Readit NewsI do recommend using https://github.com/huggingface/text-embeddings-inference for fast inference.
Which model, inference software and hardware are you running it on?
The 30BA3B variant flies on any GPU.
We essentially use web components as a templating language to dynamically generate a GraphQL query to Shopify. Then render the data as text nodes inside the web components. This is powerful because the components don't include shadow roots. So you can come with your own HTML and CSS.
Most web component libraries are opinionated about design, and give you many CSS custom properties or CSS parts to customize. We tried really hard to invert that, and instead give you the design control. Most of our web components just produce a text node, with no shadow root!
There's a few exceptions, like the cart for example, where it's easier to just have an out of the box component that does it all for you `<shopify-cart>`. Though...you can actually build the entire cart component with the lower level primitives!
Will build a quick poc integration. How can I contact you with feedback?
I thought maybe there's types of metal and punk that I don't know about, but Wikipedia, LLMs and guitar tab sites all agree with me. Punk and metal is overwhelmingly power chords, so I don't see how the data comparing chord types can be correct.
I assume that the analysis is simply counting every song chords, so a unknown band you've never heard about has the same impact as The Ramones.
I'd like to see the same graph weighted by band popularity using either YouTube or Spotify data.
Looks like I'll stay on [bge-m3](https://huggingface.co/BAAI/bge-m3)
Pix was such a game changer. It is perfect.
My use case is basically a recommendation engine, where retrieve a list of similar forum topics based on the current read one. As with dynamic user generated content, it can vary from 10 to 100k tokens. Ideally I would generate embeddings from an LLM generated summary, but that would increase inference costs considerably at the scale I'm applying it.
Having a larger possible context out of the box just made a simple swap of embeddeding models increase quality of recommendations greatly.