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Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model

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Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model

#121
post #53

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…

Hmm, I wonder if I can run this on my MyCroft II (now NeonOS) open source AI device...

Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model

#122
post #64

From 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.

My general understanding of the concenus on most models these days is that people consider google models to be some of the worst at tool calling, so certainly an interesting choice. Did you do any evals on this?

Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model

#124

What is the use case for this?

Something like this together with MCP can replace APIs for 3rd party integrations. You just give it instructions to "post a message in slack" and provide it slack MCP tools and it figures out the rest on its own. No need to read up on slack API docs or worry about breaking changes.

Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model

#125

Earlier 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. :)

One of the most important things missing from too many projects. Even fifteen seconds can often help significantly.

Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model

#128
post #119

Earlier 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…

OK.. but what would you have all this "automation" actually do? What is Siri failing to do that you want it to do? How would customizing an assistant (for whatever definition) help?

Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model

#129
post #115
post #108

Earlier 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

I've reopened it for editing if you want to (it's totally fine either way - we just care about fixing things going forward)

Re: Show HN: Needle: We Distilled Gemini Tool Calling into a 26M Model

#130

Earlier 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…

> But how do you get 'Paris' into the value vector in that case?

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

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