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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(63) | [
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] | p | And you will look at their retrieval section and see which one of those marks very close to whatever is your domain or your problem area, basically. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,572 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(63) | [
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[
"Quantized Emdedding Models - Qdrant",
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] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(63) | [
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[
"Quantized Emdedding Models - Qdrant",
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] | p | So that is where you would start. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,575 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(64) | [
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"tags",
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[
"Quantized Emdedding Models - Qdrant",
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] | p | But it’s not going to take you, let’s say, a month to do this. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,578 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(64) | [
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] | p | So just to put a rough order of magnitude, once you have that, you try to take whatever is the best model on that subdomain data set and you see how does it work within your domain and you launch from there. | [
"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
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[
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[
"Quantized Emdedding Models - Qdrant",
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] | p | Like a civil engineer builds a bridge around how many cars they expect, they’re obviously not going to build a bridge to carry a ship load, for instance, or a plane load, which are very different. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,583 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(64) | [
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[
"Quantized Emdedding Models - Qdrant",
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] | p | So for instance, one of the reasons I mentioned binary quantization and product quantization is with something like binary quantization you can get 98% recall, but with 30 to 40 x memory savings because it discards all the extraneous bits and just keeps the zero or one bit of the embedding itself. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,586 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(65) | [
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[
"Quantized Emdedding Models - Qdrant",
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] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(65) | [
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[
"Quantized Emdedding Models - Qdrant",
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] | p | So you might want to use that to just massively scale while keeping your budgets as an engineer. | [
"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(65) | [
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] | p | Now, in order to do this, you need to have some sense of three numbers, right? | [
"tags",
"tags/quantized-emdedding-models"
] | 16,590 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(65) | [
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
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"tags",
"tags/quantized-emdedding-models"
] | 16,592 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(66) | [
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"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
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] | p | Is what I was waiting for. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,594 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(66) | [
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] | p | Man, so excited for this one, exactly this. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,595 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(66) | [
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] | p | Please tell us the hacker answer. | [
"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(67) | [
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[
"Quantized Emdedding Models - Qdrant",
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[
"Quantized Emdedding Models - Qdrant",
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"tags",
"tags/quantized-emdedding-models"
] | 16,599 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(67) | [
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] | p | The second is most embedding models which are larger or equivalent to 768 embeddings, can be optimized and improved by adding a small linear head over it. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,600 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(67) | [
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"tags",
"tags/quantized-emdedding-models"
] | 16,601 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(67) | [
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"tags",
"tags/quantized-emdedding-models"
] | 16,602 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(67) | [
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[
"Quantized Emdedding Models - Qdrant",
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[
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] | p | I think the largest I have used in production is about 400 500 KBS. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,605 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(67) | [
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] | p | That’s about it. | [
"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(67) | [
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] | p | And that will improve your recall several, several times. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,607 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(68) | [
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[
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] | p | And a third bonus hacker trick is if you’re using an LLM, sometimes what you can do is take a question and rewrite it with a prompt and make embeddings from both, and pull candidates from both. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,610 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(68) | [
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] | p | And then with Qdrant Async, you can fire both these queries async so that you’re not blocked, and then use the answer of both the original question which the user gave and the one which you rewrote using the LLM and see select the results which are there in both, or figure some other combination method. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,611 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(68) | [
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] | p | Also, so most Kagglers would be familiar with the idea of ensembling. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,612 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
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[
"Quantized Emdedding Models - Qdrant",
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] | p | Okay, dude, I’m not going to lie, that was a lot more than I was expecting for that answer. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,615 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(70) | [
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(71) | [
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"tags",
"tags/quantized-emdedding-models"
] | 16,621 |
[
"Quantized Emdedding Models - Qdrant",
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] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(71) | [
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] | p | So what about when it comes to the know, we had Andre V, the CTO of Qdrant on here a few weeks ago. | [
"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
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] | p | He was talking about binary quantization. | [
"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(71) | [
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"tags",
"tags/quantized-emdedding-models"
] | 16,624 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(71) | [
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] | p | Can you explain a little bit more about what quantized embedding models are and how they enhance the CPU performance? | [
"tags",
"tags/quantized-emdedding-models"
] | 16,625 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(72) | [
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[
"Quantized Emdedding Models - Qdrant",
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[
"Quantized Emdedding Models - Qdrant",
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[
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"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(72) | [
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"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
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] | p | So you can basically go from let’s say level zero, which is there are no optimizations to let’s say 99 where there’s a bunch of extra optimizations happening. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,631 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(72) | [
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"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(72) | [
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"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(72) | [
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] | p | So for instance, there is a norm layer which you can fuse with the previous operation. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,634 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(72) | [
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"tags",
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[
"Quantized Emdedding Models - Qdrant",
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[
"Quantized Emdedding Models - Qdrant",
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[
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] | p | So let’s say when somebody asks a question, you want that to be converted into an embedding as fast as possible and as cheaply as possible. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,639 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(73) | [
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"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
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] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(73) | [
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"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(73) | [
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[
"Quantized Emdedding Models - Qdrant",
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[
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[
"Quantized Emdedding Models - Qdrant",
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] | p | So when we do quantize it, we do quantization right now with CPUs in mind. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,647 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(74) | [
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"tags",
"tags/quantized-emdedding-models"
] | 16,648 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(74) | [
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(74) | [
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[
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"tags",
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[
"Quantized Emdedding Models - Qdrant",
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] | p | Yes, let’s do that. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,655 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(77) | [
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] | p | Dude, this has been great. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,658 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(77) | [
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"tags",
"tags/quantized-emdedding-models"
] | 16,659 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(77) | [
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] | p | All right, I will see you later. | [
"tags",
"tags/quantized-emdedding-models"
] | 16,660 |
[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(77) | [
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] | p | Thank you so much, Naran. | [
"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(77) | [
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[
"Quantized Emdedding Models - Qdrant",
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"tags",
"tags/quantized-emdedding-models"
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[
"Quantized Emdedding Models - Qdrant",
"Transcript:"
] | /tags/quantized-emdedding-models/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(77) | [
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"tags",
"tags/llm-recommendation-system"
] | 16,667 |
[
"LLM Recommendation System - Qdrant"
] | /tags/llm-recommendation-system/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(1) | [
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"tags",
"tags/llm-recommendation-system"
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[
"LLM Recommendation System - Qdrant"
] | /tags/llm-recommendation-system/ | html > body > div:nth-of-type(1) > div > section > div > div > div > div > div:nth-of-type(2) > div > div > div > div:nth-of-type(2) > article > p:nth-of-type(2) | [
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] | p | His expertise spans causal ML, XGBoost models, NLP, and generative AI, with a current focus on improving outfit recommendation systems. | [
"tags",
"tags/llm-recommendation-system"
] | 16,671 |
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