How to use kenrogers/gte-ft-yt-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kenrogers/gte-ft-yt-2", trust_remote_code=True) sentences = [ "1. What is the significance of the beginning of thinking and end of thinking tokens in the context of recurrent depth? \n2. How does the concept of thinking about a single token relate to the overall sequence in the discussion?", "done by researchers at Lawrence Livermore and elsewhere um this is not this not you know something that product teams are using today this is this is very much a research grade thing that is cool and we're seeing some you know early signs that it's potentially quite useful um I I wanna I want to zoom in on on like just when people think about the the actual how of this when they think about actually implementing this in in maybe one of their applications so whereas in the Coconut space you're you're going to go and you're G to you're gonna like oh nope not going out into natural language space just going to sit here and chew on it chew on it chew on it and then I'm gonna pop out my final answer so all you get is final answer baby it's called a blackbox okay when we go to the recurrent depth piece um you said something interesting earlier when we were chatting about this and it was it was like I'm going to think and think and think and think and think and all of a sudden I know and I'm", "chains of thought and this is where this idea of test time compute came up and this was a paper from Google in August last year called scaling test time compute you know it's basically taking that scaling paper originally and saying well now we have this sort of other axis to scale on and again this is the idea that we're anthropomorphizing a little bit but humans tend to think longer on difficult problems maybe we should let machines do that and when we think of test time Compu it's just time spent thinking you know and so if we we think about kind of how we can leverage this we've seen some of these things come out in recent weeks and recent months we talked about deep seek R1 just last week and you know this is the same idea it thinks before it answers and this is again just sort of the next step in the evolution of what we've got going on here and we saw moreover deep seek one generates one token at a time it's able to spend more time processing and it generates these thinking", "piece um you said something interesting earlier when we were chatting about this and it was it was like I'm going to think and think and think and think and think and all of a sudden I know and I'm going to do that just with one token is sort of the recurrent depth paper and then I'm going to let the rest of the tokens stream like normal right so so there's this real interesting idea of like which things are you thinking about and this is where the idea of a beginning of thinking end of thinking token or a beginning of sequence end of sequence token comes into play you can think about the sequence or you can think about the thinking is this does this make sense um yeah okay okay we can think about the thinking right so we can uh we can double we can double it up uh yeah we can think about the thing yeah yeah okay okay so so recurrent depth in short I mean is like you think about a single token and then you let the sequence go like that's what I thought was interesting and and maybe" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
The community tab is the place to discuss and collaborate with the HF community!
