DeepSeek on Monday released a new multimodal artificial intelligence model that can handle large and complex documents with significantly fewer tokens – the smallest unit of text that a model processes – by using visual perception as a compression medium for information.
The open-source DeepSeek-OCR (optical character recognition) model, available via online developer platforms Hugging Face and GitHub , was the result of an “investigation into the role of vision encoders” to compress text for large language models (LLMs), the Hangzhou -based AI start-up said in a blog post.
By using that approach, LLMs would be able to process a massive amount of text without incurring a proportional increase in computing cost.
“Through DeepSeek-OCR, we demonstrated that vision-text compression can achieve significant token reduction – seven to 20 times – for different historical context stages, offering a promising direction” to address long-context challenges in LLMs, the company said.
That showed DeepSeek’s steadfast efforts to raise the efficiency of AI models, while driving down the costs of building and using them – a principle that the company followed in the development of its breakthrough open-source models V3 and R1 that were released in December and February, respectively.
[LIVE] China Future Tech webinar | How is DeepSeek shaping the race for AI supremacy?
[LIVE] China Future Tech webinar | How is DeepSeek shaping the race for AI supremacy?
According to the company’s blog post, DeepSeek-OCR consisted of two main components: DeepEncoder and DeepSeek3B-MoE-A570M as the decoder.
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