jn2clark

7 ms·

1.
Celeris-1: A diffusion LLM benchmarked at 2,082 output tokens/s(artificialanalysis.ai)
11 points by jn2clark 1mo ago|1 comments
2.
by jn2clark 1y ago
How does it compare to previous work on learning to learn? I don't see it referenced https://arxiv.org/abs/1606.04474
3.
Ask HN: What are the best options for transpiling using LLM's?
1 points by jn2clark 2y ago|0 comments
4.
The Impacts of Data, Ordering, and Intrinsic Dimensionality on Recall in HNSW(arxiv.org)
3 points by jn2clark 2y ago|0 comments
5.
by jn2clark 2y ago
With binary representations you still get 2^D possible configurations so its entirely possible from a representation perspective. The main issue (I think at least) is around determining the similarity. Hamming distance gives an output space
6.
by jn2clark 2y ago
That's a great question. I think regimes like that could offer better trade-offs of memory/latency/retrieval performance, although I don't know what they are right now. It also assumes that going to the larger dimensions
7.
by jn2clark 2y ago
I would love an LLM agent that could generate small api examples (reliably) from a repo like this for the various different models and ways to use them.
8.
by jn2clark 2y ago
What is accuracy in this case? is it meant to be recall or is it some evaluation metric?
9.
by jn2clark 3y ago
We (Marqo) are doing a lot on 1 and 2. There is a huge amount to be done on the ML side of vector search and we are investing heavily in it. I think it has not quite sunk in that vector search systems are ML systems and everything that come
10.
by jn2clark 3y ago
Take a look here https://github.com/marqo-ai/local-image-search-demo . It is based on https://github.com/marqo-ai/marqo . We do a lot of image search applications. Feel free to reach out if you have
11.
by jn2clark 3y ago
Can anyone comment on an open source multi-modal LLM that can produce structured outputs based on an image? I have not found a good open source one yet (this included), seems to be only closed source that can do this reliably well. Any sugg
12.
by jn2clark 3y ago
Fair enough, apologies for the confusion!
13.
by jn2clark 3y ago
That sounds much longer than it should. I am not sure on your exact use-case but I would encourage you to check out Marqo ( https://github.com/marqo-ai/marqo - disclaimer, I am a co-founder). All inference and orchestra
14.
by jn2clark 3y ago
Try this https://github.com/marqo-ai/marqo which handles all the chunking for you (and is configurable). Also handles chunking of images in an analogous way. This enables highlighting in longer docs and also for images
15.
by jn2clark 3y ago
As others have correctly pointed out, to make a vector search or recommendation application requires a lot more than similarity alone. We have seen the HNSW become commoditised and the real value lies elsewhere. Just because a database has
16.
by jn2clark 3y ago
Thanks for the feedback and questions - really appreciate it.
17.
by jn2clark 3y ago
At the moment you would need to do this yourself. It would be possible to have additional preprocessing to accommodate this though. Feel free to add a feature request here https://github.com/marqo-ai/marqo/issues .
18.
by jn2clark 3y ago
Regarding metric and dimension - it is really problem dependent as is throughput. Recall and latency numbers reported in benchmarks are typically on very well curated and structured datasets and average across all queries. Recall is not jus
19.
by jn2clark 3y ago
I think it depends a bit on the definition of search here. It might satisfy a literal definition of search but not search as users would expect - which I think is the important point. IMHO vector similarity and vector search are conflated t
20.
by jn2clark 3y ago
Good question! At the moment if you have the abstraction of data -> model -> vector then it is amenable to searching like this. It will depend a bit on the use case though.
21.
Show HN: Marqo – Vectorless Vector Search(github.com)
62 points by jn2clark 3y ago|15 comments
22.
by jn2clark 3y ago
In the quest for ultimate speed, I started developing a vector database in assembly using gpt4 as a side project https://github.com/jn2clark/GPT4Memory .
23.
by jn2clark 3y ago
If anyone is looking for a vector search engine, see here https://github.com/marqo-ai/marqo . Has additional functionality to make vector search much easier.
24.
by jn2clark 3y ago
This article also has some other methods for hallucination and reference checking by using cross-encoders https://github.com/marqo-ai/marqo/blob/mainline/examples/GPT... . It is not perfect but can b
25.
by jn2clark 3y ago
Not sure which one, but if you are after a vector search engine (not just a database) then I can recommend this https://github.com/marqo-ai/marqo . Includes inference, transformations, schema's, multi-modal search,
26.
by jn2clark 3y ago
See here https://github.com/marqo-ai/marqo/blob/mainline/examples/GPT... and https://github.com/marqo-ai/marqo/blob/mainline/examples/Spe... . Multiple examp
27.
by jn2clark 3y ago
Not sure on your interest/use case but something that is designed for "documents in" -> "documents out" is here https://github.com/marqo-ai/marqo . It does retrieval using embeddings and comb
28.
by jn2clark 3y ago
Plenty of good open-source options https://github.com/marqo-ai/marqo/blob/mainline/examples/GPT... . LLM choice is a bit harder but the composability of it all lets you easily choose alternatives.
29.
Context is all you need(twitter.com)
6 points by jn2clark 3y ago|3 comments
30.
by jn2clark 3y ago
I think you can and it has some benefits. One interesting thing that can help is to store representations from transformations over the document and then "fuse" the vectors (i.e. average them) at indexing time. You are effectively

More ›