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LlamaParse optimizes VLM OCR with speculative decoding

Jerry Liu@jerryjliu0
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For developers, this is a practical optimization for using VLMs in OCR tasks, reducing processing time for document analysis.

LlamaParse uses a fast draft model to propose tokens for OCR tasks, which the main model then checks. This approach reduces latency in sequential decoding steps, especially for documents with predictable structures. The method is built into LlamaParse to improve accuracy and cost while lowering latency.

Image · Jerry Liu
Original · Jerry Liu

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 ⚡

#LlamaParse#OCR#VLMs#speculative decoding#LLM

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