Show HN: Fine-tuned Llama 3.2 3B to match 70B models for local transcripts

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Cost is a big factor - i really want to make models that can run on average CPU only machines so most of the world can benefit, rather than needing expensive GPUs or an internet connection + subscriptions. Another big factor is privacy (you don't need to trust a 3rd party with your inputs).

As for the hyperparameters, pure bruteforce trial and error. It feels more like a dark art than a science. You roll the dice and then start tweaking things until the loss looks like it's dropping nicely and consistently, and the checkpoints are starting to output things resembling what we want. I sometimes do inference using checkpoints just to get a feel of if the model is learning (regardless of loss)

What hardware are you running? Parakeet runs on nvidia and Mac and it’s way faster than Whisper. And I’ve had issues with training Qwen3 (and even Qwen2.5 but I think I was masking stop tokens wrong). I’ve had success with Gemma 3 though, and they have some really small models (270m and 1b). Maybe 270m for just transcript cleaning? I wonder if the 1b model can handle the transcript analysis…