open source models

“GPT Neo X was that it was one of the major models that had everything fully documented and they like, why they make this change in the architecture and so on and so forth. And that became like Basically reference notes for all other subsequent open source mode…”

“Beyond OpenAI's model, and beyond ChatGPT and Claudia, the two big models, right? Outside of the English speaking nations, right? A lot of the open source models really fall flat.”

“Yeah, I mean, for completions, open source is, is state of the art right now.”

“And this might be a little controversial, but like, I find a lot of arguments, ah, based on whether, like, closed source models are safer, or open source models are safer, very much related to whether, what kind of cultural, ah, culture they grew up in, what k…”

“Maybe open source models are being as used as GPT is at this point in, like, all kinds of, in a very fragmented way. Like, in aggregate, all the open source models together are probably being used as much as GPT is. Maybe, you know, close to that.”

“Probably doesn't have a chance against Google, because, you know, Google has Android, and Chrome, and Gmail, and Google Docs, and everything, you know. So people just use that a lot.”

“If that central sinkhole is there, who's gonna go coordinate all of this integration across all of these, like, open source frontends? But I think if we do that, if that actually happens, I think that probably has a real chance of the open source models having…”

“I'd say, like, in terms of number of careers, it's maybe similar. In terms of, like, cost and compute, I think the closed models make, make up more of the budget, since the main cases where you want to use closed models are cases where they're just smarter, wh…”

“Releasing models open source is not our day-to-day bread and butter. It's kind of a fun reward that we get to do sometimes when we have something really cool to share and a little bit of time and spare GPUs in our hands. But for the most part, everything is go…”

“Among customers running in production, it's less than five percent.”

“Early on, we were able to leverage open source models, sort of bootstrap it internally within our company. But then to ship, we finally had some requirements like, Hey, we want this model to have fill in the middle capabilities and a bunch of other things. And…”

“Another thing at a very practical level that we've thought about with open source models is that people building on our open source model are kind of building on our tech stack, right? If you are relying on us to help improve the model that you're relying on u…”

“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.”

“We see at the large enterprise level, particularly those who have large data assets, a desire to train their own models and to go a little smaller, say in the 10 to thirty billion parameter category. I think especially the very large companies, enterprises hav…”

“I think for sort of AI companies like Cognition, like all your competitors, like AlphaSense, like dozens of others, they are trying to replace closed source models with very, very fast open source models, and they're trying to drive the open source Accuracy dr…”

“I think it's going to go up because of the amount of enterprise adoption of open models that I'm seeing. And also there's a lot of demand. Like there's the enterprises would much rather be on open models if they actually could get the performance they're loo…”

“I think once you take coding out, I think, yeah, it can be like 15, 20%, but I think with coding, it's still gonna be very low because like these max plans are like, So subsidized and so many tokens are being generated”

“I think you're now with, you know, we'll be seeing the trend going also with the coding agents, you know, fine-tuning open source models and like way faster as a result and more accurate. You can actually beat frontier models on quality that way.”

“Interestingly, in Tau Tau Two Bench Telecom, it's cheaper to run, you know, on a per token basis, more expensive models, like a GBD five, compared to some smaller open source models, because the some of the GBD five, for instance got to the answer faster. And …”

“Like all the open source models, I think what happened was they got like very hyped and people were very interested in using them. But I think like over time, like there was a spike in usage for these models. And then it goes back to open AI, Anthropic and Goo…”

“You can have basically an inexpensive computer running, you know, what was a state of the art model year and a half ago, running it locally on a device with an open source model, which also means that the Chinese can have it, the Russians can have it, the Nort…”

“I think for example, LM inference, thousands more companies are going to post train their own models and deploy open source models for inference.”

“And actually, if you look at the amount of value captured, it's actually the closed source frontier models. You know, the whole pie is growing, but it's growing so fast that even as the open source models like share expands, the frontier models are still able …”