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grimjimΒ 
posted an update 4 days ago
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118
I tinkered a bit with refusal ablation after seeing this article that proposed using optimal transport.
https://arxiv.org/abs/2603.04355
Title: Efficient Refusal Ablation in LLM through Optimal Transport
Authors: Geraldin Nanfack, Eugene Belilovsky, Elvis Dohmatob

While closed Bures-Wasserstein sounded cool, the use of top-K at low K implied that the approach likely wouldn't capture refusal in detail, and increasing KL divergence with greater K suggested that the approach was too damaging for latent reasoning. I tried it out, and the results were disappointing.
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mahwizzzzΒ 
posted an update 9 days ago
mahwizzzzΒ 
posted an update 2 months ago
grimjimΒ 
posted an update 2 months ago
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448
I think it's clear in retrospect that "frankenmerges", which repeated blocks of layers, amounted to a crude approximation of looped transformers architecture, hence them able to work at all instead of just breaking. They lucked out due to much of the signal passing through residual streams being preserved and only modulated along the way. That said, not all models are suited for this. Models which feature ever-increasing magnitudes as inference progressess through layers risk exploding precision limits.
mahwizzzzΒ 
posted an update 3 months ago
eienmojikiΒ 
posted an update 5 months ago
dippatel1994Β 
posted an update 5 months ago
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1,149
To make revising LLM architectures and training methods faster, I created a deck of 180 visual flashcards. It started as a personal hobby, but slowly became cheat code for reviewing LLM concepts before technical interviews. People love it!

Swipe through these samples, and if you want to grab the full set or follow the project, the repo is here: https://github.com/llmsresearch/llm-flashcards.
mahwizzzzΒ 
posted an update 5 months ago
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407
Released lafzyn , built over Qwen, an Urdu language model that converts Urdu text into IPA phonetic transcription, with GGUF builds for local inference.

Release contents:
- mahwizzzz/lafzyn: full weights
- mahwizzzz/lafzyn-gguf: quantized builds

Try it out πŸ€—
Demo: https://huggingface.co/spaces/mahwizzzz/Lafzyn