ColVintern-1B-v1
| Property | Value |
|---|---|
| Parameter Count | 938M |
| Model Type | Vision-Language Model |
| Architecture | Transformer-based with Colpali pipeline |
| Languages | Vietnamese, English |
| Tensor Type | BF16 |
What is ColVintern-1B-v1?
ColVintern-1B-v1 is a specialized vision-language model designed for Vietnamese and English document understanding. Built on Vintern-1B-v2, it implements the Colpali pipeline for efficient RAG (Retrieval-Augmented Generation) by extracting embedding vectors from questions and images containing relevant information. This model achieves comparable results to larger Colpali models while maintaining a smaller parameter count of 938M.
Implementation Details
The model has been trained on multiple datasets including Colpali dataset and Vietnamese image-based QA pairs. It utilizes a transformer architecture with late interaction approach for processing both text and visual inputs. The model operates in BF16 precision and demonstrates strong performance across various document understanding benchmarks.
- Trained on 4 specialized datasets including Colpali training set
- Implements efficient RAG capabilities for document understanding
- Achieves 78.8% average accuracy across benchmark tests
- Optimized for both Vietnamese and English language processing
Core Capabilities
- Bilingual document understanding and QA
- Image-text embedding generation
- Cross-modal retrieval
- Document visual question answering
- Performance comparable to 2B-3B parameter models
Frequently Asked Questions
Q: What makes this model unique?
ColVintern-1B-v1 stands out for its efficient bilingual capabilities and smaller parameter count while maintaining competitive performance with larger models. It's specifically optimized for Vietnamese language processing while supporting English, making it ideal for multilingual document understanding tasks.
Q: What are the recommended use cases?
The model is best suited for document visual question answering, information retrieval from images containing text, and cross-lingual document understanding tasks. It's particularly effective for applications requiring Vietnamese language support in document processing and visual understanding.
