Model Stock: All we need is just a few fine-tuned models
Paper β’ 2403.19522 β’ Published β’ 14
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TitleOS/ExperimentTwo")
model = AutoModelForCausalLM.from_pretrained("TitleOS/ExperimentTwo")This is a merge of pre-trained language models created using mergekit.
This model was merged using the Model Stock merge method using mistralai/Mistral-7B-v0.1 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: mistralai/Mistral-7B-v0.1
- model: uukuguy/speechless-code-mistral-7b-v1.0
- model: cognitivecomputations/samantha-1.2-mistral-7b
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TitleOS/ExperimentTwo")