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Cengiz Poyraz

CengizPoyraz
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reacted to etemiz's post with 🔥 29 days ago
evolution has started with lineages=base 3.8, some abliterations, ostrich surgeries behavior steering experiments are somewhat successful. we can play with feelings of models (make it like some behavior or hate some behavior). but this is not that effective. we found that when abliterated models are more eager to adopt a behavior. dataset that has contemplations is in effect and evolving models.. two orthogonal stages of evals: 1. quickly check the evolved model in terms of mmlu, long context (needle in haystack), basic chatting capabilities, </think> tag closing correctly, and shorter version alignment using log probabilities of first tokens 2. our regular alignment eval that has q&a's in json formats (for parsing better) another eval in progress that will mathematically check overfitting instead of inference. instead of using GPU and time consuming inference, can we do math tricks using CPU to approximate detection of degenerate / repeating / broken / high cliff models? GLM 5.2 said no but Qwen 3.8 Max said lets do it. i guess we are doing it.
reacted to etemiz's post with 🔥 29 days ago
Qwen 3.8 fine tuning going well All these dots are a lineage in the evolution. I am playing safer this time, measuring lots of things like Abliteration, MMLU, MMLU-Pro, ARC-Challenge, .. while doing alignment fine tuning. In the end I want the model to keep existing capabilities.
reacted to etemiz's post with 🔥 29 days ago
fine tuning going well, without breaking the model
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