Naïve and clumsy question: how would you pair this with speech-text-speech stuff, wake words etc.? Are there good examples of this for a Pi 5? The demo is super — I'm just having trouble seeing the whole picture for e.g. a screenless device. ETA: pun not intended
Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots
141–150 of 188 posts
Re: Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots
#142Re: Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots
#143“ 5° warmer”
And it said:
“ setting the temperature to 5°F”
Re: Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots
#144Re: Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots
#145Earlier quoted context omitted.
So, its not a general language model, focused on tool call strictly for tiny edge-devices. There are solutions everywhere for high-capacity devices, Needle is for sub-$200 devices.
It seems to me that the model struggles to have enough general intelligence, knowledge, or reasoning capacity for arbitrary prompted tool calling. At this size, not surprising. I am VERY interested in seeing how it could perform with some fine-tuning for a specific family of tools/tasks. That would be a great addition to the demo.
Re: Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots
#146My first query: > Make it a little warmer in here. The reply: > "name": "set_thermostat", > "arguments": { > "temperature": 65, > "mode": "cool", > ... > "reasoning": "'warmer' implies need for cooling; set_thermostat with temperature 65 (typical warmth) and mode 'cool'.", Maybe I'm doing it wrong?
It's not a conversational model. It's meant as a local tool calling model.
"Make it warmer"
tool that cools room called
Re: Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots
#147This is cool. I definitely think the "micro" sized LLM space is underappreciated, so it's always good to see work like this. I foresee a paradigm in some contexts where you have a hierarchy of LLMs, with more competent models actively training smaller models to solve specific tasks very efficiently, and something like this could be the smallest layer in that stack. With that being said, the web demo is not particular…
Re: Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots
#148Earlier quoted context omitted.
I think the test above is about tool calling... That's how I read it. The issue here is known as "out of distribution detection" in the old-timey classification world. I am not sure how a micro model will fundamentally solve it. Would love to understand what dannyw and team did there?
How did you draw an association between dannyw and Cactus? There are no 'Danny's on the list of GH contributors nor is there one named in the paper. Just curious.