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Comment by 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.

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0x457

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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.

    • nico  3 hours ago

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

      Amazing, thank you for sharing your setup. Very cool applications