Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 14
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("mayacinka/Calme-Rity-stock")
model = AutoModelForCausalLM.from_pretrained("mayacinka/Calme-Rity-stock")This is a merge of pre-trained language models created using mergekit.
This model was merged using the Model Stock merge method using MaziyarPanahi/Calme-7B-Instruct-v0.9 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: MaziyarPanahi/Calme-7B-Instruct-v0.9
- model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
- model: liminerity/M7-7b
merge_method: model_stock
base_model: MaziyarPanahi/Calme-7B-Instruct-v0.9
dtype: bfloat16
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mayacinka/Calme-Rity-stock")