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Description
DeepSeek-VL is an innovative open-source model that integrates vision and language capabilities, catering to practical applications in real-world contexts. Our strategy revolves around three fundamental aspects: we prioritize gathering diverse and scalable data that thoroughly encompasses various real-life situations, such as web screenshots, PDFs, OCR outputs, charts, and knowledge-based information, to ensure a holistic understanding of practical environments. Additionally, we develop a taxonomy based on actual user scenarios and curate a corresponding instruction tuning dataset that enhances the model's performance. This fine-tuning process significantly elevates user satisfaction and effectiveness in real-world applications. To address efficiency while meeting the requirements of typical scenarios, DeepSeek-VL features a hybrid vision encoder that adeptly handles high-resolution images (1024 x 1024) without incurring excessive computational costs. Moreover, this design choice not only optimizes performance but also ensures accessibility for a broader range of users and applications.
Description
Voyage AI has unveiled voyage-code-3, an advanced embedding model specifically designed to enhance code retrieval capabilities. This innovative model achieves superior performance, surpassing OpenAI-v3-large and CodeSage-large by averages of 13.80% and 16.81% across a diverse selection of 32 code retrieval datasets. It accommodates embeddings of various dimensions, including 2048, 1024, 512, and 256, and provides an array of embedding quantization options such as float (32-bit), int8 (8-bit signed integer), uint8 (8-bit unsigned integer), binary (bit-packed int8), and ubinary (bit-packed uint8). With a context length of 32 K tokens, voyage-code-3 exceeds the limitations of OpenAI's 8K and CodeSage Large's 1K context lengths, offering users greater flexibility. Utilizing an innovative approach known as Matryoshka learning, it generates embeddings that feature a layered structure of varying lengths within a single vector. This unique capability enables users to transform documents into a 2048-dimensional vector and subsequently access shorter dimensional representations (such as 256, 512, or 1024 dimensions) without the need to re-run the embedding model, thus enhancing efficiency in code retrieval tasks. Additionally, voyage-code-3 positions itself as a robust solution for developers seeking to improve their coding workflow.
API Access
Has API
API Access
Has API
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
DeepSeek
Founded
2023
Country
China
Website
www.deepseek.com
Vendor Details
Company Name
MongoDB
Founded
2007
Country
United States
Website
blog.voyageai.com/2024/12/04/voyage-code-3/