fine-tuning

“That's one of those things where I think long-term, you definitely can. I think some people say you can't. I disagree. I think you definitely can. Just right now, I haven't gotten into work yet.”

“RAG is like such a inefficient hack, really, isn't it? It's like, You know, segment up my data in some somewhat arbitrary way, embed it, ask questions about that, you know, hope that my embedding, you know, model embeds questions in the same embedding space as…”

“To me, the right way to do this is to fine, fine-tune language models, is to actually throw away the idea of fine-tuning. There's no such thing. There's only continued pre-training.”

“Unless, unless you're fine tuning for on device use, I think fine tuning current existing models, especially the smaller ones is a useless waste of time, right?”

“Combining all those techniques all together, right? So we'll give you essentially another boost, right? So that kind of one thing that we learn on the technical side.”

“And like, at the end of the day, it's just all meta-learning, right? Like, all we want is, like, the best meta learning workflow or meta learning setup possible to be able to adapt the model to do anything.”

“Sorry, it wasn't the end of fine-tuning, but more that we should treat it as a continuum, and we should have much higher expectations of how much you can do with an already trained model. You can really add a lot of behavior to it. You can change its behavior.…”

“People are building too many vanity foundation models rather than taking better advantage of fine-tuning”

“Something over the next 12 months people will be spending time thinking about is how to, like, where to use RAG, where to use fine-tuning, where to use KV cache storage, you know, and how to use state.”

“It's actually a lot easier to get started than a lot of people expect. I think they might need Tens of thousands of examples, but even a hundred really high quality ones or a thousand is enough to get going.”

“There's a lot of cases now, especially with large context models, where in context learning just beats fine tuning.”

“For it to be a business, you need to align with the problem, not the technology. And I think that Automatic optimization is a really great business problem to solve. And I think if you're too fixated on fine tuning as the solution to that problem, then you're …”

“I will say in my own experience with customers as of the recording date today, which is September or something, yeah, very few of our customers are currently fine-tuning models. And I think a very, very small fraction of them are running fine-tuned models in p…”

“And I think we're going back to fine tuning where we realize these models are really cosplay. It's better to use just a small model. We try to specialize it. So I think it's a little bit of a cycle and we're going to start to see like more of fine tuning and l…”

“Not out of the box, but with a lot of that fine tuning and that the training, et cetera, you are able to kind of close the gap and even have better performance on metrics.”

“The dollar cost, I would say, is basically never a factor. It's just so much less than the time, the amount you're spending this engineer to do the work that it's not, I mean, it's, you know, each of these runs is between five and a couple of hundred dollars.”

“I would say for 90% of use cases where you aren't forced to a smaller model, then it's still not a good ROI, and you probably shouldn't invest in it today.”

“I can offer anecdotally that a single negative example goes a long way.”

“When it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning.”

“If there is a core capability that you actually care about, that capability should be part of the foundation and not a fine-tuned artifact.”

“It would actually really slow us down to have a model that was fine tuned on our tools because we'd have to retrain it and cut a new model every time we did that.”

“Just changing how the model behaves, you can do most, most of that via prompting nowadays, and in terms of capabilities, the models are very good out of the box.”

“MOEs are challenging to fine tune. I don't know if we've talked about that in the past, but MOEs in general are like an extremely good architecture. They work great for inference. But when people fine tune them, they struggle a bit. Like they are not as easy t…”

“You can't fine tune your way to success, right? Major capabilities emerge from training a base model made accurate and useful during fine tuning.”