Thread

1. jwr · 1789978273 · focus · HN ↗

   I wonder how these would do filtering my spam. I have been using 27B-class models for a while now, and they are nearly perfect at determining what is spam and what isn't. The only disadvantage is computational cost.

   1. walrus01 · 1789979607 · focus · HN ↗

      Take a look at Thomson 1.0-small, which is a variant of qwen 3.6 35b post trained by Thomson Reuters for text analysis. It classifies text content very well.

      1. Mumps · 1789989873 · focus · HN ↗

         Are you on the foundation research team for Thomson? (If so, hiya from B!) Why would you expect Thomson to be particularly good at spam clf? I figured your additional corpus was all news and legal?

         1. walrus01 · 1790010472 · focus · HN ↗

            I have no connection with Thomson Reuters other than as an end user of a GGUF of the LLM I mentioned. That said, from my personal experience with this specific LLM, it's a decent improvement over a "base" Qwen 3.6 35B A3B Q8, and it does a good job of analyzing and categorizing documents on relatively small resources. It'll run fine in llama-server in pure CPU only on a 64GB RAM system with plenty of room to spare, takes something like 47GB with RAM reserved in llama-server for cache and full context size.

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