Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Paper • 2311.03099 • Published • 33
How to use mllm-dev/gpt2_m_experiment_drug_data_dare_ties_test_new_run with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="mllm-dev/gpt2_m_experiment_drug_data_dare_ties_test_new_run") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("mllm-dev/gpt2_m_experiment_drug_data_dare_ties_test_new_run")
model = AutoModelForSequenceClassification.from_pretrained("mllm-dev/gpt2_m_experiment_drug_data_dare_ties_test_new_run")This is a merge of pre-trained language models created using mergekit.
This model was merged using the DARE TIES merge method using mllm-dev/gpt2_f_experiment_0_drug_data_new_run as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
base_model: mllm-dev/gpt2_f_experiment_0_drug_data_new_run
dtype: float16
merge_method: dare_ties
parameters:
normalize: 1.0
slices:
- sources:
- layer_range: [0, 12]
model: mllm-dev/gpt2_f_experiment_0_drug_data_new_run
- layer_range: [0, 12]
model: mllm-dev/gpt2_f_experiment_1_drug_data_new_run
parameters:
density: 0.9
weight: 0.2
- layer_range: [0, 12]
model: mllm-dev/gpt2_f_experiment_2_drug_data_new_run
parameters:
density: 0.9
weight: 0.2
- layer_range: [0, 12]
model: mllm-dev/gpt2_f_experiment_3_drug_data_new_run
parameters:
density: 0.9
weight: 0.2
- layer_range: [0, 12]
model: mllm-dev/gpt2_f_experiment_4_drug_data_new_run
parameters:
density: 0.9
weight: 0.2