# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("appvoid/merging-x")
model = AutoModelForCausalLM.from_pretrained("appvoid/merging-x")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 passthrough 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: appvoid/palmer-003
layer_range: [0, 5]
- sources:
- model: appvoid/palmer-003
layer_range: [3, 10]
- sources:
- model: appvoid/palmer-003
layer_range: [6, 15]
- sources:
- model: appvoid/palmer-003
layer_range: [9, 20]
- sources:
- model: appvoid/palmer-003
layer_range: [12, 22]
merge_method: passthrough
dtype: float16
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Model tree for appvoid/merging-x
Base model
appvoid/palmer-003
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/merging-x")