The Coming of Local LLMs
21–30 of 214 posts
Re: The Coming of Local LLMs
#22Re: The Coming of Local LLMs
#23I was able to run a LLaMa on my personal machine to run some labeling on my documents, as a test of its capabilities. It was instruct tune. 30b parameters 4 example labels, and I had a binary classifier in seconds. Sure, semantic text classifiers were possible for a while, but making it accessible changes everything. Giving anyone who can use a spreadsheet the power of a local LLM (or, basically free LLMs) can make t…
Maybe both the buzz factor and broader applicability means it's more likely to happen this go around?
Re: The Coming of Local LLMs
#24I expect we will see the biggest jump in performance if (when) consumer-grade coprocessors like mobile GPUs start incorporating attention layers as a primitive building block at the hardware level, e.g., with instructions and memory layouts engineered specifically to make ultra-low-precision (say, 4-bit) transformer layers as compute- and memory-efficient as possible on consumer devices. That seems almost inevitable…
Re: The Coming of Local LLMs
#25How realistic is CPU-only inference in the near future?
Re: The Coming of Local LLMs
#26I found this to be very liberating, that I can finally type whatever I want into the LLM, without the possibility of the government knowing what I am writing. Just being able to do that, and have the watchful eye of the state not being able to monitor you is amazing.
Re: The Coming of Local LLMs
#27Re: The Coming of Local LLMs
#28I was able to run a LLaMa on my personal machine to run some labeling on my documents, as a test of its capabilities. It was instruct tune. 30b parameters 4 example labels, and I had a binary classifier in seconds. Sure, semantic text classifiers were possible for a while, but making it accessible changes everything. Giving anyone who can use a spreadsheet the power of a local LLM (or, basically free LLMs) can make t…
Agree, though plain old Bayesian classifiers have been able to handle some significant portion of that office work for a long time. And not much ever came from it for everyday stuff outside of spam filters. Maybe both the buzz factor and broader applicability means it's more likely to happen this go around?
If you're interested, see this paper that argues that point: https://arxiv.org/abs/2302.06541
Essentially, being label efficient is more important than being compute efficient, because the biggest computing constraint we have is enough humans doing the labeling (and knowing how to work a jupyter notebook), not tensor smashing nvidia cards
Re: The Coming of Local LLMs
#29Re: The Coming of Local LLMs
#30I was able to run a LLaMa on my personal machine to run some labeling on my documents, as a test of its capabilities. It was instruct tune. 30b parameters 4 example labels, and I had a binary classifier in seconds. Sure, semantic text classifiers were possible for a while, but making it accessible changes everything. Giving anyone who can use a spreadsheet the power of a local LLM (or, basically free LLMs) can make t…
Agree, though plain old Bayesian classifiers have been able to handle some significant portion of that office work for a long time. And not much ever came from it for everyday stuff outside of spam filters. Maybe both the buzz factor and broader applicability means it's more likely to happen this go around?
Sure, you and I know how to write a little script to sort a directory of documents into "schoolwork" and "other stuff".
But most people don't have that ability, so giving them that would really help accessibility.