CogVLM Chat Model
| Property | Value |
|---|---|
| Parameter Count | 17.6B |
| License | Apache-2.0 |
| Paper | arXiv:2311.03079 |
| Tensor Type | BF16 |
What is cogvlm-chat-hf?
CogVLM is a state-of-the-art visual language model that combines powerful vision and language capabilities. With 10 billion vision parameters and 7 billion language parameters, it represents a significant advancement in multimodal AI technology. The model has achieved SOTA performance on 10 classic cross-modal benchmarks, competing with and often surpassing larger models like PaLI-X 55B.
Implementation Details
The model architecture consists of four key components: a Vision Transformer (ViT) encoder, an MLP adapter, a pretrained GPT-style language model, and a specialized visual expert module. It requires approximately 40GB of GPU VRAM for inference, though it can be split across multiple smaller GPUs using the accelerate library.
- Advanced visual-language processing capabilities
- Supports both chat and VQA (Visual Question Answering) modes
- Implements efficient multi-GPU distribution for resource management
- Uses BF16 precision for optimal performance
Core Capabilities
- State-of-the-art performance on NoCaps, Flicker30k captioning, RefCOCO series
- Excellence in visual question answering tasks (VQAv2, OKVQA, TextVQA)
- Advanced image description and understanding
- Robust performance in scientific and general question answering (ScienceQA, GQA)
Frequently Asked Questions
Q: What makes this model unique?
CogVLM's distinctive feature is its visual expert module and the balanced architecture of vision (10B) and language (7B) parameters, allowing it to achieve SOTA performance with fewer parameters than competitors.
Q: What are the recommended use cases?
The model excels in image captioning, visual question answering, reference object identification, and general visual-language understanding tasks. It's particularly suitable for applications requiring detailed image analysis and natural language interaction.
