Instructions to use yamathcy/spearmint-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yamathcy/spearmint-slerp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="yamathcy/spearmint-slerp")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("yamathcy/spearmint-slerp") model = AutoModel.from_pretrained("yamathcy/spearmint-slerp") - Notebooks
- Google Colab
- Kaggle
merged_model
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: facebook/hubert-base-ls960
layer_range: [0, 12]
- model: m-a-p/MERT-v0-public
layer_range: [0, 12]
trast_remote_code: true
merge_method: slerp
base_model:
model: facebook/hubert-base-ls960
override_architecture: HubertModel
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
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