Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Paper • 2203.05482 • Published • 8
How to use mllm-dev/merge_yelp_droid_linear with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="mllm-dev/merge_yelp_droid_linear") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mllm-dev/merge_yelp_droid_linear")
model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/merge_yelp_droid_linear")This is a merge of pre-trained language models created using mergekit.
This model was merged using the linear merge method.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
dtype: float16
merge_method: linear
slices:
- sources:
- layer_range: [0, 12]
model:
model:
path: mllm-dev/merge_diff_data_YELP
parameters:
weight: 1.0
- layer_range: [0, 12]
model:
model:
path: mllm-dev/merge_diff_data_DROID
parameters:
weight: 1.0