This is quantized version of
grimjim/Llama-3-Perky-Pat-Instruct-8B created using llama.cpp
Below, we explore negative weight merger, and propose Orthogonalized Vector Adaptation, or OVA.
This is a merge of pre-trained language models created using
mergekit.
Task arithmetic was used to invert the intervention vector that was applied in
MopeyMule, via application of negative weight -1.0. The combination of model weights
(Instruct - MopeyMule) comprises an Orthogonalized Vector Adaptation that can subsequently be applied to the base
Instruct model, and could in principle be applied to other models derived from fine-tuning the Instruct model.
This model is meant to continue exploration of behavioral changes that can be achieved via orthogonalized steering. The result appears to be more enthusiastic and lengthy responses in chat, though it is also clear that the merged model has some unhealed damage.
Built with Meta Llama 3.
This model was merged using the
task arithmetic merge method using
meta-llama/Meta-Llama-3-8B-Instruct as a base.
1base_model: meta-llama/Meta-Llama-3-8B-Instruct
2dtype: bfloat16
3merge_method: task_arithmetic
4parameters:
5 normalize: false
6slices:
7- sources:
8 - layer_range: [0, 32]
9 model: meta-llama/Meta-Llama-3-8B-Instruct
10 - layer_range: [0, 32]
11 model: meta-llama/Meta-Llama-3-8B-Instruct
12 parameters:
13 weight: 1.0
14 - layer_range: [0, 32]
15 model: failspy/Llama-3-8B-Instruct-MopeyMule
16 parameters:
17 weight: -1.0
18