Show HN: Needle2: 14MB agentic LLM for phones, wearables, smart home and robots

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 layer in that stack.

With that being said, the web demo is not particularly impressive. It really doesn't like anything I throw at it. I'm fine with accepting that fine-tuning is the solution to this, but I wonder if there's anything to gain from a bigger model? I know it's completely counter to the whole point of this, but a 14MB binary using 28MB of RAM seems unnecessarily small and pretty arbitrary.

Like, what does a 28MB binary get you? Or a 140MB binary? Or a 1.4MB binary? I'm guessing the choice of 14MB came from minimizing the size as much as possible while meeting certain requirements/performance expectations, but even a Pi 5 has plenty more room to spare. Curious if there's a good explanation for this (which I may have missed in my skim of the post).

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 also appears to have no concept of what a door or light actually is, whenever the query diverges from "Lock door X" or "Turn on light X", it tries to shoehorn whatever additional context is given into the device name:

Query: "Lock out the vacuum salesman at the front door" Agent tries to lock "front door vacuum salesman"

"The way you talk really makes me appreciate silence" is classified as "positive" with 82% confidence.

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?

Funny result from the web demo. I'm well aware that it's an extremely small and, well, stupid, model, but even so:

Query: HN

Result:

{ "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 expect it to at least ignore (call no tools) for the queries that it doesn't understand. And it seems like it does do that, just not consistently.

Congratulations. 28MB is impressive, I've not played around with actual queries/outputs.

I'm wondering what is the overall thesis/plan here and where exactly the innovation lies? Would love if you can throw light on below,

  - If I understand, this is complete stack of a custom architecture (attention only transformers), custom quantisation format and a runtime engine all packaged together?
  - How do you differentiate / compete against LiteRT (former TensorFlowLite) and Lite RT LM? Google is heavily investing in this ecosystem because Android is where they have distribution moat. Wouldn't it be easier for me as a developer to build on top of LiteRT since it is relatively open ecosystem and I can pack large number of open models from HF directly?
  - What exact challenges you saw with TFLite, TVM etc that prompted this effort ?
  - What will be the pricing model like?

That's really cool - I was already thinking of compressing `functiongemma-270m-it` down to 1-2 bits so it would work flawlessly in the browser. Your `Fine-tuning` feature is even much more convenient.

It seems to fail. I sent the prompt:

“ 5° warmer”

And it said:

“ setting the temperature to 5°F”

Could someone please share how such open source micro-LLMs might have been created?

Do the creators take something like DeepSeek, and then delete most of the neurons to whittle down the size?

Nice. I used Needle as a router in https://rcarmo.github.io/projects/memento/, need to take a look at this

> Turn the lights down low in the bedroom

Sets lights to 30% but also off

> Turn the lights low in the bedroom

Sets lights to on

This is a cool idea but I think humans assume more than 14MB of intelligence. This is like the unhelpful guard in the swamp castle of Monty Python's Holy Grail

I tested with

  import needle

  @needle.tool
  def add(a: int, b: int):
      "Add two numbers."
      return a + b

  agent = needle.Needle(tools=[add])
  print(agent.run("calculate 1 + 1?")["reasoning"])

python main.py No calculator or math tool available.

conclusion: completly useless

This is cool!

While most of the industry focuses on the frontier of “intelligence” (function), a release like this represents the frontier of the other end of the spectrum (form).

Both are important if we ever want to see “Opus-level” capability running locally on commodity machines in the future.

Its pretty significant you've got this working locally in wasm. Very cool.

Re: robotics: I'm unsure how this could be helpful.

It fails a pretty simple navigation prompt.

X0: (0.0, 0.0). Object bounding box: [1.0, 1.0, 2.0, 2.0]. navigate to (3.0,3.0)

I changed it to "call path planner to navigate: a_star(x0, xf, obs)"

Another fail.

My intuition tells me micro llms will/are important for robotics. I just can't grok it. Can someone without control theory experience give me a good example?

Probably at the planning level of the navigation stack. That's where I see reasoning being helpful. Lower than that...idk

Give me an example of a robotics prompt that seems useful and I'll give you an example why we don't need LLMs to be a tracking controller, etc.

