Entiende cualquier PDF sin leerlo.
Resúmenes, puntos clave y extracción de entidades — en tu navegador, sin servidores.
Lee mejor. Cero subidas.
Un LLM local lee tu documento y entrega ideas estructuradas en segundos.
100% privado
Tus documentos nunca salen de tu dispositivo. La inferencia corre localmente vía WebGPU.
Dos modelos abiertos
Elige SmolLM2 1.7B o Qwen2.5 1.5B — pequeños, rápidos y sorprendentemente capaces.
Pensado para documentos largos
Los PDFs largos se resumen por partes y luego se destilan en un informe final.
Salida estructurada
Resúmenes, puntos clave y listas de entidades listos para Markdown.
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.