Post
301
I've observed that the layers targeted in various abliteration notebooks (e.g., https://colab.research.google.com/drive/1VYm3hOcvCpbGiqKZb141gJwjdmmCcVpR?usp=sharing ) appear to be arbitrary, reflecting probable brute-force exploration. This doesn't need to be the case.
Taking a cue from the paper "The Unreasonable Ineffectiveness of the Deeper Layers" ( https://arxiv.org/abs/2403.17887 ) and PruneMe (https://github.com/arcee-ai/PruneMe), it seems reasonable to target deeper layers identified as more redundant given measured similarity across layers, as the result should be less damaging to models, reducing the need for subsequent fine-tuning. Intuitively, one should expect the resulting intervention layers to be deep but not final. The only uncertainty is if the redundancy successfully encodes refusals, something which is almost certainly model-dependent. This approach only requires the redundancy to be computed once per model, and the result used as a starting point for which layer range to restrict intervention to.
Taking a cue from the paper "The Unreasonable Ineffectiveness of the Deeper Layers" ( https://arxiv.org/abs/2403.17887 ) and PruneMe (https://github.com/arcee-ai/PruneMe), it seems reasonable to target deeper layers identified as more redundant given measured similarity across layers, as the result should be less damaging to models, reducing the need for subsequent fine-tuning. Intuitively, one should expect the resulting intervention layers to be deep but not final. The only uncertainty is if the redundancy successfully encodes refusals, something which is almost certainly model-dependent. This approach only requires the redundancy to be computed once per model, and the result used as a starting point for which layer range to restrict intervention to.