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Stephen Panaro
903 posts
@mweinbach Thought it was a cleaner summary. Possible I’ve just never looked beyond that though 🤔
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@flat i'm pretty sure anthropic doesn't hide theirs, so that feels normal
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@mweinbach Huh weird. I also got it from Opus today too but had never seen it before. Assumed it was an Anthropic issue
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@mattcassinelli @tylerangert Oh for sure some cool non-MLX stuff this year. When Google released their on-device base model + LoRA, I crossed my fingers Apple would do the same. And they did!
Am actually curious now if anyone has shipped an AFM LoRA.
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@flat @tylerangert I just mean overall, because they weren’t available to use last year, are foundational instead of frontier, and generally native-only, the entire potential of Apple Intelligence models are skipped over.
Specific ones like handwriting also enable apps like this easily now
Matthew Cassinelli@mattcassinelli
This developer just turned the iPad into Tom Riddle’s diary ‼️
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training a tiny handwriting synthesis model. this is the latest checkpoint at 44%. so cool
Tyler Angert@tylerangert
I’ve been radicalized by MLX and now need a cluster of Mac minis
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@mattcassinelli @tylerangert Having been away from MLX for a bit, what did I miss? (Or is this about non-MLX like CoreML/Foundation Models?)
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@tylerangert This year’s APIs are so cool but because they’re not frontier-level models everyone decided to not even check
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This ButterflyQuant paper looks neat, but also a little sus:
- no code
- no comparison against its closest relative (SpinQuant)
A good test project for coding agents?
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MLP from dequantized int8 with CoreML 9.x test / example source code
gist.github.com/Anemll/a9838e1…
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Incoming new coremltools looks like it has some nice bits:
- 8 bit input/output tensors (previously all 8bit compute was kept internal)
- >1 input can be enumerated shapes (👀ANE)
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Looks like it’s happening!
Stephen Panaro@flat
Wonder if we’re gonna get a new version of coremltools. Last year it dropped on Monday.
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@AaronWeiHuang 👋 Any chance y’all plan to release code for DBellQuant?
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@cloneofsimo Can use this for quantization too:
arxiv.org/abs/2506.04985
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Oh my god, with relu / relu^2 and no bias, there is further redundancy in parameterization of MLP because you can multiply one row of fc1 by k and divide same column of fc2 by k^2, and output is completely identical!
so we have *extra* n redundancy if your activations are scale equivariant...
Simo Ryu@cloneofsimo
Its nightmare to think 2 layer MLP are super redundant: even if you have a global minima, there are at least n! more of em. For n = 8k, 2 layer MLP is like 10^27800 larger in terms of hypothesis space WHO ALLOWED THIS ???
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🐙: gist.github.com/smpanaro/5d301…
📄: arxiv.org/pdf/2506.04985
(R₅ is a rotation matrix, so its transpose is its inverse and it naturally cancels out in Q@K.T)
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Turns out you don’t need R₅⁻¹ at all. 🫠 Fusing into Q and K is enough!
Cool paper from Qualcomm explains this and a few similar transforms.
No code in the paper, so gist proof👇
Stephen Panaro@flat
Liking the line of research where you multiply LLM weights by rotation matrices and the model still works. Most do it in between layers, but you can also sneak one between Q/K and RoPE. Extra parameters? None. Useful? …Maybe. Cool? I think so. (See R₅ below.)
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The python library is interesting too. “Download files”:
pypi.org/project/tamm/0…
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See for yourself:
1. Get the adapter training toolkit: #download-toolkit" target="_blank" rel="nofollow noopener">developer.apple.com/apple-intellig…
2. Clone: github.com/smpanaro/netro…
3. Edit draft.mil:
- delete all functions except the first
- rename it to: func main<ios18>(
4. Follow readme to start netron, and open the .mil
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Curious about the Apple Foundation Model architecture? I updated my netron fork to visualize the draft model*.
*they say it might differ from the real model but looks convincing to me
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btw, you can quantize the “hard-to-quantize” Llama 3.1 8B now.
(LDLQ is GPTQ)
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