This is a merge of pre-trained language models created using
mergekit
.
One of the merge components had refusal partially ablated, then partially healed with a small merge contribution with a model tuned for narrative text completion. The other merge component was a personal preference, with refusals still present. A merge ambition was for the refund of the safety tax in the form of improved reasoning to survive merger and contribute, while avoiding the worst tendencies toward literal repetitions of passages. Sometimes model damage can be leveraged to vary text completion outputs, as damage can function as noise; it's likely not good for benchmarks, in principle, but can reduce the need for samplers that actively suppress passage repetitions.
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