Currently, the open-source version of Librarian uses a general-purpose model to read the summary index and route the relevant messages. It works great for accuracy and drastically cuts token costs, but it does introduce a latency penalty for shorter conversations because it requires an initial LLM inference step before your actual agent can respond.

To solve this, we are currently training a heavily quantized, fine-tuned model specifically optimized only for this context-selection task. The goal is to push the selection latency below 1 second so the entire pipeline feels completely transparent. (We have a waitlist up for this hosted version on the site).

If anyone here has experience fine-tuning smaller models (like Llama 3 or Mistral) strictly for high-speed classification/routing over context indexes, I'd love to hear what pitfalls we should watch out for.