GLM-Edge-V-2B
| Property | Value |
|---|---|
| Model Size | 2 Billion parameters |
| Developer | THUDM |
| Framework | Transformers |
| Model URL | huggingface.co/THUDM/glm-edge-v-2b |
What is glm-edge-v-2b?
GLM-Edge-V-2B is a multimodal model designed for vision-language tasks, developed by THUDM. It represents a significant advancement in combining image understanding with natural language processing capabilities, optimized for efficient inference using bfloat16 precision.
Implementation Details
The model is implemented using the Hugging Face Transformers library and supports automatic device mapping for optimal performance. It utilizes an image processor and tokenizer architecture for handling both visual and textual inputs, making it particularly suitable for vision-language tasks.
- Supports bfloat16 precision for efficient inference
- Implements automatic device mapping for optimal resource utilization
- Uses a specialized chat template for processing inputs
- Capable of processing both images and text simultaneously
Core Capabilities
- Image-to-text generation
- Visual understanding and description
- Multimodal processing
- Efficient inference with mixed precision support
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to process both visual and textual information while maintaining efficiency through bfloat16 precision makes it particularly suitable for edge applications requiring multimodal capabilities.
Q: What are the recommended use cases?
The model is well-suited for tasks involving image description, visual question answering, and general vision-language applications where efficient processing is required.
