LlamaParse 用推测性解码优化文档OCR效率
The difference with using VLMs for OCR vs. other tasks is that the ratio of output/input tokens is q...
LlamaParse 用新方法优化了文档OCR，能更快处理文本，比传统方法更高效。
使用 VLMs 进行 OCR 时，输出/输入令牌比例很高，模型会花费大量时间生成文本。推测性解码方法中，快速草稿模型先提出多个令牌，主模型再共同检查。被接受的令牌推进输出，被拒绝的则被修正。这种方法能减少顺序解码步骤的延迟，尤其适用于结构化文档中易于预测的文本部分。
The difference with using VLMs for OCR vs. other tasks is that the ratio of output/input tokens is q...
The difference with using VLMs for OCR vs. other tasks is that the ratio of output/input tokens is quite high - meaning the model can spend a lot of time writing markdown. Speculative decoding offers a useful approach where a fast draft model proposes several tokens, and then the main model checks them together. Accepted tokens move the output forward; rejected proposals are corrected. The net benefit here is you reduce latency on sequential decoding steps, especially if the document structure makes certain text easier to predict. Check out the video as an overview! We've built a lot of optimizations into LlamaParse to push the frontiers of accuracy and cost, and then latency. There's still a ton to come: cloud.llamaindex.ai Your browser does not support the video tag. 🔗 View on Twitter 💬 3 🔄 1 ❤️ 2 👀 442 📊 4 ⚡
