DeepSeek的新模型很疯狂：整个AI圈都在研究视觉路线，Karpathy不装了

Core Insights - The article discusses the groundbreaking release of the DeepSeek-OCR model, which compresses 1000 words into 100 visual tokens while maintaining a high accuracy of 97% [1] - This model addresses the long-context efficiency issue in large language models (LLMs) and suggests a paradigm shift where visual inputs may be more effective than textual inputs [1][5] Group 1: Model Features and Performance - DeepSeek-OCR can process 200,000 pages of data daily using a single NVIDIA A100 GPU [1] - The model's compression efficiency is ten times better than traditional text tokens, allowing for a significant reduction in the number of tokens needed to represent information [9] - The model eliminates the need for tokenizers, which have been criticized for their complexity and inefficiency [6] Group 2: Community Reception and Expert Opinions - The open-source nature of DeepSeek-OCR has led to widespread validation and excitement within the AI community, with over 4000 stars on GitHub shortly after its release [2][1] - Experts like Andrej Karpathy have praised the model, highlighting its potential to redefine how LLMs process inputs [3][5] - The model has sparked discussions about the efficiency of visual tokens compared to text tokens, with some researchers noting that visual representations may offer better performance in certain contexts [9][11] Group 3: Implications for Future Research - The article suggests that the use of visual tokens could significantly expand the effective context length of models, potentially allowing for the integration of extensive internal documents into prompts [12][13] - There are references to previous research that laid the groundwork for similar concepts, indicating that while DeepSeek-OCR is innovative, it is part of a broader trend in the field [18][20] - The potential for combining DeepSeek-OCR with other recent advancements, such as sparse attention mechanisms, is highlighted as a promising avenue for future exploration [11][12]