转自差评X.PIN： DeepSeek昨天开源的新模型，有点邪门。 点击下方 卡片 ，关注" 大模型之心Tech "公众号 戳我-> 领取大模型巨卷干货 本文只做学术分享，如有侵权，联系删文 ，欢迎添加小助理微信AIDriver004做进一步咨询 DeepSeek 又整出新东西来了，只用到原本十分之一 token，就能存下几乎一样的文字信息，这压缩比，香农看了都要流泪，冯·诺伊曼看了都要沉默。 更是直接把一堆老外给钓成了翘嘴。 昨天，DeepSeek 发布了新模型 DeepSeek-OCR，OCR 这东西咱们都熟悉，就是把图片里的文字给识别出来。 经常用微信的差友应该知道，微信客户端里的图片，被咱们点开后，是可以直接复制文字的。 没错，这就是一种 OCR 技术的应用。 但是 DeepSeek 这次的 「技能 OCR」则是刚好相反，它可以把大量文字变成一张图片，作为 AI 的"记忆载体"。 是的，用文字来存储信息，已经不够满足它了。 过去的大模型，不管是什么 ChatGPT、Gemini、Llama、Qwen、还是 DeepSeek 过去的自己，在读取数据的用的都是一种方式： 文字，也就是平时常说的 token ...

Core Viewpoint - DeepSeek has introduced a new model, DeepSeek-OCR, which utilizes images to store information instead of relying solely on text tokens, significantly improving data compression and model efficiency [5][11][26]. Group 1: Model Functionality - DeepSeek-OCR can convert large amounts of text into images, serving as a memory carrier for AI, which allows for more efficient data storage [9][14]. - The model demonstrates superior performance by using fewer visual tokens compared to traditional models, achieving better results with less resource consumption [11][26]. - In tests, DeepSeek-OCR used only 100 visual tokens to outperform GOT-OCR 2.0, which required 256 tokens, and it achieved results with less than 800 visual tokens compared to over 6000 tokens for MinerU 2.0 [11][14]. Group 2: Data Collection and Utilization - The model can capture previously uncollected data from two-dimensional information, such as graphs and images in academic papers, which traditional models could not interpret [22][24]. - DeepSeek-OCR can generate over 200,000 pages of training data in a day on an A100 GPU, indicating its potential to enhance the training datasets for future models [24]. - The model's ability to remember the position of images and surrounding text allows for a more comprehensive understanding of the data [18][22]. Group 3: Resource Efficiency - By using image-based memory, DeepSeek-OCR can reduce the number of tokens required to one-tenth of the original, while maintaining a high accuracy rate of 96.5% [26][27]. - The model's design allows for dynamic adjustments in token usage based on the complexity of the document, optimizing resource allocation [14][15]. - The research indicates that even with a 20-fold compression, the model can retain around 60% accuracy, showcasing its robustness [27]. Group 4: Open Source Collaboration - DeepSeek-OCR is an open-source project that integrates contributions from various global open-source communities, utilizing datasets and models from companies like Huawei, Baidu, Meta, and OpenAI [32][34]. - This collaborative effort has resulted in a model capable of "thinking in images," highlighting the importance of community-driven innovation in AI development [34].

Core Insights - DeepSeek has introduced a new model called DeepSeek-OCR, which can compress text information into images, achieving a significant reduction in token usage while maintaining high accuracy [5][31][39]. Group 1: Model Capabilities - DeepSeek-OCR can store large amounts of text as images, allowing for a more efficient representation of information compared to traditional text-based models [9][10]. - The model demonstrates a compression ratio where it can use only 100 visual tokens to outperform previous models that required 256 tokens, and it can achieve results with less than 800 visual tokens compared to over 6000 tokens used by other models [14][31]. - DeepSeek-OCR supports various resolutions and compression modes, adapting to different document complexities, with modes ranging from Tiny to Gundam, allowing for dynamic adjustments based on content [17][18]. Group 2: Data Utilization - The model can capture previously unutilized data from documents, such as graphs and images, which traditional models could not interpret effectively [24][26]. - DeepSeek-OCR can generate over 200,000 pages of training data in a day on an A100 GPU, indicating its potential to enhance the training datasets for future models [29]. - By utilizing image memory, the model reduces the computational load significantly, allowing for a more efficient processing of longer conversations without a proportional increase in resource consumption [31]. Group 3: Open Source Collaboration - The development of DeepSeek-OCR is a collaborative effort, integrating various open-source resources, including Huawei's Wukong dataset and Meta's SAM for image feature extraction [38][39]. - The model's architecture reflects a collective achievement from the open-source community, showcasing the potential of collaborative innovation in AI development [39].