51–60 of 82 posts
There was an error while installing on Linux, was solved with: ''' sudo chown root:root /home/hn/AnythingLLMDesktop/anythingllm-desktop/chrome-sandbox sudo chmod 4755 /home/hn/AnythingLLMDesktop/anythingllm-desktop/chrome-sandbox ''' Other than that it worked really well.
Figure out how to assign proper permissions instead. (Or at least don't invite other people to do the same thing...)
Downloaded and checked it out. Looks great so far. Tried using it to read a bunch of regulatory PDFs using GPT-4o. Some quick and early feedback: 1. The citations seem to be a bit dicey. The response seems to give largely correct answers but the citations window seems to show content that's a bit garbled. 2. Please please add a text search to search within existing chat content. Like if I searched for something about…
For your 2nd suggestion, this would be solved if previous chats were (automatically) added to the vector database. Is this possible in this or similar apps?
Earlier quoted context omitted.
Large Language Model All good
The linked GitHub page mentions “Language Learning Models”, so GP is saying that it seems like the authors of this project wrote the whole thing while not knowing what LLM actually stands for.
LLM will become like web frameworks in the future, in the sense that they will be free, open source, and everybody will be able to build on them. Sure, there will be paid options, such as there are paid web frameworks, but most of the time free options will be more than good enough for most of the jobs.
Oh and the other question would be in your hosted version does the instance have access to a gpu that you provide? Which in that case we could just use a local model with (relative) ease right?
And if this hosted service is as I have described, I feel like this sort of service coupled with top tier frontier open source models is the (not too distant) future for AI!—- at least in the short to medium term (which is probably a pretty short period relatively, given the rapid speed of AI development).
Thanks
LLM will become like web frameworks in the future, in the sense that they will be free, open source, and everybody will be able to build on them. Sure, there will be paid options, such as there are paid web frameworks, but most of the time free options will be more than good enough for most of the jobs.
What is your argumentation for that? It seems that building LLMs is very costly, and that will probably remain so for quite some time. I think it is equally likely that LLMs will perform worse in the future, because of copyright reasons and increased privacy awareness, leading to less diverse data sets to train on.
"AI" as we're calling it is aggressively defacto open-source, and I can see in no way how that competition doesn't drive down prices.
Earlier quoted context omitted.
For your 2nd suggestion, this would be solved if previous chats were (automatically) added to the vector database. Is this possible in this or similar apps?
Yes. The fact that literally no one does this makes me continue to wonder wtf people are actually doing w/ LLMs, since I feel this need so acutely and the solution is so obvious.
I assume that this sort of functionality will become more commonplace.
LLM will become like web frameworks in the future, in the sense that they will be free, open source, and everybody will be able to build on them. Sure, there will be paid options, such as there are paid web frameworks, but most of the time free options will be more than good enough for most of the jobs.
What is your argumentation for that? It seems that building LLMs is very costly, and that will probably remain so for quite some time. I think it is equally likely that LLMs will perform worse in the future, because of copyright reasons and increased privacy awareness, leading to less diverse data sets to train on.
Building LLMs is dropping in cost quickly. Back in mid 2023 training an 7B model had already dropped to $30K and it's even cheaper now.
> I think it is equally likely that LLMs will perform worse in the future, because of copyright reasons and increased privacy awareness, leading to less diverse data sets to train on.
I'll bet a lot of money this won't happen.
Firstly copyright isn't settled on this. Secondly people understand a lot more now about how to use less, higher quality data and how to use synthetic data (eg MS Phi series, Persona dataset etc and of course the upcoming OpenAI Strawberry and Orion models which use synthetic data heavily). Thirdly the knowledge about how to use multi-modal data in your LLM is much more widely spread, which means that video and code can both be used to improve LLM performance.