Training a 3.8B LLM to 0.384 CORE for $998 – Hugo Vergnes
38 points by Anon84 4 hours ago | 6 comments
johnnylambada 2 hours ago
I don’t blame you for using an LLM to write an article about an LLM that you built. I’ve been reading so much LLM output that now I see it everywhere. I wonder if humans will start writing more like LLMs?
replySubiculumCode 53 minutes ago
Anyone can level a charge of AI writing, but it's getting to the point that you must write sloppy to seem human. and never never write well with clear technical prose. It is getting to be just ridiculous McCarthyism.
replydakolli 4 minutes ago
I don't see it. I sit in a mild state of rage trying to decode llm prose all day, this isn't it.
reply
LLMs are interesting in their own ways but as an engineer, this is a way to unlock a new way of building software.
I recently build a Claude-assisted Excel/CSV parser for a US based property management system (tax compliance). Uses Haiku and has a lot of deterministic code to extract column/row combinations to check known formats and finally handing out the headers to Haiku to give us a translation plan to our support columns.
These would eventually become part of the software, in a tiny LLM. The gap between training (such tiny LLMs) and inference will shrink. We can consult Claude for edge cases, create sample dataset and train a the tiny LLM on demand so we go to Claude less.
The tooling that a project needs is really important. Something I have been feeling as well. Not just in LLM building projects, but regular software projects that are LLM generated.