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Comment by nico

7 hours ago

If you only need classification, and you can provide some training data, you can ask Codex/Claude to build an embeddings + logistic classifier model for you

For emails, I get 95% accuracy with this method, with only 50-100 examples for training

Training the model takes less than 5 minutes on a CPU

The resulting model is <1MB, and inference is sub 100ms

Some other cool things about this approach:

* the model doesn’t train on some “ideal” or general classification, instead it learns your preferences

* the model runs on pretty much any mobile device and can be retrained online on the device

* privacy, the whole training and inference is 100% local, no data goes anywhere (except whatever you feed codex/claude while building the model)

Note: to do a more general test, I made a classifier for the Banking77 dataset. The model is <10MB, trains in <30s on CPU and gets 94.5% accuracy, which puts it in the top 5?models by accuracy for that set (the best one is at 94.86%, but it’s 350MB in size and takes hours to train on a GPU).

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nico

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0x457  6 hours ago

I built a whole thing that collects data, trains classifiers, exports models and dataset just for that. Claude writes me a terraform file that contains shape of the classifier and dataset. For images it can create datasets based of another dataset (crop this region from images that have these labels).

Originally it was so I can label data to fine-tune a VLM, but now a few tiny classifiers that run in milliseconds on cpu.

Now its collecting data to make a domain specific BERT and do what Jev does.

  • nico  5 hours ago

    Very cool. What kinda of classifications are you running? How big are the models/training sets?

    Also curious about if you plan on doing some sort of routing for the requests. Like detecting the type of task to decide which model to route the request to

    • 0x457  3 hours ago

      Some classifiers are tiny - like 2k params, maybe even less.

      This whole thing started because I wanted something to help me play Dune Imperium. Even relatively large models with vision encoders couldn't reliably extract the full state of the board. Now that I have ~2k labeled screenshots, I want to train heads on top of SigLIP2 to extract all of that data in one go.

      That's how it started. Now the thing supports multiple kinds of datasets:

        Images - currently the Dune Imperium and Bolatro screenshots, with SigLIP2 heads being the next step.

  STT - my self-hosted Linux dictation tool feeds this dataset. I run Nemotron ASR tuned for my voice.

  TTS - for Piper TTS, trained to speak like SHODAN. Trained from data generated by Qwen3-tts + original video games files.

  Text pairs - for a 1.2B model that converts normal text into "what would SHODAN say?"


  FastApply - a Qwen3.5-4B LoRA adapter for doing fast edits.

  Chat threads - all agent/chat threads get saved too, so eventually I can turn the useful ones into a dataset and train a LoRA for a really good Rust-specialized version of Qwen3.8-27B.

  Tool calls (extracted from chat threads) - this is where I want something Jev-like, mainly to add an auto-approval mode to my agent harness.

      A model router isn't planned because I'm trying to gear everything toward self-hosting, and there just isn't that much to route between. I’ll probably build something Jev-like for smart-home control, though.

      The FastApply dataset is already ~20k entries, with the majority of outputs being 8k–16k tokens. The STT dataset is roughly 30 hours and growing.

      Basically, the whole thing has turned into a Collect -> Distill -> Train pipeline for whatever I happen to need.

      1 reply →

constantlm  3 hours ago

I did this yesterday and it works incredibly well. I finetuned ModernBERT to classify documents. With zeroshot it achieved around ~30% accuracy, which jumped to 98.2% with finetuning, and latency of around 150ms on my Macbook. Just incredible!

rgbrgb  7 hours ago

love this idea. did you try comparing to jev?

  • nico  6 hours ago

    Yes, I ran some benchmarks. This architecture seems to match or beat Jev and Laya in all basic classification tasks (datasets tested: AG News, Emotion, MASSIVE Intent, Banking77)

    The type of task in which it does really well, especially against Laya, is classification with >50 classes

    But this architecture has no “reasoning”, so it performs rather poorly on tasks that require it, like the ones from the XLNI dataset (Jev/Laya do a lot better on this one)

    For the latter cases, you could probably enhance the architecture with a lightweight LLM, something like a Gemma model. Or even some basic MLP

samuel  6 hours ago

Do you realize people is using LLM's as classifiers, right? For lots of companies and developers reaching an API is feasible, while running a training pipeline, no matter how simple, is not. I know that they should still be gathering data for evaluation and they could use it to train a model instead. But they won't do it, for a variety of reasons.

This is the same route but WAAAY faster and cheaper. And you can modify it like you do with code or prompts. It's really appealing, TBH.