Comment by jwr
15 hours ago
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.
15 hours ago
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.
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.
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?
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.