Curious, why did you go down to 2bit rather than 4 bits? 4bit with folding the layers should arrive at the same size, but with better quality?

This is really cool, I'm curious how much knowledge can their be in smaller models? It seems the current trade off is you need sizeably larger models for more performance but I'm curious if in your work how far this is true, as edge ai is really what needs to get better before physical ai can take off (my two cents).

Makes me think of the demo from some time ago where someone got a ~29M parameter model running on an esp32. I wonder what kind of throughput this could get if a handful of esp32s were strung together...

Edit: I have a pile of d1 minis, but not much time.

What about use case for replacing regex? I.e "random formatted title.extension" - extract the title or some tag or something for more dynamic string manipulation for pulling structured data out of strings efficiently and more simply than regex provides?

Congrats on this release. The WASM implementation is really cool. This is a surprisingly good fit for a lot of cases, and I totally want to try turning this into a helper assistant for an application.

Please, though, take a pass at humanizing the text on the page. It's Clauded up all over and makes it hard to read.

> turn on the tv

{ "function_calls": [ { "name": "lock_door", "arguments": { "door": "tv" } } ], "confidence": 0.0158 }

Very interesting, seems confidence is 0 when tool calls are right?

I wonder if there's any way to get this to plan out a dag of tool calls? i.e. use the results from earlier calls as the parameters to later ones? I tried introducing a stack based system, but gave up pretty quickly.

Was really cool to see yous use Engrams to cut down compute!

Given its basically an O(1) lookup with disk space being the main constraint, I was curious if you've tried ablating engram layers and sizes across your setup?

Also, why mHC over attention residuals?

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

How many languages does it supports in such a small size?

Looking forward to npm version of needle-rs supporting v2. I added needle support for tool use in my side project.

Any instructions available for running this on an ESP32-S3 or P4 like the site says?

I’d be interested in attempting to run this in a network- enabled 32MiB RAM microVM.

This is so cool! Congrats to the team!

these micro LLMs could be a game changer for hearing aids

so many interesting lowfi hardware use cases

"make it as dark as possible"

  {
    "function_calls": [
      {
        "name": "set_thermostat",
        "arguments": {
          "temperature": 72,
          "mode": "cool",
          "room": "living room"
        }
      }
    ],
    "reasoning": "'as dark as possible' -> set_thermostat to warm; 'dark' implies higher temperature; 'cool' mode for darkness.",
    "confidence": 0
  }

... maybe this counts as dark humor at least.

Since it seems limited to matching a few templates and otherwise falling flat on its face, I wonder how 14MB of regexes would fare in its stead. Normally you wouldn't want to parse arbitrary natural language input with regex because of how tedious and brittle it would be, but for the tedium we have LLMs and this alternative isn't exactly robust either.

Is there a prebuilt apk somewhere?

Any plan to release on ollama?

Great work! Keep it up

If you want Needle2 to rget lots of testing, become well known, etc - make a Home Assistant plugin.

This is very interesting! I'm going to spend some time with this. This is really the only class of LLM I'm interested in at all. I sincerely hope on-device takes over and everyone looses their asses on these data centers.

What is the difference between this and random sentence generator?

Utterly Fascinating!

For the longest time, I conceptualized LLM's as Text Input -> Text Output transformers, then later as Text Input -> Video Output transformers. Later still I conceptualized them (if they were general purpose) as Any Format Input -> Any Format Output transformers...

The idea of a smaller parameter model runable on smaller/slower/less complex hardware (computers with no GPU, slower CPU's, less memory, aka "Edge Devices") trained for Text Input -> JSON Output (used for tool calls, etc.) I could honestly not conceptualize before seeing the demo on the web page...

But now that I've seen it and conceptualized it -- I'd have to say: "Yes, there's definitely a huge niche, a huge market for this, directly between the non-LLM driven tools and software and SaaS's of yesteryear, and the latest, cutting edge Frontier AI models of today!"

So, I like Needle a lot!

I like Needle a lot, and I love the idea of any tiny resource-thrifty LLM that can run on older hardware, that outputs only JSON!

I can see a huge market for it!

what does the first L mean in LLM?

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The learned confidence gate is the crucial piece for a 14MB action model. On ambiguous requests such as the HN example, what calibration target decides between abstaining locally and escalating to the cloud?