Summarize:

As a kind of "just because I can, even though it's pointless" project I've been playing around with using LLMs for what amounts to flavor text on raw data. For example, turning raw weather data into more celebratory allusions to themes or stories. Something to highlight the beauty inherent in the seemingly ordinary moments of life.

Vicuna 7b 1.1's been one of the solid winners for me in terms of speed and writing quality. Also in keeping track of comparisons. Not getting item 1 and item 2 mixed up when comparing and contrasting data points for example. It's been the best for me when it comes to providing a more balanced combination for everything I'm looking for. A good jack of all trades if you will. The 13b is even better of course.

One recent surprise for me was metharme 7b. The llama model trained on the pygmalian roleplay dataset. I just swapped it out a handful of times out of curiosity. But I was surprised by how good some of the results were given that I'm presumably using it pretty differently than intended. But I guess it makes sense that it'd be good with more descriptive language than the norm. I haven't had a huge amount of time to really play around with it too much. But it might be worth giving a shot.

As for resources, I think you should be good if you're working with automated processes. I just schedule a lot of my stuff to run while I'm asleep. My cpu's pretty solidly mid-tier and it still handles 7b at an acceptable pace with llama.cpp. I wouldn't want to sit there as it chugge