Understand any PDF without reading it.
Summaries, key points, and entity extraction from your documents — powered by on-device LLMs, no servers.
Smarter reading. Zero uploads.
Point a local LLM at your document and get structured insight in seconds.
100% private
Your documents never leave your device. Inference runs locally via WebGPU.
Two open models
Choose SmolLM2 1.7B or Qwen2.5 1.5B — small, fast, and remarkably capable.
Long-document aware
Long PDFs are summarized part by part, then distilled into a final brief.
Structured output
Markdown-ready summaries, key points, and entity lists you can copy anywhere.
Three steps. Zero servers.
- 01
Upload a PDF
Text is extracted page by page in your browser via pdf.js. Nothing is uploaded.
- 02
Pick a task & model
Summary, key points, entity extraction, or your own question — answered by SmolLM2 or Qwen2.5 running locally.
- 03
Read the brief
Answers stream in as they are generated, ready to copy as Markdown.
About the PDF summarizer
A free tool that summarizes PDFs and answers questions about them with a language model that runs on your device — no API keys, no uploads, no per-page fees. It is aimed at students, researchers, and professionals who need the gist of long papers, reports, and documentation quickly.
How it works
pdf.js extracts the text layer locally, then the document is chunked and mapped-reduce style summarized by SmolLM2, a small language model running in the browser via Transformers.js. In QA mode, the most relevant passages are retrieved and answered from the document itself. Progress streams live, and results are copy-ready or exportable as Markdown.
Limits & requirements
On-device models are small by design: they summarize well and answer factual questions grounded in the text, but they are not a frontier chatbot for open-ended reasoning. Only text-based PDFs are supported (scanned PDFs need OCR). Chrome or Edge with WebGPU is strongly recommended; model weights download once and are cached.
Privacy
Papers, contracts, and notes never leave your machine. Summarization runs locally after the one-time model download, so documents that would be reckless to paste into a cloud LLM can be summarized safely.