LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned
| Property | Value |
|---|---|
| Parameter Count | 7.5B |
| Model Type | Vision Foundation Model |
| License | Apache 2.0 |
| Paper | arXiv:2411.04997 |
| Training Data | CC3M, CC12M, YFCC15M, Recap-DataComp-1B(30M subset) |
What is LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned?
LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned is an innovative model that bridges the gap between large language models and CLIP's visual understanding capabilities. Developed by Microsoft, this model represents a significant advancement in cross-modal AI by fine-tuning the LLM in the caption space using contrastive learning.
Implementation Details
The model utilizes a BF16 tensor type and implements a novel training process where the fine-tuned LLM serves as a teacher for CLIP's visual encoder. It's designed to handle both longer and more complex captions, overcoming the traditional limitations of vanilla CLIP text encoders.
- Improved textual discriminability through LLM fine-tuning
- Enhanced cross-modal performance with 16.5% improvement over EVA02
- Advanced cross-lingual capabilities despite English-only training data
- Seamless integration with multimodal systems like Llava 1.5
Core Capabilities
- Zero-shot classification tasks
- Cross-modal retrieval operations
- Enhanced text-to-image matching
- Multilingual understanding and processing
Frequently Asked Questions
Q: What makes this model unique?
This model uniquely combines LLM capabilities with CLIP's visual understanding, enabling superior performance in cross-modal tasks while supporting longer and more complex text inputs than traditional CLIP models.
Q: What are the recommended use cases?
The model excels in zero-shot classification, image-text retrieval, and cross-lingual applications. It's particularly useful for applications requiring sophisticated understanding of both visual and textual content.
