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DeepSeek开源新模型!单张A100日处理可超20万页数据

Core Insights - DeepSeek has released a new OCR model named DeepSeek-OCR, which focuses on context optical compression to address the challenges faced by large language models in processing long texts [1][4][6] Group 1: Model Features - The DeepSeek-OCR model utilizes visual modalities to efficiently compress text information, achieving nearly 10 times lossless context compression while maintaining an OCR accuracy of over 97% [4][5] - The model consists of two main components: DeepEncoder for image feature extraction and compression, and DeepSeek3B-MoE for reconstructing text from compressed visual tokens [5] - The model's architecture allows it to have the expressive power of a 30 billion parameter model while maintaining the inference efficiency of a 500 million parameter model [5] Group 2: Potential Applications - The research indicates potential applications in long context compression and the memory forgetting mechanisms of large models, simulating human memory decay by gradually reducing the size of rendered images over time [5][6] - This approach could represent a significant breakthrough in handling ultra-long contexts, balancing theoretically infinite context information [6] Group 3: Industry Reception - The release of the DeepSeek-OCR model has garnered positive attention, receiving over 1,400 stars on GitHub shortly after its launch [7] - There are mixed opinions in the market regarding DeepSeek's pace of innovation, with some suggesting that the company is focusing on internal development for future models [8]