3 ms·

Thanks for your feedback. I completely agree with you, I have some scripts for detecting common sequences (n-grams), there's a lot BTW, but since this is just a

• leakycap 1y ago
  I wonder why an LLM wouldn't be better at 'natural' sounding internet comments, given the unending source of samples I can imagine were fed in. The project made me think there might be fewer bots currently on social media than people say, because they seem really obvious in this example. Thanks for sharing.
  • 7rin0 1y ago
    LLMs are getting better every day, though some are more effective than others depending on several factors (model design, token limits, training, etc.). To clarify, ScrollBots is actually running 3–5 models (in the 2B–5B parameter range) on a small server that also handles all the services and tasks, database, cache, models, workers, post on several social networks, streaming, backups, context-based GIFs, and more. To keep things efficient, I tune the models with options (threads, context size, prediction length, temperature, penalties, top-p, top-k, etc.) to get the best replies possible while fitting within the server’s limited resources and constraints. Of course, this isn’t a production-ready setup in terms of architecture :)
    • leakycap 1y ago
      Ah, I was moving too quickly and didn't catch the small model sizes. Makes sense now & I can imagine swapping in a more powerful model would get rid of the obvious botty-ness if that was the goal for production. Cool that this can run on a small shared system!