LLM2CLIP-Openai-L-14-336
| Property | Value |
|---|---|
| Parameter Count | 579M |
| License | Apache-2.0 |
| Paper | arXiv:2411.04997 |
| Training Data | CC3M, CC12M, YFCC15M, Recap-DataComp-1B(30M subset) |
What is LLM2CLIP-Openai-L-14-336?
LLM2CLIP-Openai-L-14-336 is a groundbreaking vision foundation model that combines the power of Large Language Models (LLMs) with CLIP architecture to enhance visual-language understanding. Developed by Microsoft, this model represents a significant advancement in cross-modal capabilities, achieving a 16.5% improvement over previous state-of-the-art EVA02 model performance.
Implementation Details
The model employs a sophisticated fine-tuning approach where LLMs are trained in the caption space using contrastive learning. This process allows the model to handle longer and more complex captions while maintaining efficient processing capabilities. The architecture includes a vision encoder that learns from the fine-tuned LLM, acting as a teacher model.
- F32 tensor type implementation
- Supports zero-shot classification pipeline
- Incorporates custom code for enhanced functionality
Core Capabilities
- Enhanced cross-lingual performance despite English-only training
- Superior long-text and short-text retrieval capabilities
- Improved textual discriminability through LLM integration
- Seamless integration with multimodal systems like Llava 1.5
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its ability to leverage LLMs to enhance CLIP's capabilities, particularly in handling complex and longer text inputs while maintaining strong cross-modal performance. It achieves this without being restricted by vanilla CLIP text encoder limitations.
Q: What are the recommended use cases?
This model is particularly well-suited for cross-modal retrieval tasks, zero-shot classification, and scenarios requiring sophisticated visual-language understanding. It excels in both long-text and short-text retrieval applications, making it versatile for various industrial applications.
