How to use kenrogers/gte-ft-yt with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kenrogers/gte-ft-yt", trust_remote_code=True) sentences = [ "1. What advancements in technology are mentioned as contributing to faster inference times in applications? \n2. In what scenarios does the context suggest that response latency is less of a concern for users?", "your take on this yeah I mean so no not better uh it's definitely different it's definitely uh you know do it's trying to do a different thing which is dope I would say like at the end of the day uh they're they're using the same process but they're they're they're finding different ways to uh to take advantage of that process uh the recurrent depth is more of an architecture change right it's more of a let's actually get this reasoning inherent to the to the model we're going to train it to be very good at this recurrent task we're going to train it to do this this accordion thing that it does very well right uh versus coconut which is like let's adapt and add to existing uh architecture right to to get this uh this kind of reasoning flavor that that coconut winds to get or winds up getting so it's it's it's a it's the same process two different approaches though where they're coming at it from two different angles uh I would say like current depth is uh is interesting because it's", "right we kind of got to go a little bit more into the blackbox we gota go back beyond the unknown yeah it happens but it's it's it's it's the the timing is right and uh with companies like you know Nvidia with companies the other accelerators that are that are coming out they're super good at inference Gro and S all these other peeps right uh we're getting real fast at inference and so the spending that time you know becomes less and less impactful to the user experience but more importantly uh you know we have a lot of applications LMS aren't good for yet where we don't care about response latency like research like uh PhD level math where it's like it doesn't matter if it takes a day yeah because that means it didn't take some some other person a day right like that's the that's the the we're at this time the models are capable enough that we can think about problems that we can't just do ourselves faster it the whole the whole you know ecosystem is set up for this to be the right", "today that's right that's right so so reasoning is some right now because our models are System One machines right this is the this is the they're not reasoners they're they're uh they're they're just they they just do they just do they just do right uh we need some way to stretch them into this reasoning domain and the way that we do that is through some kind of test time computer some kind of test time scaling things that you know it's interesting to think about but something like an agent right is an example or expression of test time compute right we're we're we're using the agent to leverage more compute to do cooler things right so these kinds of systems are also test time compute uh very broad definition you love agents are also reasoning right that's right agents are reason there you go but the idea is that we we need some way to stretch the system one machine to a system two machine and the way that we know how to do that right now is is through these time compute methods" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
