Kev: Tiny Jev-like family of decision models built on top of Qwen3.5
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Kev: Tiny Jev-like family of decision models built on top of Qwen3.5
Unofficial Hacker News client; not affiliated with Y Combinator.
nico · · focus · HN ↗
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).
0x457 · · focus · HN ↗
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.
swyx · · focus · HN ↗
this misses the point of jev somewhat - the point is that this is a foundational, general purpose classifier model - see some good sources <a href="https://x.com/mparakhin/status/2101683565520199887?s=12" rel="nofollow">https://x.com/mparakhin/status/2101683565520199887?s=12
0x457 · · focus · HN ↗