Core technology implementation of the llm.pdf project
llm.pdf truly represents an innovative breakthrough in artificial intelligence applications. This project, developed by EvanZhouDev, realizes the ability to run LLM directly in PDF files through a unique combination of technologies. The core technology lies in the use of Emscripten to compile llama.cpp into asm.js and combining it with the JavaScript injection feature of PDF. This approach allows the complete model inference process to be accomplished directly in the PDF without relying on external servers or computing resources.
- The project supports quantization models in the GGUF format, and in particular recommends the use of the Q8 quantization model for the best performance balance.
- Model files are embedded in PDF documents via base64 encoding, which greatly simplifies the distribution and usage process.
- provides a supporting Python script generatePDF.py, to automatically complete the entire PDF generation process
This implementation provides a new way of thinking about the deployment and sharing of AI models, and is particularly suitable for scenarios that require offline use.
This answer comes from the articlellm.pdf: experimental project to run a large-scale language model in a PDF fileThe
