This is pretty much exactly what I want for Home Assistant. I yell out, "Computer! Lights!" and it toggles the lamp in the room on or off. (I mean I can do that now, I think, but probably with a much larger model.) I haven't played with it yet, but does it ever return anything other than a tool call? What are the failure modes? What if it doesn't understand the request? Does it ever say it can't find a tool? Does it…
Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model
121–130 of 255 posts
Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model
#122From all the models that do toolcalls the only thing I am confused is why did you pick the worst? Or maybe they are only bad in agentic work it fine for one shot toolcalls?
Gemini is pretty solid for 1-shot tool call and affordable as well.
Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model
#123Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model
#124What is the use case for this?
Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model
#125Earlier quoted context omitted.
Ok, will do that now!
I know we all think of bad things when we hear "short form video" but short demos can do a LOT for any project, shows the user how its used, what it looks like, what it solves, etc all in anywhere from 15 seconds to a couple of minutes, doesn't need to be ultra fancy, screen recording is fine. :)
Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model
#126Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model
#127Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model
#128Earlier quoted context omitted.
[flagged]
A local model that can do better than Siri or Alexa as a personal or home assistant is, in my eyes, very useful. Being able to run on a phone or watch or glasses translates to me, low-powered AI, and not necessarily that I want my phone, or watch, or glasses to run things for me. My Siri use has narrowed down to just setting timers. And even then, I still have my phone call people in the middle of the night. Siri is…
Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model
#129Earlier quoted context omitted.
Can you please make your substantive points without sharp elbows? We're trying for something different here, and would appreciate it if you'd post in the intended spirit. https://news.ycombinator.com/newsguidelines.html
I’d edit it if I could, but it seems to be past the timeout. As the other poster noted, the post wasn’t meant to be read as a personal attack
Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model
#130Earlier quoted context omitted.
I guess this had always been bugging me. I get while you need activation/non-linearities, but do you really need the FFN in Transformers? People say that without it you can't do "knowledge/fact" lookups, but you still have the Value part of the attention, and if your question is "what is the capital of france" the LLM could presumably extract out "paris" from the value vector during attention computation instead of n…
> if your question is "what is the capital of france" the LLM could presumably extract out "paris" from the value vector during attention computation instead of needing the FFN for that. But how do you get 'Paris' into the value vector in that case? The value vector is just the result of a matrix multiplication, and without a nonlinearity it can't perform a data-dependent transformation. Attention still acts as a non…
Ok wait I think I see what you mean. Although maybe it's not getting paris _into_ the value vector that's hard, but isolating the residual stream to _only_ that instead of things like other capitals.
So as a naive example maybe at the very first layer consuming your tokens: Q{France} would have high inner product with K{capital} and so our residual would now mostly contain V{capital}, which maybe contains embeddings of all the capitals of all countries. You need some way to filter out all the other stuff, but can't do that without a FFN + activation.
Just throwing in a relu by itself won't help since that would still work on all the elements uniformly, you need some way to put weight on "paris" while suppressing the others, i.e. mixing within the residual stream itself.
Although maybe if you really stretch it, somewhere in a deeper layer you could have 1-hot encoded values with a "gain" coefficient so that when you do the residual addition it's something like {, , } + 10000*{, , } and then if you softmax that you get something with most of its mass on "Paris". But it seems like this would not be practical, or it's just shifting the issue to how that the right 1-hot vector is chosen