Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 15
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("varox34/7B-Model_Stock")
model = AutoModelForCausalLM.from_pretrained("varox34/7B-Model_Stock")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 Model Stock merge method using SanjiWatsuki/Kunoichi-DPO-v2-7B as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: NeverSleep/Noromaid-7B-0.4-DPO
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
- model: Undi95/Toppy-M-7B
- model: Epiculous/Fett-uccine-7B
merge_method: model_stock
base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
dtype: bfloat16
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="varox34/7B-Model_Stock")