Downloads · 30 days
69
1% of all-time downloads
CorticalStack/shadow-clown-7B-slerp
shadow-clown-7B-slerp is a text generation model from CorticalStack. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
<img src="shadowclown.png" alt="Shadow clown logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/
Downloads · 30 days
69
1% of all-time downloads
All-time downloads
10K
Public
Parameters
7.2B
14.5 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors14.5 GB · 100%
From the Hugging Face model README
shadow-clown-7B-slerp is a DARE merge of the following models using mergekit:
See the paper Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch for more on the method.
slices:
- sources:
- model: CorticalStack/pastiche-crown-clown-7b-dare-dpo
layer_range: [0, 32]
- model: MSL7/INEX12-7b
layer_range: [0, 32]
merge_method: slerp
base_model: CorticalStack/pastiche-crown-clown-7b-dare-dpo
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
slices:
- sources:
- model: liminerity/M7-7b
layer_range: [0, 32]
- model: CorticalStack/pastiche-crown-clown-7b-dare-dpo
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/M7-7b
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
slices:
- sources:
- model: ammarali32/multi_verse_model
layer_range: [0, 32]
- model: liminerity/merge
layer_range: [0, 32]
merge_method: slerp
base_model: ammarali32/multi_verse_model
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
slices:
- sources:
- model: Gille/StrangeMerges_32-7B-slerp
layer_range: [0, 32]
- model: yam-peleg/Experiment26-7B
layer_range: [0, 32]
merge_method: slerp
base_model: Gille/StrangeMerges_32-7B-slerp
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16