131–140 of 217 posts
How well do LLMS like this work with a non-English language? Or are these open source models limited to English?
Stability has a Japanese port which is getting lots of work https://twitter.com/StabilityAI_JP/status/171699857824440759...
I just want to make an embedding between a conversation of me and my friend and simulate talking to them. Is this a hard thing to train to begin with?
If anyone knows or could help me with this, I would be very grateful!
Pardon my ignorance in advance but could it be used to "chat" with PDFs and websites? I am looking for OpenAI alternatives as I am in learning phase
Earlier quoted context omitted.
It definitely is open source even if they don’t disclose all details behind the training
The very definition of what constitutes open source is being called into question in these kinds of discussions about AI. Without the training details and the weights being made fully open it’s hard to really call something truly open, even if it happens to meet some arbitrary definition of “open source”. A good definition of “truly open” is whether the exact same results can be reproduced by someone with no extra in…
Earlier quoted context omitted.
Is it tho? It's not really open source if they don't give us the information regarding training datasets
It definitely is open source even if they don’t disclose all details behind the training
Earlier quoted context omitted.
> Have links gone out of fashion? Yes. You wrote far more words than needed to answer the comment, I did it for you instead.
One of the reasons I write so much stuff is so I can provide links to things I've written to answer relevant questions.
Earlier quoted context omitted.
You could get a facsimile to a summary for a full article or short story. Reducing an 8k token article to a summary using a completions model would cost far more. So if you need to search through collections of contracts, scientific papers, movie scripts, etc. for recommendations/clustering then bigger input sizes can do that in one shot. Think of it like skipping the square root step in Euclidean distance. Perfectly…
I think I'm missing something: like, yeah, it's vector search for bigger text chunks. But arguably vector search with bigger text chunks is _definitively_ worse -- this isn't doing summarization, just turning about 25 pages of text to 1024 floats, which you then can use cosine similarity to measure the semantic similarity to other text I'd much rather know what paragraph to look in than what 25 pages to look in
Earlier quoted context omitted.
> What is the use case for an 8k token embedding? Calculating embeddings on larger documents than smaller-window embedding models. > My (somewhat limited) experience with long context models is they aren't great for RAG. The only reason they wouldn't be great for RAG is that they aren't great at using information in their context window, which is possible (ISTR that some models have a strong recency bias within the w…
I wonder how the perfomance fair when context size is increased. Intuitively this should be higher, but some quantized models I've tested showed noticeably worst performance.