agentset-ai

agentset-ai / vllm-studio

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Connect your local vLLM, upload documents, and get structured markdown, with support for tables, images, math, and more.

vllm.studio llamacpp ocr ollama pdf-to-markdown studio
14
2
100% credibility
Found Mar 22, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
TypeScript
AI Summary

vLLM Studio is a desktop app that uses vision language models to extract structured markdown from documents and images like PDFs and photos.

How It Works

1
📱 Discover vLLM Studio

You find this handy desktop app on its website or GitHub that turns messy PDFs and images into neat, readable text.

2
⬇️ Download and open

Grab the app for your Mac, launch it, and follow the friendly setup wizard to get started.

3
🧠 Pick your AI reader

Choose a smart model like Chandra or LightOnOCR, or connect your own AI service, and start it with one click.

4
📤 Drop your document

Drag a PDF, photo, or image file into the app and watch it eagerly process every page.

5
🔍 See the magic

Your document transforms into clean markdown with perfect tables, math equations, and cropped images.

âś… Ready to use

Copy the organized text, download images, or save for later—your content is now structured and beautiful!

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AI-Generated Review

What is vllm-studio?

vLLM Studio is a macOS desktop app built in TypeScript with Electrobun and React that hooks into your local vLLM server (via llama-server) to process uploaded PDFs and images into structured markdown. Drop in docs like research papers or reports, and it extracts layout-aware content—tables as GFM, math in KaTeX, cropped images, code blocks—keeping everything local for privacy. No cloud dependency, just connect localhost and go.

Why is it gaining traction?

It stands out by delivering clean, production-ready markdown from vision models without manual prompting or brittle scripts, handling OCR, tables, and diagrams out of the box. Auto server management, page-parallel processing, and model profiles for tuned VLMs like Chandra OCR make it dead simple versus cobbling together pdf.js and OpenAI APIs. Cherry studio vllm users love the zero-config local flow, especially for connect local llm to internet-free workflows.

Who should use this?

Document-heavy ML engineers building RAG pipelines or local inference apps who need reliable doc-to-markdown extraction. Researchers parsing papers with tables/math, or devs prototyping connect local git repo to github-style tools but for docs. Ideal if you're on macOS and tired of cloud OCR costs or inconsistent outputs.

Verdict

Grab it if you're experimenting with local VLMs on macOS—solid for quick doc processing, with auto-updates and intuitive UI. At 14 stars and 1.0% credibility, it's early (Linux/Windows soon), but the core delivers; test with your llama-server setup before committing. (187 words)

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