# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Theros/SpicedTahsin")
model = AutoModelForCausalLM.from_pretrained("Theros/SpicedTahsin")Quick Links
merge
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: cgato/L3-TheSpice-8b-v0.8.3
layer_range: [0, 32]
- model: Nitral-AI/Hathor_Tahsin-L3-8B-v0.85
layer_range: [0, 32]
merge_method: slerp
base_model: cgato/L3-TheSpice-8b-v0.8.3
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: 0.20
dtype: bfloat16
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Theros/SpicedTahsin")