This is a merge of pre-trained language models created using
mergekit.
Below, we explore negative weight merger and propose Orthogonalized Vector Adaptation, or OVA.
Task arithmetic was used to isolate the intervention vector that was applied to create
Instruct-abliterated-v3 by applying a negative weight of -1.0 to remove the baseline
Instruct weights.
The resulting weights comprise an Orthogonalized Vector Adaptation that can subsequently be applied to the base Instruct model using task arithmetic merger, and can in principle be applied similarly via merger to other models derived from fine-tuning the Instruct model.
Built with Meta Llama 3.
This model was merged using the
task arithmetic merge method using
failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 as a base.
1base_model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
2dtype: bfloat16
3merge_method: task_arithmetic
4parameters:
5 normalize: false
6slices:
7- sources:
8 - layer_range: [0, 32]
9 model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
10 - layer_range: [0, 32]
11 model: meta-llama/Meta-Llama-3-8B-Instruct
12 parameters:
13 weight: -1.0
14