I use them for classification, entity extraction and embeddings. I also use them as a really light UI for tasks where I don't need the capabilities of GPT3.5/4 which is overkill for the more traditional NLP tasks.

Interesting. Could you share some detailed examples? What sort of classification? What are examples of traditional NLP tasks?

I would say anything from text classification, entity extraction, sentiment analysis, transcription, etc would be traditional NLP task, even things such as NYSIIS/Soundex processing can be handled by LLM's fairly effectively.

Huggingface categorizes their various models by task https://huggingface.co/models so it is fairly straight forward to determine what models may fit your use case.

I have also been trying to find good use cases. Currently I use Mistral 7B for summarization (when I want a tldr) and its good enough. Due to this I am thinking about setting up a RAG system based on my local notes using it. Although I could just use chatGPT for summarization (sometimes I do), but having a local LLM feels nice. They are uncensored so they can be used for some fun ropleplays as well.

When I want to run it locally on normal hardware.