I've recently opened a GitHub repository which includes information for both AI model series[0] and frontends you can use to run them[1]. I've wrote a Reddit post beforehand that's messier, but a lot more technical[2].
I try to keep them as up-to-date as possible, but I might've missed something or my info may not be completely accurate. It's mostly to help get people's feet wet.
[0] - https://github.com/Crataco/ai-guide/blob/main/guide/models.m...
[1] - https://github.com/Crataco/ai-guide/blob/main/guide/frontend...
[2] - https://old.reddit.com/user/Crataco/comments/zuowi9/opensour...
these could be useful:
https://github.com/Crataco/ai-guide/blob/main/guide/models.m... -> https://old.reddit.com/user/Crataco/comments/zuowi9/opensour...
https://github.com/cocktailpeanut/dalai
the 4-bit quantized version of LLaMA 13B runs on my laptop without a dedicated GPU and I guess the same would apply to quantized vicuna 13B but I haven't tried that yet (converted as in this link but for 13B instead of 7B https://github.com/ggerganov/llama.cpp#usage )
GPT4All Lora's also works, perhaps the most compelling results I've got yet in my local computer - I have to try quantized Vicuna to see how that one goes, but processing the files to get a 4bit quantized version will take many hours so I'm a bit hesitant
PS: converting 13B Llama took my laptop's i7 around 20 hours and required a large swap file on top of its 16GB of RAM
feel free to answer back if you're trying any of these things this week (later I might lose track)
On that note, why is any RAM needed? Can't the files be loaded and diffed chunk by chunk?