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Sudeep Pillai

@spillai • 2,787 subscribers

Founder / CEO @vlmrun | FF23 @southpkcommons | ML Leader @ToyotaResearch | CS PhD @MIT CSAIL #girldad

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You can now classify documents with open-weight VLMs at ultra-low cost with our TypeSafe AI Jev-compatible API in the VLM Run gateway! $1 buys you 6700+ pages, and the full 1,651-page OmniDocBench run cost 25 cents. It reads the page as pixels (no OCR->LLM hacks) and hands back one typed, calibrated decision per page - ~180ms p50 e2e. Jev really opened developers' eyes to re-thinking how we process data at the cost-pareto front. A week later we're bringing that to our APIs with vision as a first-class citizen. Try our gateway today - free to sign-up and try!

You can now classify documents with open-weight VLMs at ultra-low cost with our TypeSafe AI Jev-compatible API in the VLM Run gateway! $1 buys you 6700+ pages, and the full 1,651-page OmniDocBench run cost 25 cents. It reads the page as pixels (no OCR->LLM hacks) and hands back one typed, calibrated decision per page - ~180ms p50 e2e. Jev really opened developers' eyes to re-thinking how we process data at the cost-pareto front. A week later we're bringing that to our APIs with vision as a first-class citizen. Try our gateway today - free to sign-up and try!

20,816 次观看

VLMs can be surprisingly accurate and token-efficient on the right tasks. In a bake-off between VLM-based document classification vs. OCR-based classification using our new TypeSafe AI-compatible SystemOne API, we're seeing over ~7x token-efficiency! VLM: 372 tokens/page, fixed. OCR->Text: 2,002 median, 19,159 on the worst page.

VLMs can be surprisingly accurate and token-efficient on the right tasks. In a bake-off between VLM-based document classification vs. OCR-based classification using our new TypeSafe AI-compatible SystemOne API, we're seeing over ~7x token-efficiency! VLM: 372 tokens/page, fixed. OCR->Text: 2,002 median, 19,159 on the worst page.

10,887 次观看

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