Three solutions to improve the performance of LLM in PDF
The following optimization strategies can be used to target performance bottlenecks:
- Model Selection: Prioritize the use of the Q8 quantized 135M parametric model, which has an inference speed of about 5 seconds/token
- Equipment Configuration: Recommended to run on devices with 8GB+ RAM, browsers need to enable WebAssembly acceleration support
- Interaction Optimization: Keep prompt to 50 words or less and close other CPU-hungry applications
Deep Optimization Tips:
- Modify the chunk_size parameter (default 4096) in generatePDF.py to adjust the memory allocation.
- You may get better asm.js execution efficiency by using Firefox instead of Chrome.
- Enable the javascript.options.asm_js switch in your browser's about:config
This answer comes from the articlellm.pdf: experimental project to run a large-scale language model in a PDF fileThe
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