I've been getting more into running LLMs locally, and I'm starting to wonder if fine-tuning is even worth the effort anymore. Models are getting really good, context windows are huge, and with things like RAG, subagents, and reasoning models, it seems like you can get pretty far without touching the model weights. For most use cases, wouldn't you be better off just using a good base model and building a decent agent around it? I'm curious what people are actually fine-tuning for these days. Is it mostly about distilling a larger model into something smaller and faster, or are there still use cases where fine-tuning makes a noticeable difference over just prompting a good model? Would love to hear from people who've actually fine-tuned models recently. What did you use it for, and was it worth the effort?   submitted by   /u/f0urxio [link]   [comments]