Shypd.aiThe largest open index of AI toolsllm.pdf is an open-source project that demonstrates running Large Language Models directly inside a PDF file. It compiles llama.cpp into asm.js for execution within the PDF, embedding the entire LLM for inference.
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llm.pdf is a proof-of-concept project showcasing the ability to run an entire Large Language Model (LLM) within a PDF file. This innovative approach leverages Emscripten to compile llama.cpp into asm.js, enabling the LLM to execute directly within the PDF environment through an old PDF JS injection method. The entire LLM file is embedded into the PDF using base64 encoding, allowing for self-contained LLM inference. While currently a proof-of-concept, it highlights the potential for highly portable and self-sufficient AI applications. Users can generate custom PDFs with compatible GGUF quantized models, with 135M parameter models taking approximately 5 seconds per token for input/output.
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