Show HN: Gemma 4 Multimodal Fine-Tuner for Apple Silicon

122 pointsposted 6 hours ago
by MediaSquirrel
(github.com)

16 Comments

mandeepj

3 minutes ago

> I had 15,000 hours of audio data

do you really need that much data for fine-tuning?

LuxBennu

5 hours ago

I run whisper large-v3 on an m2 max 96gb and even with just inference the memory gets tight on longer audio, can only imagine what fine-tuning looks like. Does the 64gb vs 96gb make a meaningful difference for gemma 4 fine-tuning or does it just push the oom wall back a bit? Been wanting to try local fine-tuning on apple silicon but the tooling gap has kept me on inference only so far.

weitendorf

an hour ago

Hey I was literally just working on this today (I was racing ahead on an audio FT myself but OP beat me by a few hours). For audio inference definitely try running your input through VAD first to drop junk data and if necessary, as one of several preprocessing steps before sending the audio to the large model. You can check out how I did it here: https://github.com/accretional/vad/blob/main/pkg/vad/vad.go

I was using https://huggingface.co/onnx-community/pyannote-segmentation-... because with ONNX, I could run it on Intel servers with vectorized instructions, locally on my Mac, AND in-browser with transformers.js

VAD is absurdly time-effective (I think like O(10s) to segment 1hr of audio or something) and reduces the false positive rate/cost of transcription and multimodal inference since you can just pass small bits of segmented audio into another model specializing in that, then encode it as text before passing it to the expensive model.

MediaSquirrel

an hour ago

Great minds think alike!

Also, I had a huge head start, as I spent a month or two working on this in September 2025, shelved it and dusted it back off this weekend.

weitendorf

an hour ago

Excellent work still, your repo is much more robust and fleshed out and I am just beelining straight to audio LoRa not really knowing what I'm doing, as this is my first time attempting a ~real ML training project.

I think in https://github.com/mattmireles/gemma-tuner-multimodal/blob/m... and https://github.com/mattmireles/gemma-tuner-multimodal/blob/m... and https://github.com/mattmireles/gemma-tuner-multimodal/blob/m... you have a superset of the various cludges I have in my finetuning repo, I'm going to study this and do what I can to learn from it. Really appreciate you sharing it here!

Definitely interested in swapping notes if you are though. Probably the biggest thing that came out of this exercise for us was realizing that Apple actually has some really powerful local inference/data processing tools available locally, they just are much more marketed towards application developers so a lot of them fly under the radar.

We just published https://github.com/accretional/macos-vision to make it easy for anybody to use Apple's local OCR, image segmentation, foreground-masking, facial analysis, classification, and video tracking functionality accessible via CLI and hopefully more commonly in ML and data workloads. Hopefully you or someone else can get some use of it. I definitely will from yours!

MediaSquirrel

an hour ago

re: Whisper v3 -- how is this possible? Whisper has a 30s context window. You have to chunk it.

MediaSquirrel

5 hours ago

Memory usage increases quadratically with sequence length. Therefore, using shorter sequences during fine-tuning can prevent memory explosions. On my 64GB RAM machine, I'm limited to input sequences of about 2,000 tokens, considering my average output for the fine-tuning task is around 1,000 tokens (~3k tokens total).

LuxBennu

3 hours ago

Ah that makes sense, quadratic scaling is brutal. So with 96gb i'd probably get somewhere around 4-5k total sequence length before hitting the wall, which is still pretty limiting for anything multimodal. Do you do any gradient checkpointing or is that not worth the speed tradeoff at these sizes?

MediaSquirrel

an hour ago

Haven’t tried yet. That’s on the do list. But good suggestion.

conception

2 hours ago

I’m pretty excited about the edge gallery ios app with gemma 4 on it but it seems like they hobbled it, not giving access to intents and you have to write custom plugins for web search, etc. Does anyone have a favorite way to run these usefully? ChatMCP works pretty well but only supports models via api.

craze3

5 hours ago

Nice! I've been wanting to try local audio fine-tuning. Hopefully it works with music vocals too

neonstatic

28 minutes ago

Just a heads up, that I found NVIDIA Parakeet to be way better than Whisper - faster, uses less compute, the output is better, and there are more options for the output. I am using parakeet-mlx from the command line. Check it out!

dsabanin

6 hours ago

Thanks for doing this. Looks interesting, I'm going to check it out soon.

yousifa

5 hours ago

This is super cool, will definitely try it out! Nice work