Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots
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Specifying units seems to be unreliable; I tried adding a description to the set_thermostat temperature: "temperature": { "type": "number", "description": "degrees Fahrenheit" }, Set the living room temperature to 70 degrees Celsius { "function_calls": [ { "name": "set_thermostat", "arguments": { "room": "living room", "temperature": 70, "mode"…
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This 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…
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It's definitely cool that you can get any reasoning whatsoever out of such a small model. That said, its reasoning is "interesting":Query: "Make the living room dark" Agent: "User wants lights on in living room. 'dark' implies dim. Room 'living room', action 'on'." (And on every test I did, it just completely ignored the "brightness" parameter)It…
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Can you share more about the architectural/design tradeoffs you considered or decided upon? Particularly for me, why is a model that is intended mainly to just make tool calls and marshal the results back focusing on speed? Speed as an inherent result of small size, I get, but speed as a design focus confuses me because it’s simply not going to be…
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Funny result from the web demo. I'm well aware that it's an extremely small and, well, stupid, model, but even so:Query: HNResult:{ "function_calls": [ { "name": "lock_door", "arguments": { "door": "front door" } } ], "reasoning": "User wants to lock the door. No specific door mentioned, so use 'front door' as default.", "confidence": 0 }I'd…
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When I asked it to call my cable company I got this response. I am wondering how this response comes out… why does it say lights twice and not mention door? LLMs are a mystery to me but I suppose this reasoning is at the heart of the model and it’s only 40% confident in it { "function_calls": [], "reasoning": "No tool for making phone calls or…
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My 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?
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I'm quite impressed by the results of the web demo, especially given its size and the precision with which it uses the three available tools (tested with German commands). I could imagine that this LLM would fit well into a setup with multiple micro-sized LLMs for different purposes; so 14 MB for precise tool invocation is a reasonable memory…
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I don't quite get what this can be used for.It cannot be used for direct customer interaction, it's simply too limited.Nobody wants to say "lock the front door", they would just do itAnd even quite direct commands like "tighten security" are not understood by the model
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> Like, what does a 28MB binary get you?For one thing, on beefy-enough recent CPUs, you could keep the weights hot in the L2 cache of a single CPU core. (Which is clearly not the use-case, but might be interesting to those looking for extreme TPS numbers. Or perhaps for efficient training!)