports an 8K context length. It's implemented using the Hugging Face transformers library and requires BF16 precision for optimal performance.
- Supports both Chinese and English languages
- High-resolution image processing capability
- Implements advanced visual-language understanding
- Requires minimal CPU memory usage during inference
Core Capabilities
- Comprehensive visual understanding and reasoning
- Superior performance in MMBench evaluations (81.1% EN, 79.4% CN)
- Advanced OCR capabilities with 786 benchmark score
- Excellent performance in image-text dialogue systems
- Strong graph and chart comprehension abilities
Frequently Asked Questions
Q: What makes this model unique?
GLM-4V-9B stands out for its exceptional performance in multimodal tasks, particularly in Chinese-English bilingual capabilities and high-resolution image understanding. It achieves state-of-the-art results across multiple benchmarks, including MMBench, SEEDBench_IMG, and OCRBench.
Q: What are the recommended use cases?
The model is ideal for applications requiring sophisticated image-text understanding, including visual question answering, image description, document analysis, and complex multimodal reasoning tasks. It's particularly effective for bilingual applications requiring both Chinese and English language processing.
