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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.
by walrus01 5d ago
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.
- Mumps 5d agoAre 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?
- walrus01 5d agoI 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.