# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Andrewstivan/AURA")
model = AutoModelForCausalLM.from_pretrained("Andrewstivan/AURA")Quick Links
merged
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: ResplendentAI/Aura_v3_7B
layer_range: [0, 32]
- model: IlyaGusev/saiga_mistral_7b_merged
layer_range: [0, 32]
merge_method: slerp
base_model: ResplendentAI/Aura_v3_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
tokenizer_source: union
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Andrewstivan/AURA")